Publications by authors named "C Badve"
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
- Glioblastoma (GBM) is an aggressive brain tumor that often infiltrates beyond its visible boundaries, making treatment challenging with standard surgical and chemoradiotherapy approaches.
- A new method was developed that combines expert insights and data augmentation to improve predictions of tumor infiltration using preoperative magnetic resonance imaging (mpMRI) scans from 229 patients.
- The model was validated through cross-institutional tests, showing varying effectiveness in predicting tumor recurrence, with odds ratios indicating strong potential for guiding targeted treatment strategies.
Article Synopsis
- The study aimed to explore sex-based differences in patients with glioblastoma to enhance personalized treatment and improve outcomes, focusing on differences in tumor parameters and survival.
- Data from 1832 patients was analyzed, revealing that women were diagnosed at an older median age and had lower tumor volumes compared to men, who generally had higher performance scores.
- Despite these differences in tumor characteristics, the research found no significant discrepancies in survival outcomes or mortality rates between sexes, although certain factors like age and treatment type influenced mortality risk for both genders.
Purpose: Quantitative MRI techniques such as MR fingerprinting (MRF) promise more objective and comparable measurements of tissue properties at the point-of-care than weighted imaging. However, few direct cross-modal comparisons of MRF's repeatability and reproducibility versus weighted acquisitions have been performed. This work proposes a novel fully automated pipeline for quantitatively comparing cross-modal imaging performance in vivo via atlas-based sampling.
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