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.

Pancreatic lymphoma is a rare entity that can mimic other pancreatic pathologies, particularly pancreatic ductal adenocarcinoma (PDAC), making accurate radiologic recognition essential as management differs significantly. Pancreatic lymphoma is treated with chemotherapy and generally carries a more favorable prognosis, whereas PDAC typically requires surgical intervention. Through a series of illustrative cases, many of which were initially referred to a PDAC multidisciplinary conference but ultimately proven to be lymphoma, we highlight the diverse CT imaging spectrum of pancreatic lymphoma, which extends well beyond the classically described bulky pancreatic mass. Observed patterns include focal well-defined hypodense masses, multifocal pancreatic lesions, diffuse infiltrative enlargement mimicking pancreatitis, and masses with extrapancreatic spread into adjacent organs. While classic features such as absent pancreatic ductal dilatation, lack of vascular occlusion, and homogeneous hypoattenuation can help distinguish lymphoma from PDAC, variant presentations with ductal dilation, necrosis, or vascular involvement may occur. Awareness of these diverse imaging appearances may help radiologists include pancreatic lymphoma in the differential diagnosis of hypodense pancreatic masses, advising tissue sampling to ensure accurate diagnosis and guide appropriate management.

Automated cell density quantification in normal human kidney tissue

OBJECTIVE:Chronic pancreatitis (CP) is a risk factor for pancreatic ductal adenocarcinoma (PDAC), with a cumulative risk of ~4% over 20 years. Most prior studies have evaluated the risk of PDAC among patients with CP. The frequency and imaging characteristics of chronic calcific pancreatitis (CCP) in patients presenting with PDAC are unknown. METHODS:A retrospective review of the clinical history and staging computed tomography (CT) scans of patients diagnosed with resectable or borderline resectable/locally advanced PDAC between 2011 and 2022 was conducted. CCP and ductal dilatation were defined by the presence of pancreatic calcifications and a maximal pancreatic ductal diameter >5 mm on CT, respectively. Two independent radiologists reviewed all imaging studies, and discrepancies were adjudicated by a third radiologist. RESULTS:Among 1008 patients with PDAC, CCP was found in 34 (3.4%), with a mean age of 69.9±8 years, 50% men, 91% White, and 59% with a history of smoking, with 26.95±18.5 pack-years. Only 7 (21%) patients had a documented history of chronic abdominal pain before PDAC diagnosis. The presence of ductal dilation, pseudocysts, or pancreatic atrophy was observed in nearly 40%, 10%, and 40% of patients, respectively, and 7 (21%) showed none of these features. Ductal dilation was associated with higher levels of CA 19-9 (349.8 U/mL [IQR; 238.3, 455.0] vs. 90.0 U/mL [IQR; 58.3, 162.2], P=0.003). Intratumoral and extratumoral-only calcifications were found in 14 (41%) and 20 (59%) patients, respectively. Extratumoral-only calcifications were associated with pancreatic atrophy (P=0.02). Radiologist inter-rater agreement was highest for tumor location, calcification location, maximal size, and number, and side branch dilatation, and was lowest for the percentage of pancreas with abnormal findings and PD contour. CONCLUSION:CCP is found in 3% of patients presenting with PDAC, and most have primary painless disease. Further studies are required to determine optimal PDAC screening strategies and surveillance in patients found to have primary painless CCP.

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

Hepatic lymphomas are rare malignancies that are classified as either primary hepatic lymphoma (PHL) or secondary hepatic lymphoma (SHL). They can present with several CT patterns, including a solid focal lesion, multifocal lesions, a diffuse pattern, or a porta hepatis lesion. PHLs most often present as solid lesions, whereas SHLs are commonly multifocal. On CT, they typically appear as hypovascular, hypoattenuating masses that may show central low-density necrosis and demonstrate only minimal enhancement compared to the surrounding parenchyma. Rim enhancement may be seen, creating a “target sign.” These lesions often exhibit an infiltrative growth pattern, encasing vessels, and biliary structures without causing compression. Although rare, it is important to include lymphoma in the differential diagnosis of a hypodense liver lesion. This pictorial essay illustrates the CT patterns and features of hepatic lymphoma that will assist radiologists in evaluating it as a possible diagnosis.

The loss of corticomedullary differentiation (CMD) on contrast-enhanced CT reflects disruption of the physiologic perfusion gradient between the renal cortex and medulla. While benign and inflammatory disorders remain the most frequent causes, malignant renal processes constitute an important subset of pathology in which recognition of altered CMD may have major diagnostic and therapeutic implications. In neoplastic presentations, diffuse or segmental alterations in CMD arise from replacement, compression, or infiltration by tumor, or from the interplay of vascular and urinary obstructive mechanisms. Characteristic enhancement kinetics across the different enhancement phases may allow differentiation among various renal masses. This pictorial review focuses on the imaging hallmarks and pathophysiologic correlates of malignant renal neoplasms with associated altered CMD, highlighting multiphasic CT patterns, representative cases, and diagnostic pitfalls.

Organ volume versus relative risk of cancer

Body mass index versus organ volume: validation analysis

Pancreatic schwannomas (PSs) are rare, benign nerve sheath tumors that can be encountered as incidental findings on imaging, and their imaging features overlap with other solid and cystic pancreatic lesions. This often leads to preoperative misdiagnosis. On computed tomography (CT), which is often the first modality to detect these tumors, PS classically appears as a well-defined, round or oval, encapsulated mass without pancreatic ductal dilation when small. Degenerative changes in larger lesions can produce cystic change, septations, calcification, and hemorrhage. In this pictorial essay, we present the CT spectrum of PS through a series of illustrative cases, spanning typical and atypical appearances, to help radiologists recognize this rare tumor and include it as a benign differential diagnosis for pancreatic masses.

The renal corticomedullary interface represents a physiologic differentiation in perfusion between the highly vascular cortex and the medulla. On computed tomography (CT), the corticomedullary phase (CMP), typically acquired 25–35 s after contrast administration, provides an optimal window for evaluating these perfusion differences during the early phase of contrast enhancement. Although often discussed in the context of neoplastic disease, the loss of corticomedullary differentiation (CMD) most commonly occurs across a variety of nonmalignant processes and can serve as an early indicator of underlying renal pathology, including vascular insults, infectious etiologies, functional disturbances related to acute kidney injury, and fibrotic or infiltrative processes seen in chronic kidney disease. A structured approach to evaluating CMD, recognizing characteristic enhancement patterns of pathology, and correlating them with clinical context can enable radiologists to differentiate among the most common presentations. In this pictorial review, we illustrate the normal appearance and enhancement dynamics of the CMP and highlight characteristic imaging patterns of CMD loss across key nonmalignant entities. These imaging patterns underscore the sensitivity of altered CMD in detecting renal pathologies and may improve diagnostic confidence and guide timely clinical management.

Emphysematous cystitis, also known as 'cystitis emphysematosa,' is a rare complicated urinary tract infection characterized by gas within the bladder wall. Most cases are mild and detected incidentally; however, delayed management can lead to significant morbidity and mortality, particularly when infection extends to the upper urinary tract. With increasing use of cross-sectional imaging, radiologists are encountering this condition more frequently. This pictorial review presents 20 cases of emphysematous cystitis from our institution, illustrating the range of CT imaging patterns and clinical features from representative cases. We identified three distinct patterns of gas distribution: loculated or curvilinear focal collections (40% of cases), single layer of cobblestone/beaded necklace pattern (30% of cases), and circumferential dissection of the bladder wall with multilayered gas (30% of cases). Diabetes is the most common predisposing factor. Other risk factors included neurogenic bladder, long-term urinary catheters, immunosuppression, chemotherapy, recurrent urinary stones, and frequent urologic procedures. The clinical presentation ranges from incidental detection on imaging to symptomatic patients presenting with acute abdomen and dysuria in the emergency department (ED). Treatment primarily consists of antibiotic therapy, with surgical intervention required only in select severe cases. Understanding these CT patterns along with the associated clinical features and risk factors allows radiologists to accurately diagnose emphysematous cystitis and guide timely clinical management.

Hepatic abscess (HA) poses a diagnostic challenge worldwide, with up to around 80

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.

Characteristics of participants in the kidney autopsy cohort

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.

Patients with pancreatic cancer have low survival rates, largely because patients are diagnosed at an advanced stage. Current strategies for early detection, including imaging, blood tests, and genetic sequencing, have limited performance. Recent advances in artificial intelligence (AI) have shown that AI models can identify subtle pre-diagnostic imaging changes that may not be visible to radiologists, raising the possibility of earlier and more consistent pancreatic cancer detection. Despite this progress, real-world implementation of AI for pancreatic cancer detection remains limited. Most models struggle with reproducibility and generalizability across different institutions. Few have undergone prospective validation, and practical issues such as workflow integration, financial constraints, and continuous model monitoring remain unresolved. This article reviews the current state of AI for pancreatic cancer detection and outlines barriers beyond model specifics that must be addressed to enable clinical translation.