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]).

Patient-derived glioblastoma organoids as real-time avatars for assessing responses to clinical CAR-T cell therapy.

Patient-derived tumor organoids have been leveraged for disease modeling and preclinical studies but rarely applied in real time to aid with interpretation of patient treatment responses in clinics. We recently demonstrated early efficacy signals in a first-in-human, phase 1 study of dual-targeting chimeric antigen receptor (CAR)-T cells (EGFR-IL13Rα2 CAR-T cells) in patients with recurrent glioblastoma. Here, we analyzed six sets of patient-derived glioblastoma organoids (GBOs) treated concurrently with the same autologous CAR-T cell products as patients in our phase 1 study.

Family-Focused Prevention and Early Intervention of Substance Use in Pediatric Primary Care Settings.

Prevention of substance misuse and substance use disorders is a national public health priority. The home environment can represent risk or protective factors for development of substance misuse. Children in homes with caregiver substance use are biologically, developmentally, interpersonally, and environmentally vulnerable to substance misuse and associated consequences, making it necessary for substance use prevention to focus on families early.

Re-Irradiation Plus Pembrolizumab: A Phase II Study for Patients with Recurrent Glioblastoma.

Purpose: Radiotherapy may enhance antitumor immune responses by several mechanisms, including induction of immunogenic cell death. We performed a phase 2 study of pembrolizumab with re-irradiation in patients with recurrent glioblastoma.

Patients And Methods: Sixty patients with recurrent glioblastoma received pembrolizumab with re-irradiation alone (cohort A, bevacizumab-naïve; n = 30) or with bevacizumab continuation (cohort B, n = 30).

Distinction of pseudoprogression from true progression in glioblastomas using machine learning based on multiparametric magnetic resonance imaging and O-methylguanine-methyltransferase promoter methylation status.

Background: It is imperative to differentiate true progression (TP) from pseudoprogression (PsP) in glioblastomas (GBMs). We sought to investigate the potential of physiologically sensitive quantitative parameters derived from diffusion and perfusion magnetic resonance imaging (MRI), and molecular signature combined with machine learning in distinguishing TP from PsP in GBMs in the present study.

Methods: GBM patients ( = 93) exhibiting contrast-enhancing lesions within 6 months after completion of standard treatment underwent 3T MRI.