Uncovering Neuroimaging Biomarkers of Brain Tumor Surgery with AI-Driven Methods

TL;DR

This study introduces an explainable AI framework using neuroimaging data to predict brain tumor surgery outcomes, identifying key biomarkers and improving model interpretability for better clinical decision-making.

Contribution

The paper presents a novel global explanation optimizer for deep learning models, enhancing the reliability and interpretability of survival predictions from neuroimaging data.

Findings

Alterations in cognitive and sensory regions influence survival.

The optimizer improves explanation fidelity and comprehensibility.

Neuroimaging biomarkers linked to long-term outcomes.

Abstract

Brain tumor resection is a highly complex procedure with profound implications for survival and quality of life. Predicting patient outcomes is crucial to guide clinicians in balancing oncological control with preservation of neurological function. However, building reliable prediction models is severely limited by the rarity of curated datasets that include both pre- and post-surgery imaging, given the clinical, logistical and ethical challenges of collecting such data. In this study, we develop a novel framework that integrates explainable artificial intelligence (XAI) with neuroimaging-based feature engineering for survival assessment in brain tumor patients. We curated structural MRI data from 49 patients scanned pre- and post-surgery, providing a rare resource for identifying survival-related biomarkers. A key methodological contribution is the development of a global explanation optimizer, which refines survival-related feature attribution in deep learning models, thereby improving both the interpretability and reliability of predictions. From a clinical perspective, our findings provide important evidence that survival after oncological surgery is influenced by alterations in regions related to cognitive and sensory functions. These results highlight the importance of preserving areas involved in decision-making and emotional regulation to improve long-term outcomes. From a technical perspective, the proposed optimizer advances beyond state-of-the-art XAI methods by enhancing both the fidelity and comprehensibility of model explanations, thus reinforcing trust in the recognition patterns driving survival prediction. This work demonstrates the utility of XAI-driven neuroimaging analysis in identifying survival-related variability and underscores its potential to inform precision medicine strategies in brain tumor treatment.