Publications by authors named "R R Colen"
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]).
Article Synopsis
- Technological advancements are enhancing the use of computational methods in fields like health care, particularly in neuro-oncology, to improve clinical decision-making through various biomarkers.
- Artificial intelligence (AI) algorithms, including radiomics, are being increasingly integrated, but challenges like generalizability and validation hinder their widespread application.
- This Policy Review aims to provide recommendations for standardizing AI practices in health care, focusing on neuro-oncology, while discussing the importance of reliable AI for future clinical trials.
The development, application, and benchmarking of artificial intelligence (AI) tools to improve diagnosis, prognostication, and therapy in neuro-oncology are increasing at a rapid pace. This Policy Review provides an overview and critical assessment of the work to date in this field, focusing on diagnostic AI models of key genomic markers, predictive AI models of response before and after therapy, and differentiation of true disease progression from treatment-related changes, which is a considerable challenge based on current clinical care in neuro-oncology. Furthermore, promising future directions, including the use of AI for automated response assessment in neuro-oncology, are discussed.
View Article and Find Full Text PDFEvaluation of radiomics as a predictor of efficacy and the tumor immune microenvironment in anti-PD-1 mAb treated recurrent/metastatic squamous cell carcinoma of the head and neck patients.
Head Neck
January 2025
Article Synopsis
- The study assessed how radiomics—a method of extracting and analyzing features from medical images—can predict the tumor microenvironment (TME) and response to anti-PD-1 treatment in patients with recurrent/metastatic head and neck squamous cell carcinoma (HNSCC).
- Using advanced techniques like CT scans and machine learning algorithms, researchers built models to evaluate disease control rates, progression-free survival, and overall survival, alongside assessing tumor characteristics like hypoxia and immune cell presence.
- Findings indicated that radiomics could accurately predict treatment outcomes and TME features, suggesting its potential as a valuable tool, although more extensive research is needed to confirm these results.
Article Synopsis
- The study investigates the differences in treatment responses among melanoma patients based on tumor characteristics, utilizing radiomic analysis of medical images to identify non-invasive biomarkers.
- This research involved 291 patients treated with either immune checkpoint inhibitors or BRAF targeted therapy, and 667 tumor lesions were analyzed for treatment outcomes.
- The findings show significant organ-level differences in treatment response and variability, with specific machine-learning models accurately predicting disease control or progression based on radiomic features, highlighting the potential for personalized treatment strategies.