Obesity is a well-established risk factor for several cancers, yet the underlying mechanisms remain incompletely understood. We hypothesized that as body size increases with obesity, organ size increases to meet metabolic demands, which in turn raises the number of cells at risk of malignant transformation. Measurement of the liver, pancreas, and kidney volumes in 747 adults across a wide body mass index (BMI) range (17.8-70.9 kg/m2) showed a strong positive correlation between BMI and organ size: a 5-unit increase in BMI was significantly associated with volume increases of 12% in the liver, 9% in both kidneys combined, and 7% in the pancreas. To determine the cellular basis of organ enlargement, kidney cell numbers were quantified using both autopsy samples (34,337 proximal tubular epithelial cells) and biopsy data from 25 individuals. The total number of cells increased substantially, indicating that approximately 61% of kidney enlargement was due to hyperplasia, with the remaining 39% increase attributable to hypertrophy. Moreover, organ volume ratios, relative to volume for normal-weight adults, strongly correlated with cancer risk across the three organs, indicating that a doubling in organ volume corresponded approximately to a doubling in cancer risk. These findings suggest a mechanism linking obesity to cancer: as body size and metabolic demands increase, organs expand primarily through hyperplasia that increases the number of cells susceptible to malignant transformation, complementing known pathways involving inflammation, hormones, and metabolic dysregulation. SIGNIFICANCE:Increasing body size corresponds to organ enlargement predominantly driven by hyperplasia that results in a greater number of cells at risk of malignant transformation, linking obesity to cancer risk.

Automated cell density quantification in normal human kidney tissue

Relationships between epithelium cross-sectional area, proximal tubule cell nuclei count and BMI in kidney biopsies

Relationship between estimated organ size ratios and relative cancer risk

Body Mass Index (BMI) and weight in 744 individuals for whom weight was reported

Organ volume versus relative risk of cancer

Body mass index versus organ volume: validation analysis

Relationship between liver, pancreas, and kidney volumes and BMI.

PURPOSE:The purpose of this study was to evaluate the contribution of radiomics features extracted from various pancreatic structures on computed tomography (CT) images, including the main pancreatic duct and cystic lesion, for predicting the pathological grade of intraductal papillary mucinous neoplasms (IPMNs) using machine learning models. MATERIALS AND METHODS:A retrospective study using preoperative CT images obtained during the venous phase of enhancement in patients with pathologically confirmed IPMNs (2003-2024) was conducted. Main pancreatic ducts and cysts were manually segmented. Machine learning models were trained to classify IPMNs into high-grade/associated invasive carcinoma (HG/I) IPMNs or low-grade (LG) IPMNs using radiomics features from three structures (i.e., cysts, main pancreatic duct, and a combination of both structures). Model performance was evaluated using area under the receiver operating characteristic curve (AUC). SHapley Additive exPlanations (SHAP) values were used to interpret feature importance. RESULTS:A total of 274 patients with IPMNs were included. There were 149 patients with HG/I IPMNs (70 women [47 %]; median age, 71.0 years; age range: 29-92) and 125 patients with LG IPMNs (73 women [58.4 %]; median age, 68.0 years; range: 35-86). HG/I IPMNs were predominantly mixed-type IPMNs (51.7 %; 77/149). LG IPMNs were unspecified (51/125; 40.8 %), main/mixed (40/125; 32 %), or branch-duct type (34/125; 27.2 %). A support vector machine trained on combined features achieved the largest AUC (0.85; 95 % confidence interval [CI]: 0.85-0.87; P < 0.001), with 90 % sensitivity (95 % CI: 90-93), and 60 % specificity (95 % CI: 58-62). SHAP analysis identified main pancreatic duct radiomics features as having the largest contribution to model output. CONCLUSION:Integrating CT-based radiomics features from pancreatic ducts and cysts improves classification performance, with main pancreatic duct features being the most contributive predictor of IPMN grade.

Abstract Obesity is a well-established risk factor for several cancers, yet the underlying mechanisms remain incompletely understood. We hypothesized that as body size increases with obesity, organ size increases to meet metabolic demands, which in turn raises the number of cells at risk of malignant transformation. Measurement of the liver, pancreas, and kidney volumes in 747 adults across a wide body mass index (BMI) range (17.8–70.9 kg/m2) showed a strong positive correlation between BMI and organ size: a 5-unit increase in BMI was significantly associated with volume increases of 12% in the liver, 9% in both kidneys combined, and 7% in the pancreas. To determine the cellular basis of organ enlargement, kidney cell numbers were quantified using both autopsy samples (34,337 proximal tubular epithelial cells) and biopsy data from 25 individuals. The total number of cells increased substantially, indicating that approximately 61% of kidney enlargement was due to hyperplasia, with the remaining 39% increase attributable to hypertrophy. Moreover, organ volume ratios, relative to volume for normal-weight adults, strongly correlated with cancer risk across the three organs, indicating that a doubling in organ volume corresponded approximately to a doubling in cancer risk. These findings suggest a mechanism linking obesity to cancer: as body size and metabolic demands increase, organs expand primarily through hyperplasia that increases the number of cells susceptible to malignant transformation, complementing known pathways involving inflammation, hormones, and metabolic dysregulation. Significance: Increasing body size corresponds to organ enlargement predominantly driven by hyperplasia that results in a greater number of cells at risk of malignant transformation, linking obesity to cancer risk.

Characteristics of participants in the kidney autopsy cohort

Cortical tubule cell density versus kidney volume from kidney autopsies

Mycophenolate mofetil (MMF), an immunosuppressive, is a pharmacologically inactive compound of mycophenolic acid, which has been widely used in solid organ transplant and autoimmune conditions. It mostly exerts gastrointestinal (GI) adverse effects, which include diarrhea, abdominal pain, nausea, and vomiting. It can lead to MMF-colitis, a challenging condition to diagnose due to its similarity with other GI-related conditions and infections. This case report discusses a heart transplant recipient who developed severe MMF-induced colitis. It adds significantly to the limited literature available for this difficult-to-diagnose condition. It also highlights the severity of the condition and underscores the importance of vigilant monitoring and the need for future cohort studies to set guidelines for diagnosing and treating MMF-associated colitis due to its widespread use.

BACKGROUND/OBJECTIVES:To evaluate the importance of image processing in a previously validated model for detecting pancreatic neuroendocrine tumors (PanNETs) and to introduce Image2Radiomics, a new framework that ensures reproducibility of the image processing pipeline and facilitates the deployment of radiomics models. METHODS:A previously validated model for identifying PanNETs from CT images served as the reference. Radiomics features were re-extracted using Image2Radiomics and compared to those from the original model using performance metrics. The impact of nine alterations to the image processing pipeline was evaluated. Prediction discrepancies were quantified using the mean ± SD of absolute differences in PanNET probability and the percentage of classification disagreement. RESULTS:The reference model was successfully replicated with Image2Radiomics, achieving a Cohen's kappa coefficient of 1. Alterations to the image processing pipeline led to reductions in model performance, with AUC dropping from 0.87 to 0.71 when image windowing was removed. Prediction disagreements were observed in up to 45% of patients. Even minor changes, such as switching the library used for spatial resampling, resulted in up to 21% disagreement. CONCLUSIONS:Reproducing image processing pipelines remains challenging and limits the clinical deployment of radiomics models. While this study is limited to one model and imaging modality, the findings underscore a common risk in radiomics reproducibility. The Image2Radiomics framework addresses this issue by allowing researchers to define and share complete processing pipelines in a standardized way, improving reproducibility and facilitating model deployment in clinical and multicenter settings.

Background:Deep learning-based pancreas segmentation in CT has advanced rapidly yet remains evaluated primarily with mean overlap metrics that fail to capture robustness-defined as the proportion of cases reaching human-level performance. Models performing well on mean Dice or surface metrics can still fail unpredictably across scanners or anatomies. Because early detection and quantitative biomarkers rely on consistent segmentation, robustness is critical for clinical deployment. Purpose:To systematically evaluate the robustness of deep learning models for pancreas segmentation relative to human readers and to investigate an active learning strategy to improve reliability. Materials and Methods:We retrospectively assembled 903 venous-phase CT scans from patients with presumed normal-appearing pancreases and without known pancreatic disease (2005-2023), split into 803 for training/validation and 100 healthy test cases. Each test case had 4 independent human segmentations. Inter-reader variability on this healthy-only test set defined the empirical human distribution, providing an upper-bound estimate of robustness. We introduced a Fractional Threshold (FT) metric, measuring the proportion of model predictions exceeding the minimum human performance. Robustness was assessed across models trained from scratch, fine-tuned, or pretrained, including both normal and abnormal cases. An active learning approach identified high-uncertainty predictions for human revision. Statistical comparisons were performed using the Wilcoxon signed-rank and proportions Z-tests. Results:The best model, a 3-dimensional U-Net trained from scratch, achieved a Dice Similarity Coefficient (DSC) of 0.88 ± 0.04 and Normalized Surface Dice (NSD) of 0.77 ± 0.09, approaching human-level segmentation (DSC = 0.89 ± 0.03; NSD = 0.75 ± 0.07). However, FT for DSC and NSD remained lower than human performance in most cases, indicating persistent model variability. Human-in-the-loop revision of acquisition-flagged outliers increased FT to 0.99, with an average time of 1.54 minutes per case, corresponding to a 23-fold workload reduction. Conclusion:Automated pancreas segmentation reduces workload but remains constrained by tail-case failures. Active learning enhances model reliability, bridging the gap between artificial intelligence and human-level performance.

BACKGROUND/OBJECTIVES:Accurate identification of grade 1 (G1) pancreatic neuroendocrine tumors (PanNETs) is crucial due to their rising incidence and emerging nonsurgical management strategies. This study evaluated whether combining conventional CT imaging features, CT radiomics features, and clinical data improves differentiation of G1 PanNETs from higher-grade tumors (G2/G3 PanNETs and pancreatic neuroendocrine carcinomas [PanNECs]) compared to using these features individually. METHODS:A retrospective analysis included 133 patients with pathologically confirmed PanNETs or PanNECs (70 males, 63 females; mean age, 58.5 years) who underwent pancreas protocol CT. A total of 28 conventional imaging features, 4892 radiomics features, and clinical data (age, gender, and tumor location) were analyzed using a support vector machine (SVM) model. Data were divided into 70% training and 30% testing sets. RESULTS:The SVM model using the top 10 conventional imaging features (e.g., suspicious lymph nodes and hypoattenuating tumors) achieved 75% sensitivity, 81% specificity, and 79% accuracy for identifying higher-grade tumors (G2/G3 PanNETs and PanNECs). The top 10 radiomics features yielded 94% sensitivity, 46% specificity, and 69% accuracy. Combining all features (imaging, radiomics, and clinical data) improved performance, with 94% sensitivity, 69% specificity, 79% accuracy, and an F1-score of 0.77. The radiomics score demonstrated an AUC of 0.85 in the training and 0.83 in the testing set. CONCLUSIONS:Conventional imaging features provided higher specificity, while radiomics offered greater sensitivity for identifying higher-grade tumors. Integrating all three features improved diagnostic accuracy, highlighting their complementary roles. This combined model may serve as a valuable tool for distinguishing higher-grade tumors from G1 PanNETs and potentially guiding patient management.