Reproducible and Interpretable Machine Learning-Based Radiomic Analysis for Overall Survival Prediction in Glioblastoma Multiforme.

Purpose: To develop and validate an MRI-based radiomic model for predicting overall survival (OS) in patients diagnosed with glioblastoma multiforme (GBM), utilizing a retrospective dataset from multiple institutions.

Materials And Methods: Pre-treatment MRI images of 289 GBM patients were collected. From each patient's tumor volume, 660 radiomic features (RFs) were extracted and subjected to robustness analysis.

[Awareness and Expectations of Primary Care Clinicians in Chronic Heart Failure Management].

Objective: Chronic heart failure (CHF) management requires a multidisciplinary approach, and it's very important for primary care physicians (PCC) to cooperate with cardiology physicians in this process. In this study, we tried to reveal the awareness and expectations of PCC about CHF management.

Methods: The study was designed as a descriptive survey in a single region and included 549 PCC.

From Data to Insights: Machine Learning Empowers Prognostic Biomarker Prediction in Autism.

Autism Spectrum Disorder (ASD) poses significant challenges to society and science due to its impact on communication, social interaction, and repetitive behavior patterns in affected children. The Autism and Developmental Disabilities Monitoring (ADDM) Network continuously monitors ASD prevalence and characteristics. In 2020, ASD prevalence was estimated at 1 in 36 children, with higher rates than previous estimates.

RFS+: A Clinically Adaptable and Computationally Efficient Strategy for Enhanced Brain Tumor Segmentation.

Automated brain tumor segmentation has significant importance, especially for disease diagnosis and treatment planning. The study utilizes a range of MRI modalities, namely T1-weighted (T1), T1-contrast-enhanced (T1ce), T2-weighted (T2), and fluid-attenuated inversion recovery (FLAIR), with each providing unique and vital information for accurate tumor localization. While state-of-the-art models perform well on standardized datasets like the BraTS dataset, their suitability in diverse clinical settings (matrix size, slice thickness, manufacturer-related differences such as repetition time, and echo time) remains a subject of debate.

Targeting ribosome biogenesis reinforces ERK-dependent senescence in pancreatic cancer.

Pancreatic adenocarcinomas (PDAC) often possess mutations in K-Ras that stimulate the ERK pathway. Aberrantly high ERK activation triggers oncogene-induced senescence, which halts tumor progression. Here we report that low-grade pancreatic intraepithelial neoplasia displays very high levels of phospho-ERK consistent with a senescence response.