Bridging the clinical gap: Confidence informed IDH prediction in brain gliomas using MRI and deep learning.

ABSTRACT: The isocitrate dehydrogenase (IDH) mutation status is a key molecular marker in diagnosing and treating brain tumors. Currently, it is determined via invasive tissue biopsy. Recent advances in deep learning (DL) have offered promising non-invasive alternatives for determining IDH status. However, their clinical translation is hindered by a significant gap between DL predictions and their clinical applicability. The limited transparency of many DL-networks and inadequate evaluation metrics hinders trust and adoption, as clinicians require clear and validated insights for determining IDH status. These challenges highlight the need for robust validation and measures of predictive reliability to make DL-predictions clinically actionable. We developed a unique approach for non-invasive predicti

SUBMITTER: Bangalore Yogananda CG 

PROVIDER: S-EPMC12365901 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature