Uncovering Neuroimaging Biomarkers of Brain Tumor Surgery with AI-Driven Methods
Journal:
arXiv
Published Date:
Jul 7, 2025
Abstract
Brain tumor resection is a complex procedure with significant implications
for patient survival and quality of life. Predictions of patient outcomes
provide clinicians and patients the opportunity to select the most suitable
onco-functional balance. In this study, global features derived from structural
magnetic resonance imaging in a clinical dataset of 49 pre- and post-surgery
patients identified potential biomarkers associated with survival outcomes. We
propose a framework that integrates Explainable AI (XAI) with
neuroimaging-based feature engineering for survival assessment, offering
guidance for surgical decision-making. In this study, we introduce a global
explanation optimizer that refines survival-related feature attribution in deep
learning models, enhancing interpretability and reliability. Our findings
suggest that survival is influenced by alterations in regions associated with
cognitive and sensory functions, indicating the importance of preserving areas
involved in decision-making and emotional regulation during surgery to improve
outcomes. The global explanation optimizer improves both fidelity and
comprehensibility of explanations compared to state-of-the-art XAI methods. It
effectively identifies survival-related variability, underscoring its relevance
in precision medicine for brain tumor treatment.
