The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers.

Background: Glioblastoma is a highly heterogeneous brain tumor, posing challenges for precision therapies and patient stratification in clinical trials. Understanding how genetic mutations influence tumor imaging may improve patient management and treatment outcomes. This study investigates the relationship between imaging features, spatial patterns of tumor location, and genetic alterations in IDH-wildtype glioblastoma, as well as the likely sequence of mutational events.

Quantified trends in provider reimbursement and utilization volume in colorectal cancer screenings: analysis of Medicare claims from 2000 to 2019.

Aim: Colorectal cancer (CRC) screening rates in the United States remain persistently below guideline targets, partly due to suboptimal patient utilization and provider reimbursement. To guide long-term national utilization estimates and set reasonable screening adherence targets, this study aimed to quantify trends in utilization of and reimbursement for CRC screenings using Medicare claims.

Method: Inflation-adjusted reimbursements and utilization volume associated with each CRC screening code were abstracted from Medicare claims between 2000 and 2019.

Automatic Segmentation and Quantification of Upper Airway Anatomic Risk Factors for Obstructive Sleep Apnea on Unprocessed Magnetic Resonance Images.

Rationale And Objectives: Accurate segmentation of the upper airway lumen and surrounding soft tissue anatomy, especially tongue fat, using magnetic resonance images is crucial for evaluating the role of anatomic risk factors in the pathogenesis of obstructive sleep apnea (OSA). We present a convolutional neural network to automatically segment and quantify upper airway structures that are known OSA risk factors from unprocessed magnetic resonance images.

Materials And Methods: Four datasets (n = [31, 35, 64, 76]) with T1-weighted scans and manually delineated labels of 10 regions of interest were used for model training and validations.