The research team, led by Yang You, Weifeng Zhang, and colleagues in the hospital’s Department of Nuclear Medicine, set out to compare two statistical models for classifying indeterminate pulmonary nodules. The first, a conventional model, relied on seven predictors drawn from clinical data, CT features, and early-phase PET measurements. The second, an extended model, added a single extra variable: the retention index of maximum standardized uptake value, or RI-SUVmax, which captures how tracer concentration in the nodule changes between the early scan and a delayed scan performed roughly 120 to 180 minutes after injection of the radiopharmaceutical. The biological rationale is appealing. Inflammatory lesions and malignant tumors can both light up on early PET images, but the kinetics of glucose uptake differ; some investigators have argued that malignant tissue retains or accumulates FDG over time, while inflammatory activity tends to wash out. If true, delayed imaging could unmask cancers that the standard single-time-point scan misses.

To test this idea properly, the researchers assembled a cohort of 612 patients, each with a single index pulmonary nodule measuring between 8 and 30 millimeters. From an initial pool of 817 screened patients, 205 were excluded, leaving a study population in which every nodule eventually received a definitive reference diagnosis. Malignancy was confirmed in all cases by histopathology, obtained through surgical resection or image-guided biopsy. Benign status was established either by the same pathological route or, for solid nodules, by at least 24 months of serial chest CT follow-up showing stability or resolution. In total, 369 of the 612 patients—60.3 percent—turned out to have malignant nodules, a proportion that underscores just how high the stakes are in this diagnostic gray zone.

What distinguishes this study from much of the prior literature on dual-time-point PET imaging is its validation strategy. Rather than splitting patients randomly, the team assigned them chronologically: 312 patients scanned between 2018 and 2022 formed the training cohort, while 300 patients scanned between 2023 and 2025 formed a temporal validation cohort. This design matters because models that are tested on randomly shuffled data can quietly benefit from temporal leakage—subtle shifts in scanners, protocols, or patient demographics that make the test set resemble the training set. Temporal validation, by contrast, asks the harder question: does a model built on yesterday’s patients still work on tomorrow’s? The researchers also employed least absolute shrinkage and selection operator regression, or LASSO, to select predictors from a starting pool of 26 candidate variables, a technique that shrinks weak coefficients toward zero and helps prevent overfitting. Internal validation was performed with 1,000 bootstrap resamples, and the final locked models were then frozen and applied, unmodified, to the later cohort.

The headline result is a study in statistical nuance. In temporal validation, the conventional model achieved an area under the receiver operating characteristic curve of 0.891, with a 95 percent confidence interval of 0.854 to 0.928. The extended model, enriched with the delayed-phase retention index, reached 0.903, with an interval of 0.868 to 0.938. The paired difference between the two was just 0.0120, with a confidence interval stretching from −0.0003 to 0.0243 and a P value of 0.056—just shy of conventional statistical significance. In plain terms, adding the delayed scan produced a small improvement in the model’s ability to separate malignant from benign nodules, but the researchers could not confidently rule out that the difference was a fluke of this particular dataset. Brier scores, which combine discrimination and calibration into a single measure of predictive accuracy, were 0.134 for the conventional model and 0.127 for the extended one, again favoring the delayed approach modestly.

But the deeper story lies in the trade-offs and the calibration problem. At decision thresholds derived from the training data, the extended model pushed sensitivity up from 76.7 percent to 84.4 percent—meaning it caught more cancers—while specificity slipped from 86.7 percent to 81.7 percent, meaning more benign nodules were falsely flagged as suspicious. In a clinical setting, that shift translates into more patients with harmless lesions being funneled toward invasive biopsies or additional imaging, with all the anxiety, cost, and procedural risk those entail. Exploratory decision-curve analysis, which estimates the net benefit of acting on a model’s predictions across a range of risk thresholds, showed only threshold-dependent differences between the two approaches, leaving the clinical value of the extra scan contingent on how aggressively a given institution chooses to pursue nodules.

Perhaps the most sobering finding was the miscalibration. Calibration slopes in the temporal validation cohort were 0.557 for the conventional model and 0.482 for the extended model—far from the ideal value of 1.0. A perfectly calibrated model would assign a 70 percent malignancy probability to a group of nodules in which 70 percent are truly malignant; these models systematically overstated or understated risk when transplanted across time. The authors attribute this to the inherent difficulty of transporting prediction models across changing clinical eras and note that the miscalibration persisted regardless of whether delayed imaging was included. Their conclusion is unambiguous: recalibration and multicenter external validation are required before either model can be deployed in routine practice. This candor is refreshing in a field where promising discrimination statistics are sometimes oversold as clinical readiness.

The study also probed the robustness of its findings through a battery of exploratory analyses documented in supplementary materials. Subgroup analyses examined whether the benefit of delayed imaging varied by nodule characteristics, including a conventional definition of PET/CT-indeterminate status based on early SUVmax values between 1.5 and 4.0 or intermediate visual uptake scores. The team tested restrictions to a narrower 75-to-105-minute interval between early and delayed acquisitions, substituted delayed SUVmax for the retention index, and even refit the models using flexible restricted cubic splines to allow nonlinear relationships. Across these variations, the advantage of delayed imaging remained small and inconsistent, reinforcing the central message that the retention index is not a transformative addition to nodule characterization.

Why does this matter beyond the walls of one hospital in Zhengzhou? Dual-time-point PET imaging has been proposed, debated, and intermittently adopted for more than two decades, and the extra scan carries real costs: additional scanner time, higher radiation exposure from the second CT pass, greater patient burden, and scheduling complexity. If the incremental diagnostic yield is a statistically uncertain 0.012 in AUC, health systems may reasonably question whether the delayed phase earns its keep. At the same time, the study’s rigorous architecture—chronological validation, bootstrap internal checks, LASSO regularization, and transparent reporting of calibration failures—sets a template for how diagnostic prediction models should be evaluated before entering the clinic. As machine learning tools proliferate in medical imaging, studies like this one serve as a reminder that a model’s true test is not how impressively it fits the data it was trained on, but how faithfully it performs on patients it has never seen, months or years down the line.

For now, patients with indeterminate pulmonary nodules and their physicians are left with a nuanced picture. Early-phase ¹⁸F-FDG PET/CT, combined with clinical and CT features, remains a strong discriminator, achieving an AUC near 0.89 even without delayed imaging. The delayed scan may tip the balance in selected cases, particularly where sensitivity is paramount and a modest loss of specificity is acceptable, but the evidence does not yet justify routine adoption. The authors’ call for recalibration and multi-institutional validation is not a bureaucratic formality; it is the necessary next step before these models can safely inform decisions about who undergoes biopsy and who can be reassured with surveillance. In the ongoing effort to shrink the diagnostic gray zone of the solitary pulmonary nodule, this study measures the ground gained—and honestly reports how much of the fog remains.

Subject of Research: Diagnostic prediction modeling with early and delayed-phase ¹⁸F-FDG PET/CT for classifying indeterminate pulmonary nodules

Article Title: ¹⁸F-FDG PET/CT models with and without delayed-phase imaging for indeterminate pulmonary nodules: development and temporal validation

Article References: You, Y., Zhang, W., Zhen, Z., Xu, J., Hu, B., & Xuan, A. (2026). ¹⁸F-FDG PET/CT models with and without delayed-phase imaging for indeterminate pulmonary nodules: development and temporal validation. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02846-7

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02846-7

Keywords: pulmonary nodules, ¹⁸F-FDG PET/CT, dual-time-point imaging, retention index, SUVmax, temporal validation, LASSO regression, lung cancer diagnosis, medical imaging, model calibration, nuclear medicine, diagnostic accuracy

Esophageal squamous cell carcinoma is one of the most common and lethal malignancies worldwide, and it is particularly prevalent in parts of East Asia. Treatment decisions increasingly hinge on PD-L1, a protein that tumor cells use to dampen the immune system’s attack. When PD-L1 is abundant on the tumor surface, immune checkpoint inhibitors, drugs that block the PD-1/PD-L1 interaction, tend to work well. The standard way to measure PD-L1 is immunohistochemistry, a laboratory technique in which a biopsy sample is stained with antibodies and scored using a combined positive score, or CPS, that reflects how many tumor and immune cells display the protein. Patients whose tumors score at or above a defined threshold are typically considered candidates for immunotherapy.

The problem is that this assessment requires tissue, and obtaining it is not always straightforward. Esophageal tumors sit deep in the chest, biopsies sample only a fragment of a heterogeneous tumor, and PD-L1 expression can vary across space and change over time. A noninvasive surrogate that could be read off a scan performed before treatment would be enormously valuable, allowing clinicians to stratify patients earlier and perhaps to repeat the assessment as the disease evolves. That is the premise behind the new study, led by Jiazhong Ren and Zheng Fu of the Department of Medical Imaging and Yongbin Cui and Yong Yin of the Department of Radiation Oncology Physics and Technology at Shandong Cancer Hospital and Institute, affiliated with Shandong First Medical University and the Shandong Academy of Medical Sciences.

The physics behind the approach is elegant. 18F-FDG is a radioactive analog of glucose that is taken up by metabolically active cells, and tumors, with their voracious appetite for energy, typically accumulate far more of the tracer than surrounding healthy tissue. The PET scanner converts this accumulation into standardized uptake values, or SUVs, which quantify how much tracer concentrates in a given region. The researchers measured a family of these parameters: the maximum standardized uptake value (SUVmax), which captures the single hottest pixel in the tumor; the mean (SUVmean) and peak (SUVpeak) values, which describe average and locally averaged intensity; the standard deviation (SUVsd), which reflects how heterogeneous the uptake is; and two volumetric measures, the metabolic tumor volume (MTV), the physical volume of metabolically active tumor, and total lesion glycolysis (TLG), which combines volume with intensity.

The team enrolled 89 patients with esophageal squamous cell carcinoma in this retrospective study. All patients underwent 18F-FDG PET/CT and had PD-L1 expression evaluated by immunohistochemistry on primary tumor tissue before receiving any anti-tumor treatment. The investigators also recorded a comprehensive set of clinicopathological characteristics, including age, gender, smoking status, drinking status, tumor location, vertical tumor length, TNM stage, and pathological grade. For the analysis, patients were divided into two groups: those with PD-L1-negative tumors, defined as a combined positive score below 10, and those with PD-L1-positive tumors, defined as a CPS of 10 or higher, a cutoff commonly used in clinical decision-making.

The results showed a clear statistical pattern. Four of the six metabolic parameters, SUVmax, SUVmean, SUVpeak, and SUVsd, were significantly higher in patients whose tumors expressed PD-L1 than in those whose tumors did not, with all comparisons reaching statistical significance at P values below 0.05. In other words, tumors that glowed more brightly and more unevenly on the PET scan were more likely to be PD-L1 positive. The volumetric measures, MTV and TLG, did not show the same association, suggesting that it is the intensity and heterogeneity of glucose metabolism, rather than the sheer bulk of metabolically active tissue, that tracks with this immune marker.

To quantify how well these imaging features could actually predict PD-L1 status, the researchers turned to receiver operating characteristic curve analysis, a standard statistical technique that plots true positive rates against false positive rates across all possible cutoff values. The area under the curve, or AUC, summarizes discriminative ability on a scale from 0.5, equivalent to a coin flip, to 1.0, perfect prediction. SUVmax achieved an AUC of 0.636 with a P value of 0.029, and SUVmean achieved an AUC of 0.628 with a P value of 0.044, both statistically significant but modest in magnitude. The optimal cutoff values were 19.74 for SUVmax and 10.01 for SUVmean, thresholds that could be applied to future patients, though with considerable uncertainty.

The team then used univariate logistic regression to examine each parameter’s independent association with PD-L1 positivity. SUVmax was the strongest predictor with a P value of 0.006, followed by SUVmean at 0.011, SUVpeak at 0.022, and SUVsd at 0.024. However, a technical complication emerged: these four SUV-derived parameters are all computed from the same underlying uptake distribution and are therefore severely multicollinear, meaning they carry heavily overlapping information and cannot be meaningfully combined in a single statistical model. Because of this severe multicollinearity, the researchers selected only SUVmax, the strongest individual predictor, for the final single-predictor logistic model, which retained its significance at P = 0.006.

The authors are explicit about what these numbers do and do not mean. An AUC of 0.636 represents discriminative performance that is only modestly better than chance, far below what would be needed for a reliable diagnostic test. Consequently, the study concludes that SUVmax should serve only as an auxiliary imaging reference rather than a dependable noninvasive substitute for pathological PD-L1 detection in patients with esophageal squamous cell carcinoma. This honest framing matters, because the temptation to overread an imaging-biomarker association is real, and premature clinical adoption of an imprecise surrogate could misdirect treatment decisions with serious consequences.

Nevertheless, the research adds to a growing body of work exploring how imaging phenotypes reflect molecular tumor characteristics, a field sometimes described as radiomics or imaging genomics. The biological plausibility is compelling: PD-L1 expression is often driven by inflammatory and oncogenic signaling pathways that also reprogram tumor metabolism, so a link between glucose avidity and immune checkpoint ligand expression is not surprising. The study was supported by the National Natural Science Foundation of China under grants 12275162 and 12575365, was approved by the Ethics Committee of the Cancer Hospital Affiliated to Shandong First Medical University in accordance with the Declaration of Helsinki, and was published open access on 29 September 2026. Future studies with larger cohorts, prospective designs, and possibly machine learning approaches that combine multiple imaging features may refine the predictive power enough to bring scan-based biomarker assessment closer to the clinic.

Subject of Research: The association between 18F-FDG PET/CT metabolic imaging parameters and PD-L1 expression in esophageal squamous cell carcinoma

Article Title: The predictive value of 18F-FDG PET/CT metabolic parameters for PD-L1 expression in esophageal squamous cell carcinoma

Article References: Ren, J., Cui, Y., Fu, Z., & Yin, Y. (2026). The predictive value of 18F-FDG PET/CT metabolic parameters for PD-L1 expression in esophageal squamous cell carcinoma. BMC Cancer. https://doi.org/10.1186/s12885-026-17026-7

Image Credits: AI Generated

DOI: 10.1186/s12885-026-17026-7

Keywords: 18F-FDG PET/CT, PD-L1, esophageal squamous cell carcinoma, SUVmax, metabolic parameters, immunotherapy biomarker, immunohistochemistry, combined positive score, cancer imaging, radiomics, tumor metabolism, BMC Cancer

The imaging technique at the heart of the study is ¹⁸F-fluorodeoxyglucose PET/CT, a hybrid technology that pairs the functional sensitivity of positron emission tomography with the anatomical detail of computed tomography. Fluorine-18 labelled fluorodeoxyglucose is a radioactive glucose analogue that cells import through the same transporters they use for ordinary sugar. Because most cancer cells run their metabolism on glycolysis even when oxygen is plentiful, the Warburg effect, malignant tissue accumulates the tracer far more avidly than surrounding healthy structures. A detector ring records the decay of the fluorine-18 isotope, and the resulting signal is converted into standardised uptake values, or SUVs, which quantify how intensely a given lesion traps glucose relative to the injected dose and body mass. The maximum standardised uptake value, SUVmax, captures the single hottest pixel within a lesion and has long served as a convenient, if imperfect, proxy for metabolic aggressiveness.

The Zhejiang team, led by Yunqi Zhu and corresponding author Xinhui Su, assembled thirty treatment-naïve patients with histologically confirmed testicular germ cell tumours who had undergone ¹⁸F-FDG PET/CT before any antitumour therapy. Twelve carried a diagnosis of seminoma and eighteen had non-seminomatous germ cell tumours, a group that encompasses embryonal carcinoma, yolk sac tumour, choriocarcinoma and other histologies, often in mixtures. Because the patients had not yet received chemotherapy, surgery or radiotherapy, the scans offered an unfiltered view of each tumour’s native metabolism, free from the confounding effects of treatment-induced inflammation or necrosis that plague post-therapy imaging.

The headline result is hard to overstate. Non-seminomatous tumours showed a median primary-tumour SUVmax of 16.66, compared with just 8.41 for seminomas, a difference that reached statistical significance at P equals 0.007. When the researchers looked instead at the hottest lesion anywhere in each patient, the gap widened further: 21.91 versus 8.41, with P below 0.001. In other words, the non-seminomatous tumours were burning through glucose at roughly twice the rate of their seminoma counterparts, a metabolic distinction that mirrors the known biological gulf between these entities. Non-seminomatous germ cell tumours tend to be more proliferative, more genetically chaotic and more prone to early haematogenous spread, and the PET data appear to capture that ferocity in a single number.

Disease extent itself was also strikingly different between the groups. Distant metastatic disease, classified as M1 under the American Joint Committee on Cancer staging system, was present in 55.6 percent of the non-seminomatous patients but only 8.3 percent of those with seminoma. This imbalance matters for interpretation, because a patient-level measurement such as the highest-lesion SUVmax can be inflated simply by the presence of a metabolically hot metastasis rather than by any intrinsic property of the primary tumour. The investigators were alert to this pitfall and designed their analyses accordingly, performing receiver operating characteristic analysis to compare diagnostic performance, restricting analyses to patients without distant metastases, and building exploratory multivariable models to adjust for tumour size and metastatic status.

Those sensitivity analyses proved revealing. When the analysis was confined to patients with M0 disease, meaning no distant metastases, the primary-tumour SUVmax remained significantly higher in non-seminomatous tumours, at 16.96 versus 8.00 with P equal to 0.001. However, the apparent advantage of the patient-level highest-lesion SUVmax over the primary-tumour measurement shrank dramatically: the difference in area under the receiver operating characteristic curve fell from 0.148 in the full cohort to just 0.023 in the metastasis-free subgroup. This tells an important story about what each metric actually measures. The highest-lesion SUVmax looked like a superb discriminator across all comers, achieving an apparent area under the curve of 0.944, but part of that performance was borrowed from the fact that metastatic spread is itself more common in non-seminomatous disease. The primary-tumour measurement, by contrast, appears to reflect genuine histology-linked biology.

The exploratory log-linear model drove the point home. After adjusting for primary-tumour size and M status, non-seminomatous tumours retained a higher primary-tumour SUVmax, with an adjusted geometric mean ratio of 1.67 relative to seminoma and a 95 percent confidence interval of 1.07 to 2.60, yielding P equal to 0.024. A geometric mean ratio of 1.67 implies that, on average, the non-seminomatous primary tumours took up roughly two-thirds more glucose than seminomas of comparable size and stage. For a single-centre study of thirty patients, this is an encouraging signal, though the authors are careful to label the modelling as exploratory and to emphasise that validation in larger independent cohorts is essential before any clinical application.

The study also placed PET-derived parameters alongside the traditional serum tumour markers that clinicians already use. Among individual biomarkers, the patient-level highest-lesion SUVmax showed the highest apparent discrimination with an area under the curve of 0.944, followed by alpha-fetoprotein at 0.910 and primary-tumour SUVmax at 0.796. Alpha-fetoprotein is a well-established hallmark of non-seminomatous histology, since seminomas do not produce it, so its strong showing is unsurprising. What is intriguing is that a purely metabolic imaging metric could rival a specific biochemical marker, suggesting that glucose avidity and embryonal differentiation are intertwined at a fundamental level. The researchers also computed a panel of volumetric parameters, including metabolic tumour volume, total lesion glycolysis and total metabolic tumour volume, which quantify not just how bright a lesion is but how much metabolically active tissue it contains, though the SUVmax comparisons dominated the significant findings.

Why should two tumours arising from the same primordial germ cell lineage differ so markedly in their sugar consumption? Seminomas are histologically homogeneous, composed of sheets of uniform cells resembling primitive spermatogonia, whereas non-seminomatous tumours can contain wildly divergent components, from gland-like yolk sac structures to syncytiotrophoblasts that secrete human chorionic gonadotropin. Embryonal carcinoma elements, in particular, are highly proliferative and express patterns of gene activity reminiscent of pluripotent stem cells, states that are metabolically expensive. Rapid proliferation demands nucleotide synthesis, lipid production and continuous ATP generation, all of which push cells toward intensified glycolysis and upregulated glucose transporters. The doubled SUVmax observed in non-seminomatous tumours is thus plausibly a readout of this underlying developmental chaos, though the study was not designed to identify the molecular mechanisms responsible.

The practical implications, if confirmed, could be meaningful. A pretreatment PET/CT that hints at histology could help clinicians anticipate the tumour type in settings where biopsy material is limited or ambiguous, flag patients at higher risk of harboring metastatic disease, and potentially refine surveillance strategies after orchiectomy. The authors themselves are appropriately measured, describing their work as a retrospective exploratory study of thirty patients from a single centre and calling for validation in larger, independent and ideally prospective cohorts. Such studies would need to address selection bias, scanner variability across institutions and the heterogeneity of non-seminomatous histologies. Still, the core observation stands out for its simplicity: before a single drop of chemotherapy, the two great families of testicular germ cell tumour already glow differently on a glucose scan, and that difference survives statistical scrutiny even when tumour size and spread are taken into account. It is a vivid reminder that cancer metabolism is not merely a byproduct of malignancy but a window into what a tumour truly is.

Subject of Research: Metabolic characteristics of testicular germ cell tumours on pretreatment ¹⁸F-FDG PET/CT imaging

Article Title: Pretreatment ¹⁸F-FDG PET/CT metabolic characteristics of seminoma and non-seminomatous testicular germ cell tumours: a retrospective exploratory study

Article References: Zhu, Y., Lu, X., Lin, L., Wang, H., Yu, Q., & Su, X. (2026). Pretreatment ¹⁸F-FDG PET/CT metabolic characteristics of seminoma and non-seminomatous testicular germ cell tumours: a retrospective exploratory study. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02855-6

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02855-6

Keywords: testicular cancer, testicular germ cell tumour, seminoma, non-seminomatous germ cell tumour, ¹⁸F-FDG PET/CT, SUVmax, tumour metabolism, metabolic tumour volume, alpha-fetoprotein, cancer imaging, BMC Medical Imaging, diagnostic imaging

The study, published in BMC Medical Imaging, was led by Seckin Bilgic and colleagues at the Department of Nuclear Medicine of Bursa Uludag University, with additional contributions from Canakkale Onsekiz Mart University and Bursa Yuksek Ihtisas Training and Research Hospital. The team set out to answer a deceptively simple question: when adults undergo fluorine-18 fluorodeoxyglucose positron emission tomography and computed tomography, known as 18F-FDG PET/CT, for cancer evaluation, how often do their kidneys show abnormalities, and how often do those abnormalities actually make it into the written report? The answer, according to their data, is that kidney findings are remarkably common and remarkably under-reported.

The researchers examined 283 consecutive adult patients who underwent oncologic 18F-FDG PET/CT at their institution between March and April 2023. Consecutive enrollment matters here, because it means the cohort was not cherry-picked; these were simply the patients who came through the door during a defined two-month window. The study received ethical approval from the Bursa Uludag University Ethics Research Committee and was conducted in accordance with the 1964 Helsinki Declaration, with informed consent obtained from all subjects. Because the design was retrospective and observational, the investigators could go back and scrutinize the images with a level of systematic attention that busy clinical workflows do not always permit.

What did that scrutiny reveal? The numbers are eye-opening. At least one cortical or pelvicalyceal renal abnormality was identified in 192 of the 283 patients, which works out to 67.8 percent of the cohort, with a 95 percent confidence interval spanning 62.2 to 73.0 percent. In other words, roughly two out of every three cancer patients scanned carried some detectable kidney abnormality on their PET/CT images. The abnormalities fell into two broad morphological categories. Cortical abnormalities, which affect the outer functional tissue of the kidney, were present in 123 patients, or 43.5 percent, and were most commonly large cortical cysts, seen in 22.3 percent of the full cohort. Pelvicalyceal abnormalities, which involve the urine-collecting system deep within the kidney, appeared in 129 patients, or 45.6 percent.

The technical distinction between these two categories is worth unpacking, because it reflects the dual nature of the information contained in a PET/CT scan. The CT component provides high-resolution anatomical detail without contrast enhancement in this setting, allowing reviewers to assess the renal cortex for cysts, masses, and structural changes. The pelvicalyceal system, meanwhile, can be evaluated for dilatation, stones, and other collecting-system pathology. The researchers even developed a stepwise visual grading algorithm, described in their supplementary material, to classify pelvicalyceal dilatation from Grade 0 to Grade 3 based on renal pelvic fullness, calyceal dilatation, and associated thinning of the renal parenchyma. Notably, they used the fused PET/CT images only for anatomical correlation and deliberately ensured that the intense urinary excretion of FDG, which naturally accumulates in the collecting system, did not influence grade assignment. That methodological care matters, because urinary FDG activity is a well-known confounder that can obscure or mimic collecting-system findings.

But prevalence alone was not the heart of the study. The investigators also wanted to know whether the kidney findings they identified on systematic review had ever been documented before. For the 192 patients with at least one renal abnormality, they reviewed available pre-index institutional records, meaning prior imaging and reports already on file at the institution, and classified each patient’s findings as previously documented, mixed, or incidentally identified. The result was stark: only 20 patients, or 10.4 percent, had findings that were entirely previously documented. Another 31 patients, or 16.1 percent, had a mixed picture, with some findings known and some new. The remaining 141 patients, a full 73.4 percent, had kidney abnormalities that were entirely new discoveries, never before captured in the institutional record. Taken together, 172 of the 192 affected patients, or 89.6 percent, had at least one renal abnormality that had never been documented anywhere in their available records.

The most sobering comparison, however, came when the researchers turned to the original clinical PET/CT reports, the documents written by the interpreting physicians at the time of the scan and sent to the treating oncologists. Of the 192 patients whose scans showed renal abnormalities on standardized retrospective review, only 58, or 30.2 percent, had at least one of those abnormalities mentioned in the original report. The other 134 patients, 69.8 percent of those with findings, had no renal abnormality documented at all. The gap between what a systematic review could see and what the clinical report conveyed is the central finding of the paper, and it is difficult to read those numbers without wondering how many clinically meaningful findings slip through the cracks in routine practice.

Why does this happen? The authors point to the inherent structure of oncologic imaging interpretation. A PET/CT study ordered for cancer staging or surveillance generates hundreds of images, and the interpreting physician’s primary obligation is to the clinical question at hand: tumor location, metabolic activity, treatment response, and new metastatic disease. Kidney cysts and mild collecting-system dilatation are, in the vast majority of cases, benign and clinically silent, and flagging every one of them in every report would risk burying the critical oncologic information in a flood of incidental detail. This is the classic tension of incidental findings in modern imaging: as scanners have grown more sensitive and datasets more voluminous, the sheer volume of non-target abnormalities has outpaced any practical reporting strategy. Yet the study’s data suggest the pendulum may have swung too far, with nearly 90 percent of affected patients carrying at least one renal finding absent from all available documentation.

The clinical stakes are not trivial. Some incidental renal abnormalities, particularly complex cysts and solid-appearing cortical lesions, can represent early renal cell carcinoma, a malignancy that is highly curable when caught at a small size but far more dangerous when it grows undetected. Hydronephrosis and collecting-system dilatation can signal obstructing stones or tumors that merit follow-up. Chronic kidney disease, too, often announces itself first through subtle structural changes visible on cross-sectional imaging. Whether the abnormalities catalogued in this study would have changed outcomes for any individual patient is a question the retrospective design cannot answer, and the authors are appropriately measured on this point. They explicitly state that further studies should determine whether more systematic recognition and reporting of these findings influences diagnostic evaluation, clinical decision-making, and patient management.

Still, the study lands at a moment when the imaging community is actively grappling with how to handle the incidentalome, the ever-growing list of unexpected findings generated by modern scans. Structured reporting templates, standardized checklists for organ systems outside the region of interest, and artificial intelligence tools that pre-screen images for non-target abnormalities are all under active investigation as potential solutions. This study provides a concrete baseline for one organ system in one high-volume imaging context, and its message is clear: kidney abnormalities are present in more than two-thirds of oncologic PET/CT patients, yet nearly 70 percent of affected patients leave the encounter with none of those findings on paper. Closing that gap, without drowning the oncologic signal in incidental noise, may be one of the defining reporting challenges of the PET/CT era. The authors received no specific funding for the work and declared no competing interests, and the article is published open access under a Creative Commons license, making the full data and methods available to any research group hoping to replicate or extend the findings.

Subject of Research: Prevalence and under-reporting of incidental renal abnormalities detected on oncologic 18F-FDG PET/CT imaging

Article Title: Incidental renal abnormalities on 18F-FDG PET/CT in oncologic patients: prevalence and reporting practices

Article References: Bilgic, S., Koroglu, I., Akovalı, B., Sahin, H. G., & Alper, E. (2026). Incidental renal abnormalities on 18F-FDG PET/CT in oncologic patients: prevalence and reporting practices. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02859-2

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02859-2

Keywords: 18F-FDG PET/CT, incidental findings, renal abnormalities, oncologic imaging, kidney cysts, pelvicalyceal dilatation, reporting practices, nuclear medicine, radiology, renal cell carcinoma, patient safety, medical imaging

The numbers reported by the research team are striking. Among 49 patients with renal medullary carcinoma who underwent 18F-FDG PET/CT scans at MD Anderson between 2016 and 2025, the radiotracer detected lesions in 98 percent of cases, or 48 of the 49 patients. Even more consequential, in 65 percent of patients the tracer revealed additional cancer lesions that were not visualized on standard anatomical imaging such as computed tomography or magnetic resonance imaging. Those extra findings were not merely academic curiosities: in 21 percent of patients with active disease, the PET/CT results directly changed the clinical management plan, steering physicians away from treatments that would have been futile or harmful and toward strategies better matched to the true extent of disease.

Renal medullary carcinoma is a disease that demands precision from the very first scan. It predominantly affects adolescents and young adults, occurs more often in males, and is strongly associated with sickle cell trait and related hemoglobinopathies, conditions in which the oxygen-carrying environment of the renal medulla appears to create conditions favorable to tumor development. At the time of diagnosis, patients frequently already present with metastatic disease, and the median overall survival is approximately 14.5 months. Against that grim backdrop, every staging decision carries enormous weight. Surgery that might offer curative potential in localized disease becomes a source of suffering and delay when the cancer has in fact spread undetected, because high-morbidity operations postpone the systemic therapies that metastatic patients urgently need.

Simone Krebs, MD, MS, a nuclear medicine physician and scientist at MD Anderson and the study’s lead author, emphasized just how much rides on getting staging right in this disease. In renal medullary carcinoma, she noted, the value of accurate staging cannot be overstated, because upstaging a patient from localized to metastatic disease spares them from noncurative, high-morbidity surgeries that would only delay the initiation of essential systemic therapy. Identifying metastatic disease, she added, may also open opportunities for more effective therapies tailored to a patient’s actual disease burden. Her comments underscore a central tension in oncology: the difference between what imaging shows and what the cancer is actually doing can determine whether a patient receives the right treatment at the right time or loses precious months to the wrong one.

The biological basis for the study’s success lies in the way renal medullary carcinoma handles glucose. 18F-FDG is a glucose analog labeled with fluorine-18; it is taken up by cells through glucose transporters and then becomes metabolically trapped, allowing positron emission tomography to visualize tissues with high glycolytic activity. Many cancers are avid for FDG, but uptake varies considerably across kidney tumor subtypes, which has historically limited the tracer’s reputation in urologic oncology. Clear cell renal cell carcinoma, the most common kidney cancer, often shows variable FDG uptake, and some renal lesions are better characterized by other tracers or by dedicated CT protocols. Renal medullary carcinoma, however, demonstrates strong and consistent FDG uptake, distinguishing it from other kidney cancers and making it an unusually suitable target for FDG PET/CT imaging.

That metabolic avidity translated into practical diagnostic power even in patients who had already begun systemic treatment. The study found that 18F-FDG PET/CT remained highly effective after therapy had been initiated, an important point because treatment can alter tumor metabolism and degrade the performance of metabolic imaging in some cancers. In renal medullary carcinoma, the tracer continued to illuminate active disease, giving oncologists a reliable way to assess how patients were responding and whether residual lesions represented viable tumor. Quantitative PET/CT metrics were evaluated alongside visual interpretation, and the researchers systematically recorded every instance in which imaging findings altered treatment, providing a structured account of the scan’s real-world impact rather than anecdotal impressions.

The clinical consequences of the additional findings were often dramatic. In one illustrative case included in the published work, a patient demonstrated intense focal FDG uptake in nodal, hepatic, soft-tissue, and osseous metastases on maximum-intensity-projection imaging. Axial FDG PET and fused PET/CT images revealed focal intense uptake in the right iliac bone, a finding without a definite correlate on the dedicated CT image. That discrepancy, a metabolically active lesion invisible on anatomical imaging, led directly to changes in the patient’s clinical management. It is precisely this pattern, PET revealing disease that CT cannot confirm, that explains how the tracer upstaged so many patients and redirected so many treatment plans in the study cohort.

For patients, the implications of these findings are potentially life-altering. A young adult with renal medullary carcinoma who appears to have localized disease on a conventional CT scan might, in fact, harbor hepatic, nodal, or bone metastases that only PET/CT can reveal. Detecting those lesions before surgery could spare the patient a major operation that would never have been curative, while simultaneously accelerating access to systemic therapy, the treatment modality most likely to extend survival in metastatic disease. Conversely, for patients whose PET/CT confirms truly limited disease, the scan provides confidence that aggressive local treatment is justified. In both directions, the additional information changes decisions, and in a cancer with a median survival measured in months, better decisions translate directly into better use of the limited time available.

The study’s authors argue that the evidence now justifies a formal change in clinical practice. Their conclusion is that the findings may support the inclusion of 18F-FDG PET/CT in upcoming guidelines regarding renal medullary carcinoma, which would enable broader clinical use beyond specialized centers like MD Anderson. Guideline inclusion matters because renal medullary carcinoma is rare enough that many treating institutions will encounter only a handful of cases, and clinicians may default to conventional staging pathways unless professional society recommendations explicitly endorse metabolic imaging. Krebs and her colleagues contend that PET/CT provides clinically meaningful information beyond conventional imaging, helping doctors better assess the extent of disease and choose the most appropriate treatment strategy, and that on a larger scale the research supports wider adoption in routine patient care.

As molecular imaging continues to expand its role in oncology, the MD Anderson study offers a compelling case study in how an old, proven tool can find new purpose in an underserved disease. 18F-FDG has been a staple of nuclear medicine for decades, yet its value in kidney cancer has been considered limited and subtype-dependent. By rigorously documenting its performance specifically in renal medullary carcinoma, a population of predominantly young patients with few good options, the researchers have turned a routine radiotracer into a potential standard of care for one of oncology’s most aggressive diagnoses. With 98 percent detection, additional lesions found in nearly two-thirds of patients, and management changes in one in five, the case for FDG PET/CT in this disease is difficult to ignore, and the coming updates to clinical guidelines may soon make this powerful scan a routine part of the fight against renal medullary carcinoma.

Subject of Research: Use of 18F-FDG PET/CT imaging for staging and treatment planning in renal medullary carcinoma

Article Title: Proven radiotracer highly effective in detecting rare, aggressive kidney cancer

Article References: Proven radiotracer highly effective in detecting rare, aggressive kidney cancer. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: renal medullary carcinoma, 18F-FDG PET/CT, kidney cancer, molecular imaging, metastatic disease, staging, nuclear medicine, radiotracer, MD Anderson Cancer Center, treatment planning, non-clear cell renal cell carcinoma, Journal of Nuclear Medicine