The research, led by Jiajia Shi and Yunjin Chen of the First Affiliated Hospital of Zhengzhou University together with colleagues across multiple Chinese institutions, enrolled 128 patients who had undergone contrast-enhanced dual-layer spectral detector CT and whose diagnoses were confirmed by pathology. Of these, 86 carried pancreatic neuroendocrine tumors while 42 had the more malignant carcinomas. The cohort was randomly split into a training set of 89 patients and a validation set of 39, in a seven-to-three ratio, allowing the investigators to build their models on one group and test them honestly on another.

The technological heart of the study is dual-layer spectral detector CT, a scanner architecture that separates the energies of X-ray photons at the detector itself rather than requiring rapid voltage switching. This capability allows the reconstruction of virtual monoenergetic images, which simulate how tissue would appear if imaged with a single X-ray energy. The team focused on images reconstructed at 40 kiloelectron volts during the venous phase of contrast enhancement. At this low energy, iodine contrast attenuation is dramatically amplified, exaggerating subtle differences in how the two tumor types take up contrast agent and exposing texture information that conventional polychromatic images wash out.

From these 40 keV images, the researchers extracted radiomics features, a family of quantitative descriptors that capture first-order statistics of pixel intensity, spatial relationships between neighboring voxels, and higher-order texture patterns invisible to the human eye. The underlying premise is that tumor biology leaves fingerprints in image texture: differences in cell density, fibrosis, necrosis, and vascularity between well-differentiated tumors and poorly differentiated carcinomas translate into measurable mathematical signatures. Combined with conventional CT morphological features and spectral parameters such as iodine concentration and effective atomic number, these features formed the raw material for a series of logistic regression models.

The investigators constructed four competing models in a deliberate escalation of complexity. The first relied solely on conventional CT features, the second added dual-layer spectral detector CT parameters, the third used only the radiomics features derived from the 40 keV virtual monoenergetic images, and the fourth integrated all three data streams into a single combined model. Performance was quantified using the area under the receiver operating characteristic curve, a standard metric expressing how well a model separates the two diagnostic classes across all possible decision thresholds.

The results were striking. In the training cohort, the combined model achieved an area under the curve of 0.956, substantially outperforming the conventional CT model at 0.794, the spectral CT model at 0.835, and the radiomics model at 0.871. DeLong’s test, a statistical procedure for comparing correlated areas under the curve, confirmed that these differences were significant, with all comparisons yielding p values below 0.05. In the validation cohort, the combined model retained strong performance at 0.905, though the gaps to the simpler models, which scored 0.787, 0.805, and 0.811 respectively, were no longer statistically significant, a common pattern when sample sizes are modest.

Beyond raw discrimination, the team interrogated the combined model with decision curve analysis and calibration assessment. Decision curve analysis evaluates the net clinical benefit of acting on a model’s predictions across a range of threshold probabilities, asking whether the model would lead to better decisions than treating all patients or none. Calibration analysis examines whether predicted probabilities match observed frequencies. Both analyses supported the robustness and favorable goodness-of-fit of the combined model, indicating that its high area under the curve was not a statistical artifact but reflected genuinely useful predictive structure.

To translate the model into clinical practice, the researchers plotted a nomogram, a graphical scoring tool that assigns weighted points to each predictive variable and sums them into a probability estimate. Nomograms occupy a beloved niche in oncology because they make complex multivariable models legible at the bedside: a clinician can read off a patient’s feature values, tally the points, and obtain an individualized risk estimate without any computation. In this case, the nomogram distills conventional CT findings, spectral parameters, and radiomics signatures into a single preoperative probability that a pancreatic neuroendocrine neoplasm is a tumor rather than a carcinoma.

The clinical stakes of such a tool are considerable. Pancreatic neuroendocrine tumors and neuroendocrine carcinomas demand different surgical strategies and different postoperative pathways, and preoperative uncertainty often forces clinicians to plan for the worst case. A validated noninvasive discriminator could refine surgical planning, inform the aggressiveness of lymph node dissection, and help select patients for neoadjuvant approaches. Because the entire workflow relies on images already acquired during standard contrast-enhanced CT examinations, the marginal cost and radiation burden to the patient are essentially zero, a significant advantage over additional imaging or biopsy.

The authors are appropriately measured about the limits of their work. The study was retrospective, conducted in accordance with the Declaration of Helsinki with ethics approval from the First Affiliated Hospital of Zhengzhou University, and drew on a relatively small single-region cohort of 128 patients. The validation set of 39 patients, while sufficient to demonstrate promise, cannot establish superiority over simpler models with statistical confidence, as the non-significant DeLong comparisons in that cohort make clear. The team concludes that the combined model shows promising performance that may aid clinical decision-making, but that its superiority requires further validation in external independent cohorts. If larger multi-center studies replicate these findings, spectral CT radiomics nomograms could become a standard preoperative instrument, turning routine CT scans into molecularly informative diagnostics for one of the pancreas’s most diagnostically treacherous tumor families.

Subject of Research: Preoperative differentiation of pancreatic neuroendocrine tumors from carcinomas using spectral CT radiomics and a nomogram

Article Title: A spectral CT radiomics nomogram for preoperative discrimination between pancreatic neuroendocrine tumors and carcinomas

Article References: Shi, J., Chen, Y., Zhang, Y., Zhang, H., Liu, M., Yu, F., Hao, H., Dang, J., Li, Z., Zhang, Y., & Wan, Y. (2026). A spectral CT radiomics nomogram for preoperative discrimination between pancreatic neuroendocrine tumors and carcinomas. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02869-0

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02869-0

Keywords: pancreatic neuroendocrine neoplasms, spectral CT, dual-layer detector CT, radiomics, nomogram, virtual monoenergetic images, machine learning, medical imaging, pancreatic cancer, logistic regression, diagnostic imaging, BMC Medical Imaging

The implications are significant. Tumor budding grade, classified under the International Tumor Budding Consensus Conference system as low-grade (Bd1) or moderate-to-high-grade (Bd2+3), influences decisions about the extent of surgery, the urgency of lymph node evaluation, and eligibility for neoadjuvant therapy. A high-grade budding tumor behaves like a formation of soldiers abandoning ranks and infiltrating enemy territory: the individual cells detach, migrate into surrounding stroma, and seed metastasis. Knowing that risk profile before surgery could reshape treatment planning for the roughly one million people worldwide diagnosed with colorectal cancer each year. What makes the new approach remarkable is that it extracts this hidden biological information from images already acquired during routine diagnostic scans, converting ordinary CT data into a predictor of microscopic tumor behavior.

The study enrolled 138 patients with colorectal adenocarcinoma treated between May 2021 and August 2025. All patients underwent dual-layer spectral detector CT, a technology that captures images at multiple energy levels simultaneously rather than producing a single conventional picture. From each scan, the researchers measured a battery of spectral quantitative parameters: the slope of the spectral Hounsfield unit curve, effective atomic number, iodine concentration, and normalized iodine concentration, along with virtual monoenergetic images reconstructed at specific keV levels such as 40 keV. These parameters reflect the physical composition of tissue, particularly its iodine uptake, which serves as a proxy for vascularity, perfusion and cellular density. A tumor that is busily angiogenic and densely packed with cells scatters X-rays differently from a more indolent lesion, and the spectral scanner registers those differences in numbers.

Alongside imaging, the researchers collected traditional clinical variables including age, sex, carcinoembryonic antigen, carbohydrate antigen 19-9, fasting blood glucose, low-density lipoprotein cholesterol and fecal occult blood test status. Univariate analysis identified which traditional and spectral parameters differed significantly between low-grade and moderate-to-high-grade tumor budding groups. Two emerged as independent predictors: low-density lipoprotein cholesterol, a routine blood marker, and the 40 keV virtual monoenergetic image in the arterial phase. The link between LDL-C and aggressive tumor biology is intriguing, given that cholesterol metabolism has long been implicated in cancer cell membrane synthesis and proliferative signaling. Its pairing with a low-energy virtual monoenergetic image, which maximizes iodine contrast and highlights hypervascular tumor regions, suggests the model is capturing complementary windows into tumor physiology.

The radiomics arm of the study was a masterclass in modern feature engineering. From three-dimensional volumes of interest drawn on spectral quantitative parameter images, the team extracted 107 radiomics features, quantifying tumor shape, texture, intensity distribution and higher-order spatial patterns. Because moderate-to-high-grade budding was the minority class, they applied the Synthetic Minority Oversampling Technique, or SMOTE, to synthesize representative examples and mitigate class imbalance, a common pitfall that causes machine learning models to simply predict the majority class. They then ran a sequential dimensionality reduction pipeline using Pearson correlation coefficients, analysis of variance, the Relief algorithm, recursive feature elimination and the Kruskal-Wallis test. That funnel winnowed 107 features down to 23 candidates for exploratory model construction, ensuring the final model would learn genuine biological signals rather than noise or spurious correlations.

The final nomogram fused three streams of information: the radiomics score derived from spectral images, the independent clinical indicator LDL-C, and the spectral parameter from the 40 keV arterial-phase image. When validated internally, the combined clinical-radiomics nomogram delivered the strongest performance, achieving a training set area under the curve of 0.895 with a 95 percent confidence interval of 0.818 to 0.972, and a test set AUC of 0.781 with a 95 percent confidence interval of 0.688 to 0.934. In practical terms, the model correctly distinguished low-grade from moderate-to-high-grade tumor budding in roughly eight to nine out of ten training cases, and in about four out of five test cases. The team also defined an optimal classification threshold for the nomogram score, providing clinicians with a concrete decision boundary rather than an abstract probability.

Performance metrics alone do not guarantee clinical usefulness, so the researchers went further. Calibration curves demonstrated good consistency between predicted and actual outcomes across the risk spectrum, with no statistically significant divergence. Decision curve analysis, a technique that quantifies the net benefit of acting on a model’s predictions at various threshold probabilities, showed that the nomogram delivered higher clinical net benefit than either the clinical model or the radiomics model alone. This matters because a model can be statistically impressive yet practically worthless if its thresholds lead clinicians to make worse decisions than they would without it. By that standard, the combined nomogram earned its place as a genuine candidate for clinical exploration, not merely an academic exercise.

The study does carry the honest limitations of its design. It was retrospective and single-center, drawing 138 patients from one institution, and the validation was internal rather than on an external cohort. Spectral CT remains less widely deployed than conventional CT, though dual-layer detector platforms are spreading rapidly through major hospitals. The authors were also transparent that this article was shared early to provide faster access to peer-reviewed, accepted research, and the version is subject to further edits before the final Version of Record. Still, the pipeline they describe, from SMOTE balancing to multi-algorithm feature selection to nomogram construction, is readily replicable, and the modest data requirements make it feasible for other centers to test and refine the approach on their own patient populations.

If external validation confirms these findings, the consequences for colorectal cancer care could be profound. A surgeon could review a nomogram score alongside routine staging scans and know, before making an incision, whether the tumor at hand is likely to be infiltrative and bud aggressively, potentially justifying wider resection margins, more rigorous lymph node harvesting or intensified adjuvant planning. Pathologists could see the prediction corroborated by histology afterward, closing a feedback loop that continuously improves both imaging and pathology. More broadly, the study exemplifies the rise of predictive medicine, in which the boundary between imaging and molecular pathology dissolves and routine scans become windows into tumor biology. The microscope is no longer the only way to see how a cancer behaves; sometimes, it turns out, the answer has been hiding in the X-rays all along.

Subject of Research: A clinical-radiomics nomogram combining spectral CT quantitative parameters and clinical variables to predict tumor budding grading in colorectal adenocarcinoma before surgery.

Article Title: A clinical-radiomics Nomogram based on spectral CT quantitative parameters for preoperative prediction of tumor budding grading in colorectal adenocarcinoma

Article References: Chen, J., Lu, L., Pan, X., Zhu, J., Li, M., Huang, J., Tang, X., Yan, X., Qian, T., Wang, M., & Liu, K. (2026). A clinical-radiomics Nomogram based on spectral CT quantitative parameters for preoperative prediction of tumor budding grading in colorectal adenocarcinoma. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02833-y

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02833-y

Keywords: colorectal adenocarcinoma, spectral CT, tumor budding, radiomics, nomogram, medical imaging, machine learning, SMOTE, virtual monoenergetic images, LDL cholesterol, predictive medicine, computed tomography

The research, led by Yuting Wen, Wanjiang Li, Xuelin Pan, Hangjia Hu and corresponding author Zhenlin Li of the Department of Radiology, with Xiaodi Zhang of Philips Healthcare, set out to answer a deceptively simple question: at which virtual monoenergetic energy level does spectral CT angiography best quantify coronary stenosis in patients whose arteries are burdened with calcified plaque? The team prospectively enrolled 42 patients with clinical suspicion of coronary atherosclerosis. Each participant underwent coronary CT angiography using a dual-layer spectral CT scanner and then completed invasive coronary angiography within four weeks, allowing the researchers to compare every imaging reconstruction against the reference standard in the same patients.

Dual-layer spectral CT, sometimes called detector-based spectral imaging, works differently from a conventional scanner. Instead of a single detector registering a blended X-ray spectrum, it stacks two detector layers that simultaneously record low- and high-energy photons from every projection. Software can then synthesize virtual monoenergetic images, or VMIs, which simulate how the anatomy would look if it were imaged with X-rays of a single, pure energy level measured in kiloelectron volts, or keV. At low energies around 40 keV, iodine contrast in the vessel lumen glows brightly, but beam hardening and noise increase. At high energies approaching 190 keV, calcium’s blooming shrinks and streak artifacts fade, but the iodine signal weakens and images can become washed out. Somewhere in between lies a sweet spot, and finding it for calcified coronary arteries was the study’s central goal.

In this trial, the researchers reconstructed conventional 120 kVp images along with virtual monoenergetic images spanning 40 to 190 keV at 30 keV intervals, all at the optimal cardiac phase on a dedicated post-processing workstation. They then compared image quality and vascular stenosis measurements across the multiple reconstruction groups, both between groups and within the same patients. Objective indicators such as signal-to-noise ratio and contrast-to-noise ratio were analyzed with one-way analysis of variance, followed by Tukey’s Honestly Significant Difference test for pairwise comparisons. Subjective image quality scores, assigned by radiologists assessing diagnostic confidence, artifacts and vascular visualization, were evaluated with the Friedman test. Two independent radiologists graded the images, and their inter-observer agreement was measured with Cohen’s weighted kappa, where values of 0.75 or above indicate excellent agreement.

The headline result is striking. Bland-Altman analysis, a statistical technique for quantifying agreement between two measurement methods, showed no significant difference between stenosis rates measured on 100 keV virtual monoenergetic images and those obtained from invasive coronary angiography, with a P value of 0.726. The bias rate of the 100 keV reconstructions was only approximately 0.1 percent, meaning the noninvasive measurements deviated from the catheter-based gold standard by a vanishingly small margin on average. By contrast, conventional 120 kVp images carried a bias rate of 10.3 percent, a discrepancy large enough to change clinical decisions in borderline cases. The improvement achieved by the 100 keV setting averaged 10.2 percentage points, a difference the authors report as statistically significant at P less than 0.01.

Image quality metrics told a consistent story. Significant differences in image quality were observed between the conventional 120 kVp images and every virtual monoenergetic group, all with P values below 0.01. Among the spectral reconstructions, the 100 keV images earned the highest subjective scores, reaching 4.81 plus or minus 0.40 on the rating scale for diagnostic confidence, image artifacts and vascular visualization, again with P less than 0.01. In practical terms, radiologists found that vessels surrounded by calcium were easier to trace, artifacts were less distracting, and they felt more confident rendering a diagnosis from the 100 keV series than from any alternative. Cohen’s weighted kappa values between the two observers all exceeded 0.75, confirming that this confidence was not the product of one reader’s idiosyncratic eye but a reproducible property of the images themselves.

The physics behind the result is worth unpacking. Calcified plaque and iodinated contrast differ in how their X-ray attenuation changes with photon energy. Calcium’s attenuation falls steeply as energy rises, so at 100 keV the bright halo around a calcified nodule contracts considerably, letting the contrast-opacified lumen behind it show through. Iodine, meanwhile, retains enough attenuation at 100 keV to keep the arterial lumen clearly delineated, even though its signal is weaker than at 40 or 70 keV. The 100 keV level therefore balances two competing demands: suppressing the calcium blooming that inflates apparent stenosis, while preserving the iodine contrast that defines the vessel wall and lumen. Lower energy reconstructions, despite their luminous iodine signal, amplify the very artifacts that distort stenosis grading in calcified segments, while higher energies sacrifice too much luminal contrast to remain diagnostically reliable.

The clinical implications are considerable. Invasive coronary angiography remains the reference standard for defining coronary stenosis, but it involves arterial catheterization, iodine loads, radiation exposure, procedural risk and cost, and it is not justified as a screening tool. Coronary CT angiography is noninvasive, fast and widely available, yet calcified plaques, which are common in older patients and in those with diabetes or chronic kidney disease, have long limited its accuracy, sometimes forcing patients into the catheterization laboratory on the basis of overestimated narrowing. If spectral CT scanners can routinely reconstruct 100 keV images that track invasive measurements within a fraction of a percent on average, the noninvasive test becomes substantially more trustworthy for the large population of patients with calcified coronary disease, potentially sparing some from unnecessary invasive procedures while ensuring that truly significant stenoses are not underestimated.

The study does have boundaries worth noting. Forty-two patients is a modest sample, and the cohort consisted of individuals with clinical suspicion of coronary atherosclerosis at a single center, so larger multicenter validation will be needed before the 100 keV recommendation becomes universal practice. The analysis focused on stenosis quantification in calcified plaques rather than on plaque characterization, ischemia prediction or outcomes, and the scanners, reconstruction software and reader expertise at West China Hospital may not translate identically to every imaging environment. The work was supported by the 1.3.5 project for disciplines of excellence at West China Hospital, Sichuan University, and the authors declare no competing interests. The article was published open access under a Creative Commons license, received by the journal on 15 July 2026, accepted on 28 August 2026 and published on 11 September 2026.

Even so, the findings land at a moment when spectral CT is spreading rapidly through hospital radiology departments, and they offer an unusually concrete, actionable takeaway: when quantifying coronary stenosis in the presence of calcified plaque, reconstruct and read the 100 keV virtual monoenergetic images. The study demonstrates that a single, well-chosen energy level can convert spectral CT from an imaging novelty into a measurement instrument whose numbers align with the catheter lab. For patients, that could mean fewer ambiguous reports and fewer unnecessary invasive procedures. For radiologists and cardiologists, it supplies an evidence-based default setting for one of coronary imaging’s most stubborn problems. As dual-layer detectors become standard equipment, the humble kiloelectron volt dial, tuned to 100 keV, may quietly become one of the most consequential settings in cardiac imaging.

Subject of Research: Determining the optimal virtual monoenergetic energy level of dual-layer spectral CT angiography for quantifying coronary stenosis in patients with calcified coronary plaques.

Article Title: Optimal virtual monoenergetic energy level of dual-layer spectral CT angiography for coronary stenosis quantification in patients with calcified coronary plaques: a prospective study

Article References: Wen, Y., Li, W., Pan, X., Hu, H., Zhang, X., & Li, Z. (2026). Optimal virtual monoenergetic energy level of dual-layer spectral CT angiography for coronary stenosis quantification in patients with calcified coronary plaques: a prospective study. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02746-w

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02746-w

Keywords: spectral CT, coronary CT angiography, calcified plaque, coronary stenosis, virtual monoenergetic images, dual-layer detector, invasive coronary angiography, image quality, blooming artifact, BMC Medical Imaging, radiology, atherosclerosis