Suman, Garima

ABSTRACT Background Family history of pancreatic cancer (PC), pathogenic germline variants (PGVs), and increased intrapancreatic fat (IPF) are individually associated with increased PC risk. Whether IPF is elevated in high‐risk individuals (HRIs) with PC family history or PGVs is unknown. Although MRI is the preferred modality for IPF measurement, region‐of‐interest–based approaches are prone to sampling bias because pancreatic fat is heterogeneously distributed. We aimed to compare IPF among HRIs and matched non‐HRI controls using an Artificial intelligence (AI)‐based tool for whole pancreatic IPF measurement applied to magnetic resonance imaging (MRI). Methods We developed and validated a deep‐learning pipeline for IPF measurement that incorporates automated pancreas segmentation on T1‐weighted MRI images registered to the corresponding iterative decomposition of water and fat with echo asymmetry and least squares estimation images to compute IPF using proton density fat fraction (PDFF). This pipeline was used to quantify IPF and assess IPF distribution patterns in 85 HRIs and 170 non‐HRIs matched 1:2 for age, sex, BMI, and diabetes status. A surface erosion approach was developed to minimize the impact of peripancreatic fat on IPF measurement. Linear models adjusting for matching variables were used to assess associations. Results Model‐expert agreement for whole‐pancreas PDFF was excellent (intraclass correlation coefficient 0.99; 95% CI, 0.98–0.99 mean absolute error 1.4%). Mean PDFF did not differ between HRIs and controls (25.8% [11.5] vs. 26.9% [11.4]; p = 0.32). PDFF increased with age ( p = 0.015), male sex ( p < 0.001), presence of diabetes ( p = 0.009), BMI ( p < 0.001). IPF distribution patterns did not differ by risk group. Conclusion An AI‐based approach provides accurate, scalable, and objective assessment of IPF quantity and distribution. IPF quantity and spatial distribution do not appear to differ based on germline or familial predisposition to PC.

Abstract Background and Aims EUS-guided gastroenterostomy (EUS-GE) is an effective option to palliate symptoms of gastric outlet obstruction (GOO). While the jejunum is conventionally targeted (EUS-GJ), targeting the duodenum (EUS-GD) may offer clinical advantages. Outcomes were compared for EUS-GJ and EUS-GD. Methods Adult patients who underwent EUS-GE for GOO between 2020 and 2024 were included and categorized as an EUS-GD if the stent entered the transverse or ascending duodenum or an EUS-GJ if the stent entered more distally. The primary outcome was clinical success (soft diet tolerance at 7 days). Secondary outcomes included adverse events (AEs), stent misdeployment, and unplanned endoscopic reintervention. Baseline demographic variables and outcomes were compared with statistical significance at p < 0.05. Results Of the 139 included patients (median age 67 years (IQR 61–74); 41% female), 105 underwent EUS-GJ and 34 underwent EUS-GD. Clinical success was 98% for EUS-GJ and 94% for EUS-GD (p = 0.25). The misdeployment rate was 7% for EUS-GJ and 0% for EUS-GD (p = 0.19). The AE rate was 22% for EUS-GJ and 12% for EUS-GD (p = 0.23). There were no significant differences for stent obstruction (EUS-GJ 12% vs. EUS-GD 6%), bleeding (2% vs. 6%), perforation (4% vs. 0%), stent migration (1% vs. 0%), infection (3% vs. 0%), and unplanned reintervention (14% vs. 9%). Conclusion Both EUS-GJ and EUS-GD demonstrate high clinical success with no significant differences in adverse event rates. These data can support intraprocedural decision-making for endoscopists performing EUS-GE.

Abstract Renal tumors are highly heterogeneous, ranging from benign lesions and indolent renal cell carcinoma (RCC) to highly aggressive phenotypes. Accurately determining tumor aggressiveness before treatment remains a major clinical challenge. Conventional MR imaging lacks sensitivity to microarchitectural differences underlying tumor biology, and biopsy is limited by sampling error and tumor heterogeneity. Radiomics offers a noninvasive approach to extract high dimensional features that may capture biologically relevant patterns. In this IRB-approved retrospective study, we evaluated MR-based radiomics combined with machine learning (ML) to differentiate indolent from aggressive tumors. Pre-operative clinically acquired MR images obtained from eighty-two RCC patients (aggressive, n = 56; indolent, n = 26) who underwent nephrectomy were included for the analysis. Regions of interest (ROIs) of the entire tumor were manually drawn using 3D Slicer on the morphological T2-weighted images and four phases (pre-contrast [PRE], corticomedullary [CM], nephrographic [NG], and delayed [DEL]) from the contrast-enhanced MR images, normalized, and used for radiomic feature extraction (PyRadiomics, v3.1.0). Independently, tumors were grouped into aggressive and indolent phenotypes based on the established histological classification of Mayo Clinic. Statistical analysis used two-sided t-test. Features selected based on mutual information were used for ML classification (Random Under-Sampling Boosting (RUSBoost)) and performance assessed using accuracy, sensitivity, precision, specificity, F1-score, and area under the curve (AUC). Of 107 radiomics features extracted, the following numbers showed statistically significant differences between aggressive and indolent tumors (p &lt; 0.05): CM phase (n = 19), NG phase (n = 24), DEL phase (n = 22), and T2-weighted images (n = 29). Several features consistently differed among contrast-enhanced images and included first-order (length, surface area, volume), GLCM (correlation, IDM, IMC1, IMC2), GLDM (dependence entropy, dependence non-uniformity, dependence variance), GLRLM (gray-level non-uniformity, run entropy, run-length non-uniformity), GLSZM (high gray-level emphasis, zone entropy), and NGTDM (coarseness). Using the 15 features with highest mutual information across 50 patients (aggressive, n = 31; indolent, n = 19), a five-fold cross-validation RUSBoost classifier achieved accuracy 0.81, sensitivity 0.84, precision 0.87, specificity 0.77, F1-score 0.85, and AUC 0.80 for identifying the aggressive phenotype. MRI radiomics features show potential to differentiate aggressive from indolent renal tumors. ML-based classification using these features demonstrates promising accuracy. Further optimization and validation in larger cohorts, may help guide individualized care, including consideration of active surveillance for indolent renal tumors. Citation Format: Huan Lu, Salim Rukhsar, Xueqing Yin, Garima Suman, Ashish Khandelwal, Ananth J. Madhuranthakam, Durga Udayakumar. MRI radiomic features to differentiate aggressive and indolent renal tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2142.

Radiologists are uniquely positioned to play a critical role in the detection, surveillance, and characterization of hereditary abdominal cancer syndromes.

Abstract Objectives: Radiologist participation in multidisciplinary tumor board can be time-consuming and demanding, requiring substantial preparation. Anecdotally radiologist participation is felt to help identify discrepancies in previous interpretations; however, there are limited data assessing the actual impact of their tumor board review on patient management. This prospective study aims to evaluate how the reinterpretation of outside imaging and review at hepatobiliary tumor board (HBTB) affects the diagnosis and management of patients with hepatobiliary neoplasms. Methods: This prospective analysis included all patients presented at the weekly HBTB between 6/13/2023 - 6/25/2024. One radiologist reviewed the available reports of outside imaging, the local reinterpretation of outside imaging, the updated local imaging, and documented conclusions from discussion of the case at HBTB. HBTB included 4 radiologists who had reviewed each case. Discrepancies (presence/absence, number, and reasons for) were recorded. When discrepancies occurred, the impact on patient management (5-point Likert scale) was determined by two of the radiologists present at the HBTB in consensus, with input from clinicians if needed. A six-month follow-up was performed to confirm suspected diagnoses. Results: 208 cases (128 males, 104 chronic liver disease patients) were presented at HBTB. Disagreements in the detection or characterization of lesions were found in 59/208 (28.4%) of cases. A change in management occurred in 38/59 (64.6%) of these discrepant cases. In cases with recorded discrepancies in which follow-up confirmation was available, the final interpretation was confirmed in 34/41 (82.9%) of cases. Conclusion: Our study found a discrepant interpretation in nearly one-third of cases presented at a weekly HBTB and a resultant change in management in 65% of those cases. These findings highlight the importance of reinterpretation of prior imaging studies and the presence of radiologists at HBTB to guide optimal patient management.

Abstract Appendiceal intussusception is a rare condition characterized by the telescoping or invagination of a portion or the entire appendix into the caecum or within the appendix itself. Diagnosing appendiceal intussusception can be challenging due to its rarity, non-specific symptoms, and lack of awareness among physicians. We present a case report of appendiceal intussusception caused by endometriosis presenting with recurrent abdominal pain in a young female that was initially missed on CT scan and laparoscopy and eventually diagnosed on CT enterography.

Objective: Investigate the performance of multiparametric MRI radiomic features, alone or combined with current standard-of-care methods, for pulmonary nodule classification. Assess the impact of segmentation variability on feature reproducibility and reliability. Methods: Radiomic features were extracted from 74 pulmonary nodules of 68 patients who underwent nodule resection or biopsy after MRI exam. The MRI features were compared with histopathology and conventional quantitative imaging values (maximum standardized uptake value [SUVmax] and mean Hounsfield unit [HU]) to determine whether MRI radiomic features can differentiate types of nodules and associate with SUVmax and HU using Wilcoxon rank sum test and linear regression. Diagnostic performance of features and four machine learning (ML) models were evaluated with area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs). Concordance correlation coefficient (CCC) assessed the segmentation variation impact on feature reproducibility and reliability. Results: Elevn diffusion-weighted features distinguished malignant from benign nodules (adjusted p < 0.05, AUC: 0.73–0.81). No features differentiated cancer types. Sixty-seven multiparametric features associated with mean CT HU and 14 correlated with SUVmax. All significant MRI features outperformed traditional imaging parameters (SUVmax, mean HU, apparent diffusion coefficient [ADC], T1, T2, dynamic contrast-enhanced imaging values) in distinguishing malignant from benign nodules with some achieving statistical significance (p < 0.05). Adding ADC and smoking history improved feature performance. Machine learning models demonstrated strong performance in nodule classification, with extreme gradient boosting (XGBoost) having the highest discrimination (AUC = 0.83, CI=[0.727, 0.932]). We found good to excellent inter- and intrareader feature reproducibility and reliability (CCC≥0.80). Conclusion: Eleven MRI radiomic features differentiated malignant from benign lung nodules, outperforming traditional quantitative methods. MRI radiomic ML models demonstrated good nodule classification performances with XGBoost superior to three others. There was good to excellent inter- and intrareader feature reproducibility and reliability. Advances in knowledge: Our study identified MRI radiomic features that successfully differentiated malignant from benign lung nodules and demonstrated high performance of our MR radiomic feature-based ML models for nodule classification. These new findings could help further establish thoracic MRI as a non-invasive and radiation-free alternative to standard practice for pulmonary nodule assessment.

Purpose This study aimed to compare accuracy and efficiency of a convolutional neural network (CNN)–enhanced workflow for pancreas segmentation versus radiologists in the context of interreader reliability. Methods Volumetric pancreas segmentations on a data set of 294 portal venous computed tomographies were performed by 3 radiologists (R1, R2, and R3) and by a CNN. Convolutional neural network segmentations were reviewed and, if needed, corrected (“corrected CNN [c-CNN]” segmentations) by radiologists. Ground truth was obtained from radiologists' manual segmentations using simultaneous truth and performance level estimation algorithm. Interreader reliability and model's accuracy were evaluated with Dice-Sorenson coefficient (DSC) and Jaccard coefficient (JC). Equivalence was determined using a two 1-sided test. Convolutional neural network segmentations below the 25th percentile DSC were reviewed to evaluate segmentation errors. Time for manual segmentation and c-CNN was compared. Results Pancreas volumes from 3 sets of segmentations (manual, CNN, and c-CNN) were noninferior to simultaneous truth and performance level estimation–derived volumes [76.6 cm3 (20.2 cm3), P < 0.05]. Interreader reliability was high (mean [SD] DSC between R2-R1, 0.87 [0.04]; R3-R1, 0.90 [0.05]; R2-R3, 0.87 [0.04]). Convolutional neural network segmentations were highly accurate (DSC, 0.88 [0.05]; JC, 0.79 [0.07]) and required minimal-to-no corrections (c-CNN: DSC, 0.89 [0.04]; JC, 0.81 [0.06]; equivalence, P < 0.05). Undersegmentation (n = 47 [64%]) was common in the 73 CNN segmentations below 25th percentile DSC, but there were no major errors. Total inference time (minutes) for CNN was 1.2 (0.3). Average time (minutes) taken by radiologists for c-CNN (0.6 [0.97]) was substantially lower compared with manual segmentation (3.37 [1.47]; savings of 77.9%–87% [P < 0.0001]). Conclusions Convolutional neural network–enhanced workflow provides high accuracy and efficiency for volumetric pancreas segmentation on computed tomography.