Machine Learning-based Prognostic Subgrouping of Glioblastoma: A Multi-center Study.

Background: Glioblastoma is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification.

Methods: We developed a highly reproducible, personalized prognostication and clinical subgrouping system using machine learning (ML) on routine clinical data, MRI, and molecular measures from 2,838 demographically diverse patients across 22 institutions and 3 continents. Patients were stratified into favorable, intermediate, and poor prognostic subgroups (I, II, III) using Kaplan-Meier analysis (Cox proportional model and hazard ratios [HR]).

Troponin I levels in systemic sclerosis patients with myocardial involvement.

Objectives: Troponin I has been suggested as a more specific diagnostic biomarker for myocardial involvement in systemic sclerosis than the frequently used troponin T. The aim of this study is to evaluate the additive value of troponin I to detect myocardial involvement in systemic sclerosis. To this end, we evaluated the association between troponin I levels and myocardial involvement in systemic sclerosis patients.

Epicardial adipose tissue in patients with systemic sclerosis.

Aims: Epicardial adipose tissue (EAT) has emerged as a mediator between systemic inflammatory disorders and cardiovascular disease, and may therefore play a role in the pathophysiology of cardiac involvement in systemic sclerosis (SSc). The aim of this study was to assess the correlation between EAT and left ventricular (LV) function, and to determine the prognostic value of EAT in patients with SSc.

Methods And Results: Consecutive patients with SSc who underwent non-contrast thorax computed tomography and echocardiography were included.