The research, published in BMC Cancer by a team from Dokuz Eylül University in Izmir, took the form of a single-center retrospective comparison. The investigators combed their institutional records for patients with localized, resectable soft tissue sarcoma of the extremities who underwent curative-intent surgery between 2008 and 2024. They settled on 41 patients, a modest number by any standard, and followed them for a median of 103 months, more than eight and a half years. During that time, 12 patients died, every one of them from sarcoma itself rather than from other causes. The five-year overall survival rate in the cohort was 78.0 percent, falling to 61.1 percent at ten years, figures that sit comfortably within the range reported for extremity sarcomas in larger international series.

To judge the two nomograms, the researchers relied on two complementary statistical concepts that are central to modern prognostic modeling. The first is discrimination, measured here with Harrell’s C-index, which asks how well a model separates patients who die earlier from those who live longer. A C-index of 0.5 means the model performs no better than a coin flip, while a value of 1.0 represents perfect ordering of every patient’s outcome. The second is calibration, which asks a subtler question: when the model says a patient has, say, a 70 percent chance of surviving five years, do roughly 70 percent of similar patients actually make it that far? A tool can rank patients correctly yet still be badly miscalibrated, promising more or less survival than reality delivers, and both dimensions matter when the numbers end up in a clinic note or a consent discussion.

Because the two tools were designed to answer questions at different time points, the comparison required careful methodological choices. The MSKCC nomogram generates predictions at four, eight, and twelve years after surgery, while Sarculator produces five- and ten-year estimates. The team used each horizon-specific prediction as a prognostic marker and computed C-index values over the full follow-up period, attaching bias-corrected and accelerated bootstrap 95 percent confidence intervals to every estimate. They prespecified a single paired comparison at the closest available horizons, Sarculator’s five-year prediction against the MSKCC four-year prediction, and were explicit that this contrast does not test identical prediction targets. With only 12 deaths in the entire cohort, they treated all discrimination and calibration findings as exploratory and hypothesis-generating, emphasizing uncertainty over p-values.

Against that cautious backdrop, the headline finding was clear enough. The MSKCC nomogram produced a higher C-index point estimate, hovering around 0.81 across its prediction horizons, compared with roughly 0.71 for Sarculator. In the prespecified paired comparison, the difference in C-index was 0.096, with a 95 percent confidence interval stretching from 0.004 to 0.225. The statistical tests told slightly different stories: a Wald test suggested the difference was significant at p equals 0.042, while a paired bootstrap test gave a p-value of 0.072, which fails the conventional significance threshold. The authors were refreshingly candid about this discordance, concluding that the wide interval, the mismatched horizons, and the small number of events together preclude any claim that one tool is definitively superior to the other.

Calibration told a similar story of cautious asymmetry. When the researchers plotted predicted survival against observed survival, the MSKCC nomogram’s estimates tracked the actual experience of the cohort most closely at the four- and eight-year marks. Sarculator, by contrast, tended to overestimate survival, and the discrepancy was most pronounced at the ten-year horizon, where the tool’s optimism diverged furthest from what the Izmir patients actually experienced. Overestimating survival is not a trivial flaw. A model that paints too rosy a picture could, in principle, lead clinicians to under-treat a high-risk patient or to schedule follow-up less intensively than the underlying biology warrants, although the authors stress that their small cohort prevents any precise quantification of how large this miscalibration really is.

Why should a comparison of 41 patients matter to anyone beyond the walls of a single Turkish sarcoma clinic? The answer lies in a persistent gap in the validation literature. Both nomograms were developed and refined in large, predominantly Western cohorts, and head-to-head comparisons restricted exclusively to extremity-localized disease, and conducted in non-Western populations, have been scarce. Soft tissue sarcomas are a heterogeneous family of more than 70 histological subtypes, and the composition of cases, the referral patterns, and even the prevalence of undifferentiated pleomorphic sarcoma versus other subtypes can vary between regions. A tool that performs beautifully in New York or Milan may drift when applied in Izmir, and the only way to find out is to test it, even imperfectly, in new settings.

The study’s technical approach also offers a small masterclass in how prognostic tools should be evaluated. Rather than simply reporting a single C-index and moving on, the investigators used horizon-specific predictions as the markers being tested, acknowledged that comparing a five-year forecast with a four-year forecast is an apples-to-oranges exercise, and leaned on bootstrap resampling to quantify uncertainty honestly. The bias-corrected and accelerated bootstrap method they employed adjusts for skewness and bias in the resampled distribution, producing confidence intervals that are more reliable in small samples than naive approximations. Their decision to prespecify a single primary comparison also guards against the inflation of false positives that comes from testing many contrasts and cherry-picking the significant ones, a discipline that small retrospective studies often lack.

The limitations, which the authors enumerate with unusual transparency, are nonetheless substantial. Forty-one patients and twelve events provide very little statistical fuel; a rough rule of thumb in prognostic modeling suggests that reliable estimation requires far more events per variable examined. The retrospective single-center design means the cohort reflects one institution’s referral patterns, surgical practices, and pathology reporting over a 16-year span, during which imaging, staging, and adjuvant therapy norms all evolved. Calibration could only be assessed graphically rather than with formal statistical tests, and the different prediction horizons of the two tools mean the head-to-head comparison is inherently approximate. The authors themselves conclude that no inference of comparative superiority can be drawn, and they frame the work explicitly as hypothesis-generating, calling for confirmation in larger, externally validated cohorts.

Even so, the study lands at a meaningful moment. Prediction tools in oncology are proliferating rapidly, powered by ever-larger registries and increasingly sophisticated machine learning methods, yet the quiet work of external validation, especially in populations far from the development data, often lags behind. Nomograms like MSKCC’s and calculators like Sarculator are already embedded in clinical practice, quoted in tumor boards and counseling sessions around the world. This Turkish analysis does not dethrone either tool, but it does something arguably more valuable: it demonstrates a rigorous, honest template for testing them, and it raises a concrete, testable hypothesis that the older MSKCC model may discriminate better while the newer Sarculator may run optimistic at long horizons. For the thousands of patients who face an extremity sarcoma diagnosis each year, the eventual answer to that hypothesis, tested in large and diverse cohorts, could mean survival estimates they can genuinely trust.

Subject of Research: Comparison of the MSKCC and Sarculator prognostic nomograms for predicting survival in extremity soft tissue sarcoma

Article Title: Discrimination and calibration of the MSKCC and sarculator nomograms in extremity soft tissue sarcoma: a single-center retrospective comparison

Article References: İzgör, R. B., Özalp, F. R., Aktepe, O. H., Karaoğlu, A., & Atağ, E. (2026). Discrimination and calibration of the MSKCC and sarculator nomograms in extremity soft tissue sarcoma: a single-center retrospective comparison. BMC Cancer. https://doi.org/10.1186/s12885-026-17087-8

Image Credits: AI Generated

DOI: 10.1186/s12885-026-17087-8

Keywords: soft tissue sarcoma, extremity sarcoma, MSKCC nomogram, Sarculator, prognostic nomogram, overall survival, C-index, calibration, external validation, retrospective study, BMC Cancer, Dokuz Eylül University

The research, conducted at the Department of Bone and Soft Tissue Oncology of the Second Affiliated Hospital of Zhejiang University School of Medicine, followed 153 patients with primary limb liposarcoma treated between January 2015 and April 2023. All patients underwent limb-sparing surgical resection, and decisions about additional treatment were made by a multidisciplinary tumor board. The researchers then divided the cohort into two groups: those who received adjuvant radiotherapy and those who did not. Their central question was deceptively simple—did the radiation actually improve survival or reduce recurrence?

Answering that question in oncology is harder than it sounds, because patients are not randomly assigned to treatment. Radiation is typically offered to those with the highest risk of recurrence: deep tumors, high-grade histology, or close surgical margins. That means the radiotherapy group starts out sicker, and any naive comparison between the groups would be hopelessly biased. To address this, the team employed propensity score matching, a statistical technique that pairs patients from each group based on eleven baseline characteristics, including age, sex, tumor grade, tumor size, tumor location, tumor type, surgical margin status, involvement of vascular and nerve bundles, use of chemotherapy, and any history of recurrence following surgery at another institution.

The results were striking. Before matching, the five-year overall survival rate was 88.8 percent in the non-radiotherapy group compared with 71.1 percent in the radiotherapy group, a difference that narrowly missed statistical significance. After propensity score matching, the gap persisted—85.2 percent versus 71.1 percent—but was no longer statistically meaningful. Recurrence-free survival told a similar story. Before matching, the non-irradiated patients fared dramatically better, with a five-year recurrence-free survival of 75.6 percent against just 41.8 percent in the irradiated group. Yet after the covariates were balanced, that apparent advantage shrank to 64.8 percent versus 41.8 percent, a difference that no longer reached significance. In total, the study recorded 16 deaths and 24 recurrences among patients who did not receive radiation, and 9 deaths and 12 recurrences among those who did.

Perhaps the most provocative finding emerged from the subgroup analysis of myxoid liposarcoma, a distinct molecular subtype that accounts for roughly 30 percent of all liposarcomas. Myxoid tumors carry a characteristic genetic signature—a translocation between chromosomes 12 and 16 that produces an abnormal FUS-DDIT3 fusion protein—and this fusion is thought to confer increased radiosensitivity. In theory, myxoid liposarcoma should be the subtype most likely to benefit from radiation. Instead, after propensity score matching, the irradiated myxoid patients showed significantly worse recurrence-free survival: 48.4 percent versus 86.5 percent in the non-irradiated group. Overall survival, however, remained comparable between the two groups in this subgroup.

The authors are careful to interpret this counterintuitive result with caution. They note that residual confounders may persist even after matching, and that the retrospective design meant important variables influencing the decision to irradiate—such as precise tumor depth and detailed margin characteristics—were not captured in the dataset. Propensity score matching also shrinks the effective sample size and can redistribute risk in ways that exaggerate differences. In other words, the worse recurrence outcomes among irradiated myxoid patients may reflect the fact that these were inherently higher-risk tumors, not that radiation itself caused harm. Still, the finding undercuts the standard rationale for treating myxoid liposarcoma as a radiosensitive disease, and it will likely prompt closer scrutiny of how this subtype is managed in clinics worldwide.

Beyond the radiotherapy question, the multivariate Cox regression analysis identified older age as the one factor that independently predicted death after matching, with each additional year of age raising the hazard of mortality by about 6 percent. This aligns with a broader body of literature showing worse outcomes for older liposarcoma patients, possibly reflecting differences in tumor biology, comorbidities, and treatment tolerance. Before matching, the analysis also flagged adjuvant chemotherapy as a dramatic risk factor for mortality, with a hazard ratio of 9.18—but the researchers urge readers not to overinterpret this. Chemotherapy, a regimen of doxorubicin and ifosfamide delivered over four to six cycles, was reserved for patients with large, high-grade tumors, meaning the association almost certainly reflects confounding by indication rather than a toxic effect of the drugs themselves. A previous analysis of the SEER database reached a similar conclusion.

The technical details of the treatment protocols matter for understanding the study’s scope. Radiotherapy was delivered using modern techniques—either intensity-modulated radiotherapy or three-dimensional conformal radiotherapy—with total doses of 50 to 60 Gray administered in daily 2 Gray fractions over five to six weeks. These are standard doses and delivery schedules for soft tissue sarcoma, so the results cannot be dismissed as an artifact of outdated radiation practice. Follow-up was rigorous, with clinical and imaging assessments at 1, 3, 6, and 12 months after surgery and every six months thereafter, using ultrasound or magnetic resonance imaging to detect local recurrence and chest computed tomography to screen for distant metastasis, with histological confirmation whenever feasible.

What does this mean for patients? The study’s conclusion is not that radiation is useless for everyone. The authors explicitly state that radiotherapy may still be appropriate for certain high-risk individuals, and the retrospective, single-center design with a modest sample size—particularly in the subgroup analyses—limits the statistical power of the findings. Variations in institutional practice could also have influenced the results even after matching. But the study does suggest that routine adjuvant radiotherapy for limb liposarcoma deserves skepticism, and that individualized surgical strategy, aimed at achieving wide negative margins whenever anatomically possible, should take priority over reflexive irradiation. The only independent risk factor for recurrence identified in the analysis was a history of prior recurrence following surgery at an outside institution, which underscores how much the quality of the initial operation matters.

Liposarcoma is the most common soft tissue sarcoma of adulthood, representing roughly 15 to 20 percent of cases, and it spans a biological spectrum from the indolent, low-metastatic-potential well-differentiated form to the aggressive pleomorphic and dedifferentiated subtypes. Reported five-year overall survival ranges widely, from 57 to 93 percent, depending on histology and clinical characteristics. Because these tumors so often wrap around the neurovascular bundles of the thigh and upper arm, the surgical dilemma at the heart of this study is one that sarcoma surgeons confront every week. As larger, ideally multicenter and prospective studies are mounted to settle the question definitively, this propensity-matched analysis adds a compelling data point to a growing argument that in limb liposarcoma, the scalpel—not the radiation beam—remains the decisive weapon.

Subject of Research: Adjuvant radiotherapy outcomes in limb liposarcoma assessed by propensity score matching

Article Title: Does radiotherapy improve the prognosis of limb liposarcoma? A retrospective study based on propensity score matching

Article References: Wang, K., Zhang, J., Li, B., Mou, H., Yang, Z., Lin, N., Ye, Z., & Qu, H. (2026). Does radiotherapy improve the prognosis of limb liposarcoma? A retrospective study based on propensity score matching. Holistic Integrative Oncology, 5(1), Article 26. https://doi.org/10.1007/s44178-026-00242-3

Image Credits: AI Generated

DOI: 10.1007/s44178-026-00242-3

Keywords: liposarcoma, radiotherapy, soft tissue sarcoma, propensity score matching, myxoid liposarcoma, recurrence-free survival, overall survival, prognostic factors, surgical margins, adjuvant therapy, oncology, retrospective study

The technique at the heart of the study is called time-dependent diffusion MRI, abbreviated T_d-dMRI. Its underlying logic is elegant. In standard diffusion-weighted imaging, water molecules inside tissue diffuse randomly, and their movement is hindered by cell membranes and other obstacles. The apparent diffusion coefficient, or ADC, summarizes this restriction in a single number, but that number conflates many different microstructural influences. Time-dependent diffusion MRI changes the experimental geometry instead. Using pulsed gradient spin-echo sequences, the diffusion-weighting gradients act over relatively long times, allowing water to wander across substantial distances and probe large-scale tissue structure. Oscillating gradient spin-echo sequences, by contrast, reverse the gradient direction rapidly, effectively confining the measurement to very short diffusion times during which water molecules can only explore their immediate cellular neighborhood. By comparing diffusivity measured at long and short diffusion times, the method gains sensitivity to features such as cell size, cell density, and the volume fraction occupied by cells, which conventional ADC cannot disentangle.

To translate these raw measurements into biologically meaningful numbers, the researchers applied a model known as IMPULSED. This analytical framework treats tissue as a collection of impermeable cells embedded in an extracellular space and fits the diffusion signal acquired at multiple frequencies to estimate specific microstructural parameters. Four quantities emerge from the fit: the extracellular diffusivity, which reflects how freely water moves outside cells; the intracellular volume fraction, which captures the proportion of the tissue volume packed inside cell membranes; an estimate of cellularity; and an estimate of mean cell diameter. Each of these parameters has a direct pathological counterpart that a pathologist can measure under the microscope, which is precisely what makes the approach so attractive for radiologic-pathologic correlation.

The experimental system consisted of xenograft tumors grown in nude mice. The team implanted two human-derived cell lines: RD, which produces rhabdomyosarcoma tumors, and A673, which produces extraosseous Ewing sarcoma tumors. Thirty-three rhabdomyosarcoma xenografts and thirty Ewing sarcoma xenografts underwent the time-dependent diffusion protocol. Two independent readers derived the microstructural parameters from whole-tumor volumes, allowing the study to assess measurement reproducibility as well as diagnostic performance. The choice of xenograft models is an important caveat that the authors themselves emphasize: these are controlled, single-cell-line systems, which are ideal for establishing proof of concept but do not capture the heterogeneity of human tumors.

The results revealed striking and statistically robust differences between the two tumor types. Rhabdomyosarcoma xenografts showed significantly lower cellularity than Ewing sarcoma xenografts, with values of 2.88 versus 4.34 per micrometer, and a lower intracellular volume fraction of 0.44 versus 0.58. The rhabdomyosarcoma cells were also larger, with an estimated mean diameter of 18.59 micrometers compared with 16.56 micrometers for the Ewing sarcoma cells, and water diffused more freely in their extracellular space, at 0.82 versus 0.69 square micrometers per millisecond. Perhaps most intriguing was the behavior of the ADC across diffusion times. When the sequence shifted from the long-diffusion-time pulsed gradient to the rapidly oscillating 50-hertz gradient, the relative ADC change was 85.5 percent in rhabdomyosarcoma but 179.8 percent in Ewing sarcoma. All of these differences reached statistical significance at P less than 0.01. In physical terms, the Ewing sarcoma tumors, being more densely packed with smaller cells, restricted water far more severely at short diffusion times, producing a much larger swing in measured diffusivity.

Diagnostic performance was assessed with receiver operating characteristic analysis, which quantifies how well a parameter separates two classes. Time-dependent diffusion MRI-derived cellularity achieved an area under the curve of 0.92, while the ADC measured with the 50-hertz oscillating gradient achieved 0.91. A DeLong test comparing the two curves found no statistically significant difference, with P equal to 0.67, indicating that the model-derived cellularity estimate and the simpler oscillating-gradient ADC performed comparably in this setting. That equivalence is worth noting: the model-free ADC at a single oscillating frequency captured nearly all of the discriminative power, which could simplify clinical implementation if the finding holds in human tissue.

The methodological centerpiece of the study, and what distinguishes it from much of the existing diffusion MRI literature, is the slice-by-slice radiologic-pathologic correlation. Rather than comparing whole-tumor imaging averages with whole-tumor histology scores, the researchers registered hematoxylin and eosin stained sections to the corresponding MRI slices, aligning each microscopic field with its radiologic counterpart. This co-registration allowed the team to test whether the in vivo imaging parameters genuinely tracked microscopic reality at the same anatomical location. The answer was affirmative across the board: all of the time-dependent diffusion MRI parameters showed significant associations with the matched histological features. The strongest relationship linked in vivo MRI-derived cellularity with ex vivo pathological nuclear density, yielding a Spearman correlation coefficient of 0.784 with P less than 0.001. In other words, the scanner was not merely producing numbers that differed between tumor types; it was reproducing, noninvasively, what the pathologist counted under the lens.

Why does this matter beyond the xenograft laboratory? Soft tissue sarcomas encompass dozens of distinct entities with divergent biology, and rhabdomyosarcoma and extraosseous Ewing sarcoma are among the most clinically consequential in young patients. Rhabdomyosarcoma, arising from skeletal muscle lineage, and Ewing sarcoma, driven in most cases by characteristic gene fusions, require different chemotherapy regimens, different local therapy strategies, and different surveillance plans. Current multiparametric MRI, combining T1-weighted, T2-weighted, and conventional diffusion sequences, provides limited traction because the tumors share so many gross features. A validated microstructural imaging biomarker could, in principle, sharpen pre-biopsy differential diagnosis, guide the placement of biopsy needles toward the most diagnostically informative tumor regions, and potentially serve as a noninvasive marker of treatment response, since effective therapy should alter cellularity and cell size in measurable ways.

The authors are careful, appropriately, to frame the study as a proof of concept rather than a clinical result. The findings are model-specific, derived from two xenograft lines in mice, and do not yet establish that the technique can differentiate human rhabdomyosarcoma from human Ewing sarcoma in patients. Human tumors are heterogeneous mosaics of divergent clones, stromal cells, necrotic regions, and edema, and their microstructural signatures will be far messier than those of pure cell-line xenografts. The IMPULSED model also rests on assumptions, such as impermeable cell membranes, that may be violated in aggressive tumors with leaky vasculature and dying cells. Validation across additional biological models and, crucially, human cohorts is required before any clinical translation. The imaging hardware is another consideration: oscillating gradient sequences demand specialized gradient hardware and acquisition protocols, although the involvement of Siemens Healthineers researchers suggests that implementation on clinical scanners is a realistic goal.

Even with those caveats, the study represents a meaningful step toward a long-sought goal in oncologic imaging: turning the MRI scanner into a virtual microscope. The combination of time-dependent diffusion encoding, biophysical modeling, and rigorous slice-matched histologic validation offers a template for how microstructural imaging biomarkers should be developed and tested. If subsequent studies in human patients confirm that cellularity and related parameters can be measured reliably in vivo and correlate with pathology at the level of individual tissue slices, the implications could extend well beyond sarcomas, potentially informing the characterization of brain tumors, breast lesions, and prostate cancer, where microstructural imaging is already under intense investigation. For now, the Shanghai team has demonstrated that the water molecules diffusing through a living tumor carry a readable record of its cellular architecture, and that record can be checked, slice by slice, against the truth that only the pathologist has traditionally been able to see.

Subject of Research: Time-dependent diffusion MRI microstructural characterization of rhabdomyosarcoma and extraosseous Ewing sarcoma xenografts with radiologic-pathologic correlation

Article Title: Time-dependent diffusion MRI for microstructural characterisation of rhabdomyosarcoma and extraosseous Ewing sarcoma xenografts: a proof-of-concept study with slice-by-slice radiologic-pathologic correlation

Article References: Long, H., Liang, H., Hu, X., Yuan, Z., Liu, M., Feiweier, T., Tao, H., Li, X., & Chen, S. (2026). Time-dependent diffusion MRI for microstructural characterisation of rhabdomyosarcoma and extraosseous Ewing sarcoma xenografts: a proof-of-concept study with slice-by-slice radiologic-pathologic correlation. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02799-x

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02799-x

Keywords: diffusion MRI, rhabdomyosarcoma, Ewing sarcoma, xenograft, cellularity, IMPULSED model, OGSE, PGSE, ADC, radiologic-pathologic correlation, soft tissue sarcoma, cancer imaging

Soft tissue sarcomas are malignant, locally invasive tumors arising from mesenchymal cells, and they are among the most common cancers in dogs. Sarcomas account for roughly 10 to 15 percent of malignant tumors in dogs, and about 80 percent of those are soft tissue sarcomas rather than bone tumors. The primary treatment is surgical removal, and the success of local control hinges on whether histological assessment of the margins confirms complete excision. Positive margins leave cancerous cells behind, increasing the risk of local recurrence and the morbidity that comes with it. The stakes are therefore high for getting margin information quickly, ideally while the patient is still on the table.

Optical coherence tomography, or OCT, is the imaging technology at the heart of this effort. It uses near-infrared light to generate real-time, high-resolution images of tissue microstructure, in much the same way ultrasound uses sound waves but with micrometer-scale resolution. Traditional spectral-domain OCT builds images from the intensity of reflected light, revealing depth-resolved structural detail. The technology has been tested for margin assessment in human breast cancer, where it achieved sensitivities of 92 to 100 percent for detecting positive margins, and human clinical trials are underway. The Ohio State team, led by Yuanlong Wang, Laura E. Selmic, and Ping Zhang, has now extended this approach to companion animals with an important upgrade: polarization sensitivity.

Polarization-sensitive OCT, or PS-OCT, is a set of hardware and software extensions that track the polarization state of the light reflected from tissue. This adds contrast mechanisms that ordinary OCT cannot provide. The key property is birefringence, an optical signature that arises from the arrangement of subcellular collagen within tissue and reflects how organized that tissue is. When tissue is damaged, degenerated, or necrotic, its structure breaks down and its birefringence drops. Cancerous tissue, with its disrupted architecture, therefore looks measurably different from healthy fat or muscle under polarization contrast. In prior studies of human breast tissue, PS-OCT demonstrated both qualitative and quantitative differences between cancerous and normal tissue. The system used in the study, a Thorlabs Telesto PS-OCT with a 1300 nanometer central wavelength, 3.5 millimeter imaging depth, and 5.5 micrometer axial resolution in air, synchronously captures traditional OCT images and three polarization metrics: retardation, optic axis, and degree of polarization uniformity, known as DOPU.

The physics behind these metrics is elegant. Two cameras record the reflected light as complex numbers, from which total intensity is computed pixel-wise for the standard OCT image. Retardation measures the difference in optical path experienced by two orthogonal linearly polarized states, calculated as an angle that reflects the ratio of irradiances in each polarization channel. The optic axis describes the orientation of the tissue’s birefringent axis within the plane transverse to the beam, derived from the Stokes parameters that fully characterize the polarization of light. DOPU quantifies the uniformity of polarization within a pixel neighborhood, ranging from 0 to 1, and serves as a regularized measure of how orderly the tissue’s polarization response is. Together with the intensity image, these four channels provide a far richer description of tissue than intensity alone, and the study set out to prove that a neural network could exploit that richness.

The researchers enrolled 48 canine soft tissue sarcoma specimens under an Institutional Animal Care and Use Committee approved protocol, ultimately analyzing 40 after excluding eight tumors that turned out not to be sarcomas. Board-certified veterinary surgeons excised the tumors with margins chosen purely on clinical grounds, and each specimen was wrapped in saline-soaked gauze to prevent drying before imaging. The team scanned the entire surgical margin in B-mode, continuously sweeping the tissue, and captured paired OCT and PS-OCT frames. An expert reviewed image quality, and a pathologist’s evaluation of corresponding histopathology sections provided the gold standard tissue labels. From 140 image pairs, the team cropped 1,553 patches using a sliding window with a 50-pixel stride, resizing each to 224 by 224 pixels to fit the ResNet50 backbone, a convolutional neural network architecture widely used in medical imaging.

The heart of the technical contribution lies in how the four image channels are combined. The team tested two fusion strategies. Early fusion simply concatenates all four images along the channel dimension and feeds them into a single ResNet50 backbone. Joint fusion instead runs four separate backbones, one per metric, and merges their learned feature vectors through a learnable weighted sum before a linear classifier makes the final cancer-versus-normal call. The joint fusion model won decisively, though at the cost of four times the parameters, longer training, and greater vulnerability to overfitting, a trade-off the authors describe explicitly between performance and computational complexity. Training used a 70-15-15 split of train, validation, and test data, with dogs kept whole within a single split to prevent patient overlap, five-fold cross-validation for hyperparameter tuning, random horizontal flips for augmentation, and early stopping against the validation set. Performance was assessed with AUROC, area under the precision-recall curve, F1 score, precision, recall, and accuracy, with uncertainty estimated by bootstrapping the test set 1,000 times.

The results were unambiguous. Both fusion strategies substantially outperformed a baseline model trained solely on traditional OCT images, which the authors attribute to the complementary polarization information. Adding PS-OCT metrics improved AUROC by up to 0.15 for cancerous image classification, and the more polarization metrics included, the greater the gain. The final joint fusion model reached 0.989 AUROC and 91 percent accuracy in detecting positive margins. Notably, the OCT-only baseline achieved the highest recall but at a punishing cost to precision, illustrating why intensity alone is insufficient for reliable intraoperative decisions. Ablation experiments with partial inputs, pairing OCT with just one PS-OCT metric at a time, confirmed that each polarization channel contributes, and that the full multimodal model is the strongest performer on the threshold-agnostic metrics that best reflect underlying discriminative power.

Perhaps the most clinically compelling feature is the diagnostic curve. Rather than simply reporting whether an image contains cancer, the model slides a fine window with a 5-pixel stride across the original image, computes the cancer probability for each patch, and aggregates overlapping predictions into a one-dimensional curve showing the probability of cancer at every horizontal position. In case studies, the curve stayed flat for pure cancerous and pure normal images, and for a mixed image containing tumor in roughly one third of the frame, it rose sharply over the cancerous region and tapered gradually across the margin into fat. This effectively performs a one-dimensional segmentation of the tumor, giving surgeons a map of where cancerous tissue lies rather than a bare yes-or-no verdict, and the window size and aggregation method remain adjustable to clinical preference.

The authors are candid about limitations. The training set is relatively small, and convolutional networks depend heavily on the comprehensiveness of their data, so broader validation is needed before generalizability can be trusted. Raw OCT images carry optical artifacts, motion blur, and noise whose effects on the model remain unquantified, and the fusion strategies explored were deliberately simple, leaving higher-order interactions between polarization metrics unexploited. The model is also far from clinical deployment; explainability, integration into surgical workflows, and careful validation of its effect on surgical outcomes all remain ahead. Still, the trajectory is clear. By teaching a neural network to read the polarization fingerprints that cancer leaves in collagen, this work moves real-time, AI-assisted margin assessment from a promising concept toward a practical tool, one that could spare dogs a second surgery and, if the companion-animal findings translate, may one day help human surgeons answer that oldest of operating room questions with far greater confidence.

Subject of Research: Deep learning-based intraoperative surgical margin assessment for canine soft tissue sarcoma using polarization-sensitive optical coherence tomography

Article Title: Deep learning-assisted surgical margin assessment for canine soft tissue sarcoma based on polarization-sensitive optical coherence tomography

Article References: Wang, Y., Selmic, L. E., & Zhang, P. (2025). Deep learning-assisted surgical margin assessment for canine soft tissue sarcoma based on polarization-sensitive optical coherence tomography. Veterinary Oncology, 2(1), Article 17. https://doi.org/10.1186/s44356-025-00032-5

Image Credits: AI Generated

DOI: 10.1186/s44356-025-00032-5

Keywords: deep learning, polarization-sensitive OCT, optical coherence tomography, soft tissue sarcoma, surgical margins, veterinary oncology, canine cancer, convolutional neural networks, birefringence, cancer imaging, intraoperative diagnosis, ResNet50

The technique at the heart of the study is called habitat imaging, and it represents a fundamental shift in how radiologists look at tumors. Instead of treating a tumor as a single lump to be measured with a ruler, habitat imaging divides it into biologically distinct subregions, or habitats, based on the functional characteristics of each voxel, the tiny three-dimensional pixels that make up the scan. The researchers used dynamic contrast-enhanced MRI, or DCE-MRI, which tracks the passage of a gadolinium contrast agent through tissue in real time. By fitting the resulting signal curves to a mathematical framework known as the Tofts model, they extracted pharmacokinetic parameters that describe the tumor’s microvasculature: Ktrans, which reflects the transfer of contrast from blood plasma into tissue; Ve, the volume of the extracellular space; and Kep, the rate at which contrast returns to the blood.

These parameters are not just numbers on a screen. They encode the biology of the tumor microenvironment. A region with high Ktrans is densely vascularized and leaky, often a sign of aggressive growth and abundant blood supply. A region with high Ve may indicate necrosis, edema, or expanded extracellular matrix, all of which can follow effective treatment. When radiation and targeted therapy attack a tumor, they strike these microenvironmental compartments unevenly. Some pockets of cancer cells die quickly as their blood supply collapses; others, perhaps better oxygenated or genetically hardier, survive. Conventional MRI and the widely used RECIST 1.1 criteria, which simply measure the longest diameter of the tumor, are blind to this internal heterogeneity. A tumor can be riddled with dead tissue yet still measure the same size on a standard scan.

To test whether habitat imaging could capture these early changes, the team prospectively enrolled 28 patients with soft tissue sarcoma who were undergoing protocol-specified neoadjuvant radiotherapy-based treatment, an approach combining radiation with targeted therapy. The study was conducted under two prospectively registered clinical trials, NCT05167994 and NCT05938374, and all participants gave written informed consent. Each patient underwent DCE-MRI twice: once at baseline before treatment began, and again six weeks into therapy. After the tumors were surgically removed, an experienced sarcoma pathologist examined the resected specimens and determined the percentage of viable residual tumor cells, the gold-standard measure of how well the treatment had worked.

The pathological results underscored just how difficult this disease is to treat. Only 5 of the 28 patients, or 17.9 percent, achieved a favorable response, defined as 5 percent or fewer viable tumor cells remaining in the surgical specimen. The remaining patients had substantial amounts of surviving cancer despite weeks of radiation and targeted therapy. This imbalance, with so few responders, is itself a reminder of why early prediction matters: if clinicians could identify non-responders early, they might intensify treatment, switch strategies, or spare patients the toxicities of a regimen that is not working.

The imaging analysis began with K-means clustering, an unsupervised machine learning algorithm that groups voxels according to their Ktrans and Ve values. This partitioned each tumor into distinct habitats representing different vascular and tissue microenvironments. The researchers then extracted radiomic features, quantitative descriptors of shape, intensity, and texture, from both the whole tumor and the individual habitat subregions. They also computed delta radiomics features, calculated as the difference between pre-treatment and post-treatment values, capturing how each feature changed over the course of therapy. After univariate analysis identified seven features associated with treatment response, the team built three logistic regression models using Akaike information criterion-based bidirectional selection: a Whole-tumor model, a Habitat model, and a Delta model.

The results were striking, though the researchers are careful to frame them as preliminary. All habitat subregions showed significant volumetric reduction after therapy, with all P values below 0.05, although the proportional shrinkage did not differ significantly among subregions. The standout performer was the Habitat model, which combined two features: pre-treatment Ktrans cluster-1 kurtosis, a measure of how peaked the distribution of vascular permeability values was before treatment, and post-treatment Kep cluster-3 uniformity, which describes how homogeneous the contrast washout rate became in one habitat after therapy. This model achieved an area under the receiver operating characteristic curve of 0.887, with 100 percent sensitivity and 73.9 percent specificity in the apparent analysis. In other words, it caught every true responder while misclassifying roughly a quarter of non-responders as responders.

Bootstrap internal validation tempered the enthusiasm somewhat. After correcting for optimism, the AUC dropped to 0.764, and the corrected discrimination was comparable across the three radiomics-based models. This kind of shrinkage is expected in small studies and is precisely why the authors describe their findings as exploratory. Still, the Habitat model showed favorable calibration, with a Hosmer-Lemeshow P value of 0.973, meaning its predicted probabilities aligned well with observed outcomes, and decision curve analysis suggested potential clinical utility. Perhaps most telling was the comparison with the current standard of care. RECIST 1.1, evaluated as a fixed categorical reference, achieved only 40 percent sensitivity, 82.6 percent specificity, and 75 percent accuracy against the pathological endpoint. The conventional method missed more than half of the patients whose tumors had actually responded, while the habitat-based model caught all of them.

The clinical implications of this work extend well beyond sarcoma. Neoadjuvant therapy is increasingly used across oncology, and the ability to predict pathological response non-invasively, mid-treatment, could transform decision-making for many cancers. In sarcoma specifically, patients who respond well might be candidates for less radical surgery, preserving limbs and function, while non-responders could be escalated to alternative regimens before precious months are lost. The pharmacokinetic parameters underlying the habitat approach are mechanistically meaningful, tied to perfusion, vessel permeability, and cell death, rather than being opaque statistical correlations. That biological grounding gives the method a plausibility that purely data-driven radiomics sometimes lacks, and it offers a window into the tumor microenvironment that no size measurement can provide.

Cautions remain, and the authors are candid about them. Twenty-eight patients is a small sample, the responder group contained only five individuals, and the optimism-corrected performance figures show how much apparent accuracy can evaporate under validation. Treatment protocols were not fully standardized across the cohort, and the models have not yet been tested on external data. The researchers themselves call for validation in larger prospective multicenter cohorts with more balanced response groups and standardized treatment regimens. Yet as a proof of concept, the study is compelling: it demonstrates that the internal architecture of a tumor, read through the lens of DCE-MRI habitat imaging, carries early signals of treatment success that conventional imaging simply cannot see. If larger trials confirm these findings, the six-week MRI scan could become a routine checkpoint in sarcoma care, giving patients and physicians the one thing they currently lack, which is time to change course.

Subject of Research: DCE-MRI-based habitat imaging for early prediction of treatment response in soft tissue sarcoma

Article Title: Longitudinal DCE-MRI-based habitat imaging for early prediction of treatment response to neoadjuvant radiotherapy and targeted therapy in soft tissue sarcoma: a preliminary study

Article References: Jiang, X., Wen, X., Jiang, J., Liu, F., Yang, Z., Miao, L., Li, J., Wang, S., Li, M., & Lu, N. (2026). Longitudinal DCE-MRI-based habitat imaging for early prediction of treatment response to neoadjuvant radiotherapy and targeted therapy in soft tissue sarcoma: a preliminary study. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02810-5

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02810-5

Keywords: soft tissue sarcoma, DCE-MRI, habitat imaging, radiomics, neoadjuvant radiotherapy, treatment response, Ktrans, Tofts model, RECIST 1.1, pathological response, machine learning, biomarker

The research team, led by Luke Borst of Antech Diagnostics and collaborators at Mars Petcare Science and Diagnostics, WALTHAM Petcare Science Institute, and Mars digital technology groups, developed and validated a deep learning system called OncoPetNet. The system is an ensemble of three fully convolutional neural networks built on encoder-decoder architectures with skip connections, a design descended from the U-Net family of image segmentation models. Rather than detecting mitotic figures directly as objects, the model frames the problem as segmentation: it predicts a pixel mask marking candidate dividing cells, and deterministic post-processing steps then convert those masks into coordinates. The encoders draw on EfficientNet-b5, EfficientNet-b3, and SE-ResNext backbones pre-trained on ImageNet, allowing the network to leverage general visual features learned from millions of natural images before fine-tuning on tumor histology.

Deploying such a model in a high-volume diagnostic laboratory required more than a clever network. The team built a production pipeline that runs asynchronously on every whole slide image uploaded to the lab. Each slide first passes through a biopsy slide classifier that decides whether mitotic counting is even appropriate. A non-model tissue detection algorithm then maps the foreground tissue and generates coordinates for the mitotic figure detector, which analyzes overlapping tiles across the entire tumor area. Morphological filters sharpen the predicted masks before a k-d tree based algorithm searches all possible placements of a 2.37 square millimeter square and selects the one containing the most mitotic figures. The final output, delivered to pathologists as annotations on the digital slide, includes both the region of interest and circles around each candidate mitosis.

Validation rested on two ground truth datasets. The first comprised 72 soft tissue sarcomas from the lab’s routine caseload, each confirmed by three pathologists, with consensus expert opinion establishing which AI-flagged candidates were true mitoses. On this dataset, OncoPetNet achieved an F1 score of 0.86, with recall, or sensitivity, reaching 0.97. The second benchmark was the MIDOG++ dataset, a publicly available multi-domain collection built specifically to test how well mitosis-detection models generalize across scanners, stains, laboratories, and tumor types. On 100 canine soft tissue sarcoma images from that set, the model scored an F1 of 0.70, with precision of 0.81 but recall dropping to 0.62. The authors attribute the difference partly to domain shift, since their model was trained on their own scanners and staining protocols, and partly to the low mitotic density of many MIDOG++ images, where a single missed mitosis can crater the score.

With the model validated, the team ran two evaluation studies. The first mimicked routine diagnostics: a single pathologist graded all 72 tumors three ways, manually, with AI assistance, and using AI alone. The results were striking. The AI-only condition averaged 19.9 mitotic figures per case, the AI-assisted condition 14, and the manual condition just 6.26. In other words, AI assistance allowed the pathologist to find about 2.24 times as many mitoses as unaided observation. The pathologist’s role shifted from exhaustive searching to verification: rejecting false positives, which the model produced more often than false negatives, and adding back any true mitoses the network missed. Human oversight measurably moderated the AI’s counts, which is precisely the behavior a safety-conscious deployment would want.

Those increased counts rippled through the grading scheme. Mitotic counts are binned into an ordinal MC score from 1 to 3, which is then summed with scores for tumor necrosis and degree of differentiation to produce the final grade. In the first study, AI-assisted scores migrated upward by one level in 29 percent of cases and by two levels in 6 percent compared with manual counts. Tumor grades followed: 18 percent of tumors moved from grade 1 to grade 2, and 7 percent from grade 2 to grade 3. No tumor migrated two full grades, and no tumor moved downward. The second study, in which nine pathologists organized into three cohorts each reviewed 24 cases both manually and with AI assistance, replicated these findings almost exactly. Across 216 paired observations, AI assistance produced an average of 11.6 mitoses versus 5.31 manually, again a roughly 2.2-fold increase, with grade migration of 15.7 percent from grade 1 to 2 and 5.6 percent from grade 2 to 3.

Efficiency gains were equally consistent. Manual counts took an average of 62.8 seconds per case, while AI-assisted counts took 33.7 seconds, nearly a 1.86-fold reduction. In a busy diagnostic laboratory processing thousands of submissions, halving the time spent on one of the most tedious steps in tumor grading represents a substantial operational improvement. Just as importantly, the AI’s pre-screening of every slide and field relieves pathologists of the cognitive burden of an unhurried, exhaustive search, a practice the authors note is technically required for accuracy but impractical under real-world workloads.

The most consequential findings concern agreement between pathologists. Using intraclass correlation coefficients, the team compared how consistently the nine pathologists rated the same tumors under manual versus AI-assisted conditions. For raw mitotic counts, AI assistance significantly improved agreement in two of the three pathologist trios and overall, lifting the overall coefficient from 0.94 to 0.98. Agreement on MC scores also improved significantly overall. Agreement on which slide to select, measured with Fleiss’ Kappa, rose in all three groups and reached statistical significance in two, reflecting the fact that AI effectively removes the guesswork about where to count. Agreement on final tumor grade showed a consistent upward trend, including a substantial jump in one group from 0.48 to 0.72, but these gains did not reach statistical significance. The authors attribute this dilution to the two remaining subjective parameters, percent necrosis and degree of differentiation, which AI does not yet assess and which continue to drive inter-pathologist variability.

The study’s authors are careful about what these results do and do not prove. The clinical significance of the higher, more sensitive counts remains unexplored: it is possible that current MC score thresholds, calibrated on imperfect manual counts, should be adjusted upward now that detection is better, or that AI-assisted grades will ultimately predict biological behavior more accurately. Testing that will require studies correlating AI-assisted grades with patient outcomes, ideally including clinical follow-up and necropsy data. Extending the approach to quantify necrosis and differentiation objectively would complete the standardization picture. There are also inherent limits to ground truth itself, since even expert consensus on what constitutes a mitotic figure carries some subjectivity, and immunohistochemical markers like phosphorylated histone H3 introduce their own interpretive complications.

What the study does establish is a working template for human-AI collaboration in diagnostic pathology. The machine does what machines do best, exhaustively scanning every slide and field without fatigue, while the human does what humans do best, exercising judgment to reject imposters such as apoptotic bodies, pyknotic nuclei, and inflammatory cells that mimic mitoses. The result is a count that is more sensitive, faster, and more reproducible than either party achieves alone. For veterinary oncology, where soft tissue sarcomas are among the most common canine tumors and grading decisions directly guide surgery and follow-up, that combination could mark the beginning of a genuine standardization of tumor grading, one algorithm-assisted count at a time.

Subject of Research: AI-assisted mitotic counting for standardizing histologic grading of canine soft tissue sarcomas

Article Title: Artificial intelligence-assisted mitotic counts improve efficiency, accuracy, and inter-pathologist agreement: a method toward canine soft tissue sarcoma grade standardization

Article References: Borst, L., Bacmeister, C., Yau, W., Aceino, A., Ranck, R., Labelle, P., Ehrensing, G., Oliveira, F., Myers, C., Willcox, J. L., Ellerby, Z., Haydock, R., Taylor, K., Parkinson, M., Fitzke, M., & Gerdin, J. (2025). Artificial intelligence-assisted mitotic counts improve efficiency, accuracy, and inter-pathologist agreement: a method toward canine soft tissue sarcoma grade standardization. Veterinary Oncology, 2(1), Article 19. https://doi.org/10.1186/s44356-025-00034-3

Image Credits: AI Generated

DOI: 10.1186/s44356-025-00034-3

Keywords: artificial intelligence, deep learning, veterinary oncology, soft tissue sarcoma, mitotic count, tumor grading, digital pathology, inter-observer agreement, canine cancer, convolutional neural networks, histopathology, MIDOG++

Liposarcoma is the most common soft tissue sarcoma in adults, accounting for roughly 20 percent of all sarcomas. The World Health Organization recognizes four principal subtypes: well-differentiated, dedifferentiated, myxoid, and pleomorphic. The dedifferentiated form, abbreviated DDLPS, arises when a slow-growing, well-differentiated liposarcoma undergoes a malignant transformation into a non-fatty, high-grade sarcoma. It represents about 15 to 20 percent of all liposarcomas and arises de novo in roughly 90 percent of cases. Compared with its well-differentiated cousin, DDLPS carries substantially higher morbidity and mortality, and while more than 80 percent of cases occur in the retroperitoneum, the space behind the abdominal organs, extremity tumors such as those in the thigh do occur and pose particularly thorny diagnostic problems.

At the molecular level, DDLPS carries a signature that has become a cornerstone of modern sarcoma diagnostics: co-amplification of two genes, MDM2 and CDK4, on chromosome 12q13-15. MDM2 encodes a protein that suppresses p53, the cell’s most famous tumor-suppressor and a key trigger of apoptosis, or programmed cell death. CDK4, meanwhile, drives unrestrained progression through the G1/S checkpoint of the cell cycle, effectively removing a critical brake on cell division. Together, these amplifications allow tumor cells to evade both death signals and growth arrest. In clinical practice, MDM2 immunohistochemistry shows a reported sensitivity of 100 percent for well-differentiated and dedifferentiated liposarcoma, with CDK4 approaching 83 percent, and fluorescence in situ hybridization for MDM2 amplification serves as the most specific confirmatory test. In this case, resource constraints meant neither molecular test was performed, and the diagnosis rested on classical histomorphological criteria alone.

The patient’s medical history complicated matters from the start. She had a ten-year history of type 2 diabetes mellitus, hypertension, hypothyroidism, and chronic kidney disease. Six months before admission, a mildly painful swelling on the postero-medial aspect of her left thigh was evaluated with Doppler ultrasonography, which showed a 9.5 by 6.1 centimeter lesion with predominantly arterial flow. The radiologist interpreted this as a hemangioma, and she was managed conservatively without a tissue biopsy. In retrospect, those arterial signals reflected tumor-associated neo-angiogenesis, the chaotic sprouting of new blood vessels that is a hallmark of high-grade sarcomas, rather than a benign vascular malformation. The lesion continued to enlarge, and systemic symptoms followed: intermittent fever, loss of appetite, weakness, and an involuntary weight loss of approximately six kilograms in a single month.

On admission, examination revealed a deep-seated, tense, tender, warm swelling with intact overlying skin, along with pitting edema from the ankle to the knee. Laboratory results painted a picture that pointed firmly toward infection: neutrophilic leukocytosis, markedly elevated erythrocyte sedimentation rate and C-reactive protein, moderate anemia refractory to erythropoiesis-stimulating agents, and deranged renal parameters. Bone marrow examination showed a hypercellular marrow with myeloid hyperplasia, attributed to chronic systemic inflammation. This biochemical constellation, combined with fever, would steer most clinicians toward an infectious diagnosis, and it did exactly that.

Contrast-enhanced CT revealed a mixed-density lesion of roughly 19 by 12 centimeters within the medial compartment muscles, featuring a large central necrotic zone and an irregularly enhancing peripheral wall. The radiological impression was an organized abscess. MRI added detail: an encapsulated mass measuring 18.4 by 11.2 by 10.6 centimeters, hypointense on T1-weighted sequences, hyperintense on T2 and STIR sequences, with peripheral enhancement, central necrosis, and prominent intra-tumoral flow voids. Those flow voids, dark signal voids caused by rapidly flowing blood, were a critical red flag, reflecting pathological tumor angiogenesis rather than a true vascular malformation. The tumor had invaded the adductor magnus muscle and extended through the upper two-thirds of the thigh. Notably, the central necrosis seen on imaging corresponded directly to the enormous volume of necrotic material later aspirated, underscoring the direct pathological link between tumor death and its clinical mimicry of a pyogenic collection.

Acting on the abscess impression, clinicians performed ultrasound-guided aspiration, yielding 600 milliliters of thick, purulent-appearing fluid. But the microbiology told a different story. Gram stain showed no organisms, culture and sensitivity returned sterile, and cytology revealed a mixed cellular pattern of 98,000 red blood cells and 25,000 white blood cells, atypical for a bacterial infection. A second aspiration two days later drained an additional 150 milliliters that had re-accumulated, itself an unusual behavior for a successfully treated abscess. The material was reclassified as necrotic tumor debris mimicking pus. The biology is well understood: in high-grade sarcomas, rapid tumor growth outpaces the blood supply, generating extensive intra-tumoral hypoxia, coagulative necrosis, and liquefaction that produce macroscopically purulent-looking but microbiologically sterile material. Concurrent core biopsies taken from the lesion wall during the procedure provided the definitive answer.

Histopathological examination showed a spindle cell neoplasm with marked nuclear pleomorphism, atypical mitotic figures, lipoblastic differentiation, a residual well-differentiated lipomatous component, and extensive tumor necrosis, classified as dedifferentiated liposarcoma, FNCLCC Grade 2. Staging PET-CT demonstrated a metabolically active mass of about 150 by 119 by 117 millimeters with peripheral FDG uptake, central necrosis, and diffuse reactive marrow uptake, but no distant metastasis. A multidisciplinary oncology team planned a wide local excision, the standard of care for DDLPS, which requires histologically negative surgical margins and often adjuvant radiotherapy. Chemotherapy plays a limited role, with anthracycline-based regimens achieving objective responses in only about 12 percent of advanced cases. For this patient, however, no intervention would arrive in time: the night after referral, she was found unresponsive in her hospital bed, and advanced cardiac life support failed. No autopsy was performed, so the formal cause of death could not be established.

The authors of the report carefully outline plausible mechanisms rather than assigning a definitive one. The most immediately life-threatening possibility is cancer-associated venous thromboembolism with fatal pulmonary embolism. In cancer patients, pulmonary embolism is the second leading cause of death after cancer progression itself, with a four- to seven-fold elevated risk of venous thromboembolism compared with the general population, and in-hospital mortality from cancer-associated pulmonary embolism ranges from 10 to 30 percent. The patient’s unilateral leg edema may have reflected venous compression by the tumor and underlying deep vein thrombosis, compounded by prolonged hospitalization, immobility, active malignancy, chronic kidney disease, diabetes, and systemic inflammation. A second mechanism is cancer cachexia-related cardiac dysfunction: tumor-derived cytokines such as TNF-alpha and IL-6 drive cardiac muscle atrophy, myocardial fibrosis, and ventricular dysfunction, potentially precipitating fatal arrhythmia or acute heart failure. A third possibility is acute hemodynamic compromise from intra-tumoral hemorrhage, sudden expansion of the necrotic cavity, or bacterial superinfection with septic shock, given the enormous tumor burden and its proximity to pelvic vasculature.

The lessons of this case reach beyond a single tragic outcome. A microbiologically sterile necrotic aspirate does not exclude malignancy, and the authors argue that concurrent core biopsy of the lesion wall should be mandatory in any deep soft tissue aspiration with atypical features. Red flags included the sheer size of the lesion, far larger than typical abscesses, the insidious six-month onset, thick and irregular enhancing walls on MRI, intra-tumoral flow voids, rapid re-accumulation after drainage, and the absence of clinical improvement with antibiotics. Clinicians are urged to maintain a low threshold for malignancy in any progressively enlarging deep soft tissue mass, regardless of radiological or inflammatory findings, and to manage thromboembolic risk, cachexia, and hemodynamic vulnerability proactively from the moment of diagnosis. Early biopsy, the authors conclude, is not merely diagnostically valuable; in cases like this one, it may be lifesaving.

Subject of Research: Dedifferentiated liposarcoma of the thigh mimicking an abscess, diagnosed by biopsy after sterile aspiration

Article Title: A Rare Case Report on Dedifferentiated Liposarcoma of the Thigh Mimicking an Abscess With Vascular Features: A Diagnostic Challenge With Fatal Outcome

Article References: Sazal, R. S., Shawon, M. H. H., Zubaer, M., Azad, M. A. K., Murshed, K. M., & Aftab, K. A. (2026). A Rare Case Report on Dedifferentiated Liposarcoma of the Thigh Mimicking an Abscess With Vascular Features: A Diagnostic Challenge With Fatal Outcome. Clinical Case Reports, 14(10), Article e73630. https://doi.org/10.1002/ccr3.73630

Image Credits: AI Generated

DOI: 10.1002/ccr3.73630

Keywords: dedifferentiated liposarcoma, soft tissue sarcoma, liposarcoma, misdiagnosis, abscess mimicry, MDM2, CDK4, MRI, biopsy, pulmonary embolism, cancer cachexia, neo-angiogenesis

Soft-tissue sarcomas, a heterogeneous family of malignancies arising from connective tissues such as muscle, fat, and blood vessels, are known to have a comparatively high propensity for spreading to the heart via the bloodstream. Yet cardiac involvement often remains clinically silent until the tumor reaches a critical size or occupies a strategically damaging location. In this patient, four years after an initial diagnosis treated with surgery, radiotherapy, and chemotherapy, the first warning signs were progressive shortness of breath and intermittent chest pain over several weeks. He had no prior cardiovascular history, which made the eventual findings all the more consequential. On examination he was hemodynamically stable, with a heart rate of 88 beats per minute, blood pressure of 128 over 76 millimeters of mercury, and oxygen saturation of 94 percent on room air. A soft systolic murmur was heard at the apex of the heart, and breath sounds were diminished over the right lower lung field, hinting at pathology within the chest rather than a primary cardiac disorder.

The diagnostic journey began with the simplest tools. An electrocardiogram showed sinus rhythm with nonspecific ST-T changes, findings that are easy to dismiss but consistent with myocardial stress. A chest X-ray revealed an enlarged cardiac silhouette and a mass in the right lung, prompting the team to proceed to transthoracic echocardiography. That ultrasound-based study proved pivotal. It demonstrated mild-to-moderate left ventricular systolic dysfunction, with the heart’s ejection fraction reduced to between 40 and 45 percent, meaning the main pumping chamber was operating well below its normal capacity of roughly 55 to 70 percent. The chambers and valves were otherwise structurally normal apart from mild mitral and tricuspid regurgitation, minor backflows of blood through the valves that often accompany altered cardiac loading conditions.

The echocardiogram’s decisive finding was a large, heterogeneous mass measuring approximately 2.7 by 3.2 centimeters within the left atrium, the chamber that receives oxygenated blood from the lungs before passing it to the left ventricle. Critically, the mass was intruding upon the right pulmonary veins, the vessels that deliver blood from the right lung into the atrium. This anatomical relationship explained much of the patient’s symptomatology: by obstructing pulmonary venous return, the tumor reduced the filling of the left ventricle, compounding the ventricle’s already weakened pumping function and contributing to breathlessness and chest discomfort. A slightly elevated pulmonary artery pressure was also noted, plausibly a secondary response to the underlying lung pathology and the left-sided hemodynamic burden.

To define the mass’s full extent and tissue characteristics, the clinicians turned to cardiac magnetic resonance imaging, the gold standard for characterizing cardiac masses. The CMR scans, acquired using steady-state free precession sequences, confirmed a far larger lesion than the echocardiogram alone suggested: a mass of 120 by 95 millimeters extending from the right lung into the left atrium through the pulmonary vein. The tissue was heterogeneous, showing mild perfusion and enhancement after contrast administration, a pattern typical of metastatic tumors and one that helps distinguish viable tumor from avascular structures such as thrombus. The imaging also revealed multiple metastatic nodules scattered through the left lung, painting a picture of widespread disease.

CMR added functional detail that proved equally important. The right ventricle, the chamber that pumps blood to the lungs, showed mildly reduced function with a right ventricular ejection fraction of 43 percent, although the chamber itself remained of normal size, indicating that some compensatory reserve persisted. The scans also detected a localized increase in T1 signal within the myocardium, the heart muscle itself, a finding the authors interpreted as possible chemotherapy-induced cardiomyopathy, a well-recognized late toxicity in patients treated with cardiotoxic agents. Trivial aortic insufficiency was also noted. Together these observations underscored that the patient’s heart was being assaulted on multiple fronts: by direct tumor invasion, by obstruction of venous return, and by the residual cardiac cost of his earlier cancer therapy.

A computed tomography scan of the chest completed the multimodal picture, confirming multiple pulmonary metastases with the dominant right-sided lesion extending directly into the left atrium. The concordance of findings across echocardiography, CMR, and CT was essential, because the differential diagnosis of a cardiac mass is broad and consequential. The clinicians systematically considered primary cardiac tumors such as atrial myxoma, the most common benign tumor of the left atrium, which can appear heterogeneous on imaging but typically does not invade surrounding structures, and rhabdomyosarcoma, a rare primary malignancy. They also weighed lung cancer metastasis, intracardiac thrombus, which lacks enhancement on CMR, infective endocarditis with its vegetations, cardiac sarcoidosis, fatty tumors such as lipoma and liposarcoma, hemangioma, and pericardial cysts. The patient’s sarcoma history, combined with the invasive growth pattern and enhancement characteristics on CMR, strongly favored metastatic disease, and the diagnosis was supported by clinical progression.

With the disease too extensive for surgical resection, the case was referred to a multidisciplinary tumor board comprising oncologists, cardiologists, and radiologists. The panel opted for palliative chemotherapy combined with targeted radiation therapy aimed at symptomatic relief. The outcome was encouraging: the patient experienced reduced dyspnea and chest pain, and follow-up imaging and clinical assessments documented improved cardiac function. This response aligns with recent evidence suggesting that continued antitumor therapy can improve survival even in patients with cardiac metastases, a population once considered to have uniformly dismal prospects. The authors emphasize that the positive trajectory followed directly from early detection and coordinated, multidisciplinary management rather than from any single intervention.

The broader lessons of the case extend beyond this single patient. Cardiac metastasis is rare but increasingly recognized as advances in imaging and cancer therapy extend survival long enough for late complications to emerge. Symptoms such as progressive breathlessness and chest pain in a patient with a known malignancy, particularly one of the sarcoma family, should prompt a low threshold for cardiac evaluation rather than attribution to more common causes. Transthoracic echocardiography remains the accessible first-line test, but its field of view can underestimate tumor extent, as it did here, where CMR revealed a mass several times larger than the initial measurement. The integration of multiple modalities, each contributing complementary anatomical, tissue-characterization, and functional information, was pivotal in establishing the diagnosis, mapping the disease, and steering therapy. As the authors conclude, early recognition and a coordinated multidisciplinary strategy remain essential to improving outcomes, even when metastatic disease is extensive, and the case stands as a testament to how modern cardiac imaging can turn a diagnostic dilemma into an actionable treatment plan.

Subject of Research: Cardiac metastasis from soft-tissue sarcoma diagnosed with multimodal imaging

Article Title: Metastatic Soft Tissue Sarcoma With Cardiac Involvement: A Case Study on the Role of Multimodal Imaging in Diagnosis and Management

Article References: Alizadehasl, A., Akhavan, S., Jebelli, S. F. H., najmabadi, A. F., Marzbali, N. A., Yousefimoghaddam, F., Haghazali, M., & Rafiei, S. (2026). Metastatic Soft Tissue Sarcoma With Cardiac Involvement: A Case Study on the Role of Multimodal Imaging in Diagnosis and Management. Clinical Case Reports, 14(9), Article e72112. https://doi.org/10.1002/ccr3.72112

Image Credits: AI Generated

DOI: 10.1002/ccr3.72112

Keywords: cardiac metastasis, soft-tissue sarcoma, echocardiography, cardiac MRI, computed tomography, left atrial mass, pulmonary veins, multidisciplinary care, palliative chemotherapy, case report, oncology, heart failure

Myxoid leiomyosarcoma, often abbreviated mLMS, is a rare morphological variant of leiomyosarcoma, a cancer arising from smooth muscle tissue. What distinguishes the myxoid variant is its abundant gelatinous stroma, a matrix that separates the neoplastic smooth muscle cells and gives the tumor a deceptively bland appearance under the microscope. While the uterus is the most common primary site, extra-uterine occurrences involving the retroperitoneum, the intra-abdominal cavity, and soft tissues are exceedingly rare. Population-based data confirm this extreme rarity and identify elderly females as the demographic most frequently affected, with the uterus representing the predominant primary site among myxosarcoma subtypes. Because so few cases exist, the clinicopathological behavior of extra-uterine disease remains poorly characterized, and management protocols are largely extrapolated from data on uterine leiomyosarcoma.

The prognosis of myxoid leiomyosarcoma is a subject of genuine scientific debate, and the disagreement is striking. Some clinical series report a highly aggressive course, with five-year overall survival as low as 11.1 percent, driven by a strong propensity for local recurrence and distant metastasis. In contrast, a Norwegian population-based study reported five-year survival approaching 73 percent, slightly more favorable than conventional leiomyosarcoma, and another investigation documented recurrence in only five of twelve cases over follow-up periods ranging from three to nine years. Researchers believe this discrepancy likely reflects heterogeneity in the diagnostic criteria applied across studies and, more fundamentally, underlying molecular diversity within the entity. Large-scale molecular profiling of uterine sarcomas has underscored that molecular classification refines both diagnosis and prognosis, suggesting that the apparent clinical variability of myxoid leiomyosarcoma may mask several biologically distinct subtypes awaiting precise definition.

Diagnostically, the tumor presents formidable challenges because its morphological and radiological features overlap with a spectrum of benign and malignant myxoid lesions, including myxoid leiomyoma, aggressive angiomyxoma, and low-grade endometrial stromal sarcoma. The myxoid matrix imparts low-attenuation characteristics on computed tomography and heterogeneous signal on other imaging modalities, frequently mimicking benign cystic structures or other soft tissue tumors, which renders imaging insufficient for definitive characterization. In the newly reported case, a contrast-enhanced abdominal CT scan revealed a large, poorly enhanced, heterogeneous soft tissue mass measuring 11.8 centimeters in the left peritoneal space, together with a separate complex cystic-solid lesion measuring 5.8 centimeters in the left lower abdomen. The presence of two spatially distinct lesions raised the possibility of a primary intra-abdominal neoplasm with a satellite deposit or multifocal disease, and a CT-guided core needle biopsy could only suggest an atypical myxoid neoplasm without definitive classification.

Definitive answers came only after surgical excision, performed for concurrent diagnosis and treatment. Gross examination of the resected specimen revealed a multilobulated mass with a gelatinous cut surface and a small adjacent satellite nodule. Under the microscope, the tumor showed a spindle cell proliferation with mild nuclear atypia and high mitotic activity, with 26 mitoses counted per ten high-power fields, all embedded within abundant myxoid stroma. Immunohistochemistry, the technique of using antibodies to detect specific proteins in tissue sections, played a pivotal role. The tumor cells displayed strong and diffuse positivity for desmin, a marker of muscle lineage, but were negative for SMA, h-Caldesmon, and SMMHC, a pattern consistent with the incomplete smooth muscle differentiation profile characteristic of myxoid leiomyosarcoma. Unlike conventional leiomyosarcoma, the myxoid variant frequently shows reduced or absent reactivity for these markers while retaining desmin in most cases, an inconsistency that can easily mislead observers unfamiliar with the entity.

The immunophenotype grew more complicated still. The tumor co-expressed the estrogen and progesterone receptors along with CD10 and Cyclin D1, a combination that prompted consideration of endometrial stromal sarcoma, another uterine mesenchymal malignancy with which myxoid leiomyosarcoma is frequently confused. However, the retention of desmin positivity, the morphological context, and subsequent molecular results argued against that diagnosis. Crucially, immunohistochemistry also revealed strong and diffuse nuclear overexpression of the PLAG1 protein, a finding that prompted a dedicated molecular workup. Overexpression of the MDM2 protein was identified as well, but fluorescence in situ hybridization, or FISH, confirmed a PLAG1 break-apart signal consistent with PLAG1 gene rearrangement while demonstrating an absence of MDM2 gene amplification. That combination effectively excluded well-differentiated and dedifferentiated liposarcoma, tumors that can morphologically overlap with myxoid lesions and that are defined by MDM2 amplification.

The PLAG1 rearrangement carries substantial diagnostic weight. Previous molecular studies have identified PLAG1 gene rearrangement in approximately 25 percent of uterine myxoid leiomyosarcoma cases, and it represents a highly specific biomarker that distinguishes this entity from ZC3H7B-BCOR high-grade endometrial stromal sarcoma and from myxoid inflammatory myofibroblastic tumor. Importantly, strong and diffuse nuclear PLAG1 immunoexpression, as observed in this patient, reliably identifies tumors harboring the rearrangement and serves as a practical triage tool before confirmatory FISH is performed. Integrating the histomorphological pattern, the immunophenotype, and the molecular findings, the clinical team rendered a definitive diagnosis of myxoid leiomyosarcoma harboring PLAG1 gene rearrangement, an example of how modern pathology increasingly depends on layered molecular evidence rather than morphology alone.

Of particular clinical significance is the patient’s prior laparoscopic myomectomy, performed two years earlier for a 13-centimeter submucosal uterine myoma. Surgical oncology literature emphasizes that myomectomy is strictly indicated for benign leiomyomas, and that inadvertent surgery on an occult leiomyosarcoma carries the risk of intraperitoneal tumor dissemination even in the absence of morcellation, the mechanical fragmentation of tissue during minimally invasive removal. Tumor cells have been detected in peritoneal fluid following myomectomy alone. The multilesional intraperitoneal distribution observed in this patient, with two spatially separate masses and an adjacent satellite nodule, raises strong suspicion that iatrogenic peritoneal seeding secondary to the prior myomectomy may have contributed to the tumor’s distribution. The authors of the report stress that any leiomyosarcoma diagnosis arising after a prior myomectomy warrants complete surgical re-evaluation, including hysterectomy, and close surveillance for intraperitoneal disease, and that the case reinforces the critical need for accurate preoperative differentiation between benign leiomyoma and leiomyosarcoma, particularly in younger patients with large or atypical uterine masses.

Treatment and outcomes in this case offer a measure of reassurance alongside the cautionary elements. The patient recovered smoothly after her tumor excision, experiencing only transient abdominal distention, and was discharged on the tenth postoperative day. At six-month outpatient follow-up she remained completely asymptomatic, with no clinical or radiological evidence of recurrence. Complete surgical excision with negative margins remains the cornerstone of curative-intent management for localized myxoid leiomyosarcoma, since standardized treatment guidelines for the rare entity remain elusive. For advanced disease, systemic therapy is an important consideration given the high rates of metastatic failure following resection. Doxorubicin has long been the standard first-line agent for metastatic leiomyosarcoma, but the LMS-04 Phase III randomized controlled trial demonstrated that combining doxorubicin with trabectedin significantly improved progression-free survival compared with doxorubicin monotherapy in metastatic or unresectable disease, an advance that should inform treatment planning for patients with unresectable or metastatic myxoid leiomyosarcoma.

The broader lessons of this single case extend well beyond one patient’s chart. The combination of an incomplete smooth muscle immunophenotype, strong diffuse nuclear PLAG1 expression, and confirmatory FISH proved instrumental in establishing a definitive molecular diagnosis that morphology and imaging alone could never deliver. The authors argue that comprehensive molecular workup, including PLAG1 immunohistochemistry and FISH, should be considered in all myxoid intra-abdominal tumors, particularly in patients with a prior history of uterine surgery. At the same time, the prognostic significance of PLAG1 rearrangement itself remains unresolved and, according to the report, warrants dedicated prospective investigation. For clinicians, the case is a reminder that the gelatinous, deceptively bland tumors encountered in the abdomen may conceal a malignant smooth muscle origin, and for surgeons and patients contemplating fibroid removal, it underscores the enduring importance of rigorous preoperative risk stratification before any uterine operation is undertaken.

Subject of Research: Intra-abdominal myxoid leiomyosarcoma with PLAG1 gene rearrangement following prior laparoscopic myomectomy

Article Title: Intra‐Abdominal Myxoid Leiomyosarcoma With PLAG1 Gene Rearrangement Following Prior Laparoscopic Myomectomy: A Case Report and Literature Review

Article References: Hsieh, C.-E., Wu, P.-H., Su, C.-W., Ma, Y.-C., & Huang, H.-Y. (2026). Intra‐Abdominal Myxoid Leiomyosarcoma With PLAG1 Gene Rearrangement Following Prior Laparoscopic Myomectomy: A Case Report and Literature Review. Clinical Case Reports, 14(9), Article e73539. https://doi.org/10.1002/ccr3.73539

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DOI: 10.1002/ccr3.73539

Keywords: myxoid leiomyosarcoma, PLAG1 gene rearrangement, leiomyosarcoma, myomectomy, fibroid surgery, peritoneal seeding, molecular diagnostics, FISH, immunohistochemistry, endometrial stromal sarcoma, trabectedin, soft tissue sarcoma

The study, conducted at BG University Hospital Bergmannsheil in Bochum and published in the Journal of Cancer Research and Clinical Oncology, followed 59 consecutive patients with histologically confirmed synovial sarcoma of the extremities treated over a 24-year period between 2000 and 2023. Of these, 23 tumors arose in the upper extremity and 36 in the lower extremity. The researchers set out with a deceptively simple question: does the limb in which the tumor sits influence the surgical pathway and the postoperative course, or is location merely a bystander variable with no practical consequence? Their findings suggest the answer is far from trivial.

The co-primary endpoints of the analysis were the presence of a microscopically positive margin, designated R1, at the initial resection, and any postoperative complication within eight weeks of surgery, graded according to the widely used Clavien–Dindo classification. Margin status is a cornerstone of sarcoma surgery because residual microscopic disease at the cut edge is associated with local recurrence and, in many series, with diminished survival. Achieving an R0 resection—complete removal with microscopically clear margins—is the explicit goal of every limb-sparing sarcoma operation.

What the team found was a striking asymmetry between the two anatomical groups. Positive margins at the index operation were recorded in 14 of 23 upper-extremity patients, or 61 percent, compared with only 5 of 36 lower-extremity patients, or 14 percent. Expressed as an odds ratio, upper-extremity tumors carried a nearly tenfold higher likelihood of an R1 margin at the first surgery, at 9.64 with a 95 percent confidence interval of 2.73 to 34.1 and a P value below 0.001. Neoadjuvant therapy, given before surgery to shrink the tumor, was also far more common in the upper-extremity group, at 39 percent versus 8 percent, a difference that reached statistical significance with P equal to 0.007.

The authors interpret this pattern as a reflection of surgical anatomy rather than of biology. The arm and hand are crowded, functionally dense territories. Vital nerves, vessels and tendons course through narrow compartments, and resecting a sarcoma with a wide cuff of healthy tissue—the standard for sarcoma surgery—often collides with the imperative to preserve hand and arm function. In the forearm and wrist, where critical structures lie millimeters beneath the skin, achieving a clear margin may be anatomically impossible without sacrificing essential function. The leg, by contrast, offers more expansive soft-tissue envelopes, particularly in the thigh, where wide resections can be accommodated with less functional penalty. This anatomical headroom appears to translate into cleaner first-pass margins.

Yet the trade-off ran in the opposite direction when the researchers examined complications. Any postoperative complication within eight weeks occurred in only 13 percent of upper-extremity patients but in 53 percent of lower-extremity patients, yielding an odds ratio of 0.13 for upper-extremity surgery relative to lower-extremity surgery, with a 95 percent confidence interval of 0.03 to 0.53 and P equal to 0.002. Major complications, the more severe grades of the Clavien–Dindo scale, followed the same trend, affecting 9 percent of upper-extremity patients and 39 percent of lower-extremity patients, with P equal to 0.015. Lower-extremity sarcoma surgery, typically involving larger resections, more extensive reconstruction, and the hemodynamic and wound-healing stresses of the leg, clearly extracts a heavier early toll.

Reconstructive strategy also diverged between the two groups. Tumors of the leg, particularly after wide resections, frequently demanded flap coverage, skin grafts and other reconstructive procedures to close large soft-tissue defects, whereas upper-extremity cases more often required staged or secondary procedures, including planned re-excision to secure clear margins after an initial R1 result. Importantly, the study found that definitive R0 resection was ultimately achieved in all patients. Even when the first operation in an upper-extremity case left microscopic disease behind, re-excision successfully converted the margin status. The message is one of cautious reassurance: the anatomical disadvantage of the arm is real, but it is surgically recoverable through vigilant follow-up and willingness to operate again.

The oncological outcomes add another layer of nuance. Seven of the nine distant metastases observed in the cohort occurred in patients with lower-extremity tumors, and several of these events emerged beyond six years after treatment, underscoring synovial sarcoma’s reputation as a late-relapsing disease. Ten-year metastasis-free survival was 90 percent in the upper-extremity group versus 56 percent in the lower-extremity group. Because the study was single-center, retrospective and modest in size, the authors deliberately classified these time-to-event findings as exploratory; the log-rank comparison yielded a P value of 0.095, which does not meet conventional thresholds of statistical significance. Still, the direction of the difference aligns with the larger tumor volumes and deeper compartments often seen in leg sarcomas, and the long tail of metastatic risk serves as a sobering reminder that synovial sarcoma follow-up must extend well beyond the five-year mark commonly used for many other cancers.

Methodologically, the study draws on a rigorous institutional registry. Screening of 1,060 registry entries from January 2000 to December 2023 identified the 59 surgically treated extremity synovial sarcomas after exclusion of entries that failed histological or anatomical eligibility criteria. Statistical analysis employed Fisher exact tests and the Mann–Whitney U test for group comparisons, and Kaplan–Meier curves with log-rank tests for survival endpoints. The researchers were careful to frame their conclusions within the limits of the design: this was a retrospective single-center cohort, and the exploratory survival findings require multicenter validation before they can inform practice guidelines. The study was conducted in line with the Declaration of Helsinki, with ethics approval from Ruhr University Bochum and written informed consent from all participants.

The clinical implications are nonetheless compelling. If upper-extremity synovial sarcomas systematically arrive at the operating table with a higher risk of positive margins, then surgical planning for these tumors should anticipate the need for neoadjuvant therapy and planned re-excision from the outset, rather than treating a positive margin as a surprise. Conversely, the high complication burden of lower-extremity surgery argues for intensified perioperative care, aggressive wound surveillance and perhaps earlier mobilization protocols in these patients. Above all, the study reframes anatomical location as an actionable variable in sarcoma care rather than a descriptive footnote. For a disease in which a single millimeter of residual tissue can set the stage for recurrence years later, understanding how the body’s own geography constrains the surgeon’s knife may prove as important as any drug in the oncological arsenal.

Subject of Research: Upper- versus lower-extremity synovial sarcoma: surgical margins, postoperative morbidity and long-term outcomes in a 24-year cohort

Article Title: Upper- versus lower-extremity synovial sarcoma: distinct surgical pathways and postoperative morbidity in a 24-year cohort

Article References: Weskamp, P., Drysch, M., Fiedler, A., Schmidt, S. V., Reinkemeier, F., Steubing, Y., Lehnhardt, M., Dadras, M., Puscz, F., & Wallner, C. (2026). Upper- versus lower-extremity synovial sarcoma: distinct surgical pathways and postoperative morbidity in a 24-year cohort. Journal of Cancer Research and Clinical Oncology, 152(9), Article 181. https://doi.org/10.1007/s00432-026-06619-1

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DOI: 10.1007/s00432-026-06619-1

Keywords: synovial sarcoma, soft-tissue sarcoma, surgical margins, R1 resection, re-excision, postoperative complications, Clavien–Dindo, neoadjuvant therapy, metastasis-free survival, reconstruction, surgical oncology, extremity tumors