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]).

Medical mistrust and receptivity to lung cancer screening among African American and white American smokers.

Although medical mistrust is associated with lower cancer screening uptake among racial minorities, such as African Americans, potential impacts on cancer screening among White Americans are generally understudied. In this study, we examined links from medical mistrust to lung cancer screening among African American ( = 203) and White American ( = 201) smokers. Participants completed the Group-Based Medical Mistrust Scale and viewed a brief online educational module about lung cancer risks, etiology, and screening.

Utility of an Untargeted Metabolomics Approach Using a 2D GC-GC-MS Platform to Distinguish Relapsing and Progressive Multiple Sclerosis.

Multiple sclerosis (MS) is the most common inflammatory neurodegenerative disease of the central nervous system (CNS) in young adults and results in progressive neurological defects. The relapsing-remitting phenotype (RRMS) is the most common disease course in MS, which ultimately progresses to secondary progressive MS (SPMS), while primary progressive MS (PPMS) is a type of MS that worsens gradually over time without remissions. There is a gap in knowledge regarding whether the relapsing form can be distinguished from the progressive course, or healthy subjects (HS) based on an altered serum metabolite profile.

Differences in Patient-Reported Outcome Measures in Patients With Cancer Six Months Before Death.

Purpose: Patient-reported outcome measures (PROMs) provide a direct report of the patient's perspective, complementary to clinician assessment. Currently, understanding the real-time changes in PROM scores near the end of life remains limited. This study evaluated differences in mean PROM scores between patients with cancer within 6 months before death compared with surviving patients with cancer.

Performance of GPT-4 with Vision on Text- and Image-based ACR Diagnostic Radiology In-Training Examination Questions.

Background Recent advancements, including image processing capabilities, present new potential applications of large language models such as ChatGPT (OpenAI), a generative pretrained transformer, in radiology. However, baseline performance of ChatGPT in radiology-related tasks is understudied. Purpose To evaluate the performance of GPT-4 with vision (GPT-4V) on radiology in-training examination questions, including those with images, to gauge the model's baseline knowledge in radiology.

Association of Social Determinants with Patient-Reported Outcomes in Patients with Cancer.

Patient-reported outcome (PRO) scores have been utilized more frequently, but the relationship of PRO scores to determinants of health and social inequities has not been widely studied. Our goal was to determine the association of PRO scores with social determinants. All patients with a new cancer diagnosis who completed a PRO survey from 2020 to 2022 were included.

Utility of an untargeted metabolomics approach using a 2D GC-GC-MS platform to distinguish relapsing and progressive multiple sclerosis.

Introduction: Multiple sclerosis (MS) is the most common inflammatory neurodegenerative disease of the central nervous system (CNS) in young adults and results in progressive neurological defects. The relapsing-remitting phenotype (RRMS) is the most common disease course in MS and may progress to the progressive form (PPMS).

Objectives: There is a gap in knowledge regarding whether the relapsing form can be distinguished from the progressive course or healthy subjects (HS) based on an altered serum metabolite profile.

Blood-based targeted metabolipidomics reveals altered omega fatty acid-derived lipid mediators in relapsing-remitting multiple sclerosis patients.

Unresolved and uncontrolled inflammation is considered a hallmark of pathogenesis in chronic inflammatory diseases like multiple sclerosis (MS), suggesting a defective resolution process. Inflammatory resolution is an active process partially mediated by endogenous metabolites of dietary polyunsaturated fatty acids (PUFA), collectively termed specialized pro-resolving lipid mediators (SPMs). Altered levels of resolution mediators have been reported in several inflammatory diseases and may partly explain impaired inflammatory resolution.

Intermittent fasting induced ketogenesis inhibits mouse epithelial ovarian cancer by promoting antitumor T cell response.

In various cancer models, dietary interventions have been shown to inhibit tumor growth, improve anticancer drug efficacy, and enhance immunity, but no such evidence exists for epithelial ovarian cancer (EOC), the most lethal gynecologic cancer. The anticancer immune responses induced by 16-h intermittent fasting (IF) were studied in mice with EOC. IF consistently reduced metabolic growth factors and cytokines that stimulate tumor growth, creating a tumor-hostile environment.

System-based integrated metabolomics and microRNA analysis identifies potential molecular alterations in human X-linked cerebral adrenoleukodystrophy brain.

X-linked adrenoleukodystrophy is a severe demyelinating neurodegenerative disease mainly affecting males. The severe cerebral adrenoleukodystrophy (cALD) phenotype has a poor prognosis and underlying mechanism of onset and progression of neuropathology remains poorly understood. In this study we aim to integrate metabolomic and microRNA (miRNA) datasets to identify variances associated with cALD.

Association of partial T2-FLAIR mismatch sign and isocitrate dehydrogenase mutation in WHO grade 4 gliomas: results from the ReSPOND consortium.

Purpose: While the T2-FLAIR mismatch sign is highly specific for isocitrate dehydrogenase (IDH)-mutant, 1p/19q-noncodeleted astrocytomas among lower-grade gliomas, its utility in WHO grade 4 gliomas is not well-studied. We derived the partial T2-FLAIR mismatch sign as an imaging biomarker for IDH mutation in WHO grade 4 gliomas.

Methods: Preoperative MRI scans of adult WHO grade 4 glioma patients (n = 2165) from the multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium were analyzed.

Intermittent Fasting induced ketogenesis inhibits mouse epithelial ovarian tumors by promoting anti-tumor T cell response.

Epithelial Ovarian Cancer (EOC) is the most lethal gynecologic cancer with limited genetic alterations identified that can be therapeutically targeted. In tumor bearing mice, short-term fasting, fasting mimicking diet and calorie restriction enhance the activity of antineoplastic treatment by modulating systemic metabolism and boosting anti-tumor immunity. We tested the outcome of sixteen-hour intermittent fasting (IF) on mouse EOC progression with focus on fasting driven antitumor immune responses.

MicroRNA and metabolomics signatures for adrenomyeloneuropathy disease severity.

Adrenomyeloneuropathy (AMN), the slow progressive phenotype of adrenoleukodystrophy (ALD), has no clinical plasma biomarker for disease progression. This feasibility study aimed to determine whether metabolomics and micro-RNA in blood plasma provide a potential source of biomarkers for AMN disease severity. Metabolomics and RNA-seq were performed on AMN and healthy human blood plasma.

SARS-COV-ATE risk assessment model for arterial thromboembolism in COVID-19.

Patients with SARS-CoV-2 infection are at an increased risk of cardiovascular and thrombotic complications conferring an extremely poor prognosis. COVID-19 infection is known to be an independent risk factor for acute ischemic stroke and myocardial infarction (MI). We developed a risk assessment model (RAM) to stratify hospitalized COVID-19 patients for arterial thromboembolism (ATE).

3D-PAST: Risk Assessment Model for Predicting Venous Thromboembolism in COVID-19.

Hypercoagulability is a recognized feature in SARS-CoV-2 infection. There exists a need for a dedicated risk assessment model (RAM) that can risk-stratify hospitalized COVID-19 patients for venous thromboembolism (VTE) and guide anticoagulation. We aimed to build a simple clinical model to predict VTE in COVID-19 patients.

Blood-based untargeted metabolomics in relapsing-remitting multiple sclerosis revealed the testable therapeutic target.

Metabolic aberrations impact the pathogenesis of multiple sclerosis (MS) and possibly can provide clues for new treatment strategies. Using untargeted metabolomics, we measured serum metabolites from 35 patients with relapsing-remitting multiple sclerosis (RRMS) and 14 healthy age-matched controls. Of 632 known metabolites detected, 60 were significantly altered in RRMS.

Glioma progression is shaped by genetic evolution and microenvironment interactions.

The factors driving therapy resistance in diffuse glioma remain poorly understood. To identify treatment-associated cellular and genetic changes, we analyzed RNA and/or DNA sequencing data from the temporally separated tumor pairs of 304 adult patients with isocitrate dehydrogenase (IDH)-wild-type and IDH-mutant glioma. Tumors recurred in distinct manners that were dependent on IDH mutation status and attributable to changes in histological feature composition, somatic alterations, and microenvironment interactions.