Differentiation between renal cell carcinoma metastases to the pancreas and pancreatic neuroendocrine tumors in patients with renal cell carcinoma on CT or MRI.

Purpose: To determine whether renal cell carcinoma metastases (RCC-Mets) to the pancreas can be differentiated from pancreatic neuroendocrine tumors (PNETs) in patients with RCC on CT or MRI at presentation.

Methods: This retrospective study included patients with biopsy-proven RCC-Mets (n = 102) or PNETs (n = 32) at diagnosis or after nephrectomy for RCC. Inter-observer agreement (Cohen kappa) was assessed in 95 patients with independent reads by two radiologists, with discrepancies resolved by consensus for final analysis.

Large-scale multi-center CT and MRI segmentation of pancreas with deep learning.

Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective study, we collected a large dataset (767 scans from 499 participants) of T1-weighted (T1 W) and T2-weighted (T2 W) abdominal MRI series from five centers between March 2004 and November 2022.

Identifying Local Facilitators and Barriers to Screening Mammography Within a Rural Acute Care Hospital Service Area.

Introduction: Women living in rural areas are more likely to be diagnosed with advanced-stage breast cancer than their urban counterparts. The advanced stage at diagnosis is potentially attributable to lower rates of mammogram screening. We aimed to elucidate factors affecting women in decision-making about mammogram screening in a rural area in Wisconsin served by a critical access hospital.

Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI.

Background Multiparametric MRI can help identify clinically significant prostate cancer (csPCa) (Gleason score ≥7) but is limited by reader experience and interobserver variability. In contrast, deep learning (DL) produces deterministic outputs. Purpose To develop a DL model to predict the presence of csPCa by using patient-level labels without information about tumor location and to compare its performance with that of radiologists.

Imaging cholangiocarcinoma: CT and MRI techniques.

Cross-sectional imaging plays a crucial role in the detection, diagnosis, staging, and resectability assessment of intra- and extrahepatic cholangiocarcinoma. Despite this vital function, there is a lack of standardized CT and MRI protocol recommendations for imaging cholangiocarcinoma, with substantial differences in image acquisition across institutions and vendor platforms. In this review, we present standardized strategies for the optimal imaging assessment of cholangiocarcinoma including contrast media considerations, patient preparation recommendations, optimal contrast timing, and representative CT and MRI protocols with individual sequence optimization recommendations.