Citations

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 Purpose To assess if PSA density (PSAD) velocity is a predictor of clinically significant prostate cancer (csPCa). Methods Patients undergoing prostate MR for suspected csPCa between 2017 and 2023 were included. Of these, patients who underwent two or more MR exams within 0.3 to 2 years to each other and pathological confirmation within 1 year after the last MR were identified. PI-RADS score, patient age, screening or active surveillance (AS), PSA, PSAD, PSA-velocity and PSAD-velocity were assessed if they were predictor of csPCa. For univariate analysis, all variables were assessed using student t or chi-square tests. When variables were considered significant, variables were entered into multivariate logistic regression model. Results 23,244 MRs were performed (17,703 screening, 5,541AS). 1,174 patients (mean age: 65.9) met the inclusion criteria, and 550 patients (47%) were diagnosed with csPCa. On univariate analysis, PI-RADS score (p<.0001), age (67.0 vs 64.9, p<.0001), AS exam (67% vs 59%, p = 0.004), PSA (7.5 vs 6.6, p<.001 ), PSAD (0.16 vs 0.12, p<.0001) and PSAD-velocity (0.023 vs 0.012, p = 0.005 ) were predictors of csPCa but PSA-velocity (1.4 vs 1.2, p=.4) was not. On multivariate analysis, PI-RADS score (p<.0001), age (p<.0001), AS exam (p<.0001), PSA (p<.0001) and PSAD (p<.0001) remained significant predictors, but PSAD-velocity was not (p=.1). Conclusions Higher PSAD-velocity was associated with presence of csPCa on univariate analysis but not on multivariate analysis when combined with other predictors such PI-RADS score, age, and PSAD.

Imaging is integral to detection and characterization of complications after prostate interventions, including vascular, infectious, urinary, and radiation-related complications, and facilitates timely diagnosis and management.

Abstract Background: Rectosigmoid endometriosis (RSE) presents with a diverse array of MRI findings that impact surgical planning. No standardized reporting and data system has been established for RSE. Purpose: We propose a novel MRI scoring system designed to predict the likelihood of muscularis propria (MP) involvement in RSE, which would, in turn, influence surgical planning. Material and Methods: The records of patients with bowel endometriosis treated surgically from May 2018 to June 2022 were retrieved. Surgery was classified as partial thickness discoid, full thickness discoid, or segmental resection. Each pre-treatment MRI was scored based on the mutual agreement of two abdominal radiologists (reference score). The MRI score was defined as (1) score 0: no evidence of RSE, (2) score 1: minimal tethering involving the serosal surface without MP involvement, (3) score 2: intermediate soft tissue thickening involving the rectosigmoid colon with indeterminate MP involvement, or (4) score 3: definite mushroom cap sign or definite MP involvement. In the reader study, two radiologists independently scored each exam. The area under the curve (AUC) was evaluated for predicting the need for segmental or full thickness discoid resection. Results: The cohort consisted of 95 patients (median age: 36 years); 16, 14, 30, and 35 patients had MRI score 0, 1, 2, and 3, respectively. Patients with MRI scores 3 and 2 underwent partial thickness discoid (6% vs 50%), full thickness discoid (6% vs 17%), and segmental resection (89% vs 33%), respectively. All patients with MRI scores 1 or 0 either underwent partial thickness discoid resection or did not undergo rectosigmoid surgery. The AUCs were 92.2%, 84.5% and 93.9% for MRI scores of the reference, reader 1, and 2, respectively. Conclusion: Our MRI scoring system based on suspected depth of bowel invasion showed good diagnostic performance to predict the type of surgical intervention needed. Summary Statement This novel MRI scoring system for rectosigmoid endometriosis is a concise and qualitative scale based on suspected depth of bowel wall invasion to best guide operative planning.

Objectives: The aims of the study are to develop a prostate cancer risk prediction model that combines clinical and magnetic resonance imaging (MRI)–related findings and to assess the impact of adding Prostate Imaging-Reporting and Data System (PI-RADS) ≥3 lesions-level findings on its diagnostic performance. Methods: This 3-center retrospective study included prostate MRI examinations performed with clinical suspicion of clinically significant prostate cancer (csPCa) between 2018 and 2022. Pathological diagnosis within 1 year after the MRI was used to diagnose csPCa. Seven clinical, 3 patient-level MRI-related, and 4 lesion-level MRI-related findings were extracted. After feature selection, 2 logistic regression models with and without lesions-level findings were created using data from facility I and II (development cohort). The area under the receiver operating characteristic curve (AUC) between the 2 models was compared in the PI-RADS ≥3 population in the development cohort and Facility III (validation cohort) using the Delong test. Interfacility differences of the selected predictive variables were evaluated using the Kruskal-Wallis test or chi-squared test. Results: Selected lesion-level features included the peripheral zone involvement and apparent diffusion coefficient (ADC) values. The model with lesions-level findings had significantly higher AUC than the model without in 655 examinations in the development cohort (0.81 vs 0.79, respectively, P = 0.005), but not in 553 examinations in the validation cohort (0.77 vs 0.76, respectively). Large interfacility differences were seen in the ADC distribution (P < 0.001) and csPCa proportion in PI-RADS 3–5 (P < 0.001). Conclusions: Adding lesions-level findings improved the csPCa discrimination in the development but not the validation cohort. Interfacility differences impeded model generalization, including the distribution of reported ADC values and PI-RADS score-level csPCa proportion.

Multifaceted risk stratification is required for management of upper tract urothelial carcinoma, and imaging plays an important integrated role in diagnosis and management.

The performance of a deep learning model was not different from that of radiologists in the detection of clinically significant prostate cancer at MRI, and gradient-weighted class activation maps localized the tumor.

AbstractBackground and ObjectivesWe assessed the accuracy of preoperative gallium‐68 DOTA‐Tyr3‐octreotate (DOTATATE) positron emission tomography (PET) imaging in estimating multifocality and nodal metastases of small bowel neuroendocrine tumors (sbNETs).MethodsA multicenter analysis was performed on patients with sbNETs who underwent preoperative DOTATATE PET imaging and surgical resection, with manual palpation of the entire length of the small bowel, between January 2016 and August 2022. Preoperative imaging reports and blinded secondary imaging reviews were compared to the final postoperative pathology reports. Descriptive statistics were applied.ResultsOne‐hundred and four patients met inclusion criteria. Pathology showed 53 (51%) patients had multifocal sbNETs and 96 (92%) had nodal metastases. The original preoperative DOTATATE PET imaging identified multifocal sbNET in 28 (27%) patients and lymph node (LN) metastases in 80 (77%) patients. Based on original radiology reports, sensitivity for multifocal sbNET identification was 45%, specificity was 92%, positive predictive value (PPV) was 86%, and negative predictive value (NPV) was 62%. For the identification of LN metastases, sensitivity was 82%, specificity was 88%, PPV was 99%, and NPV was 29%.ConclusionsAlthough DOTATATE PET imaging is specific and relatively accurate, sensitivity and NPV are insufficient to guide surgical planning. Preoperative use should not replace open palpation to identify additional synchronous lesions or to omit regional lymphadenectomy.