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

2025-07-07

AI Summary

This study addresses the challenges of imprecise biomarker identification and limited model interpretability in predicting postoperative prognosis for brain tumor surgery. We propose a deep learning framework integrating explainable artificial intelligence (XAI) with multi-scale neuroimaging feature engineering. Methodologically, we incorporate structural MRI (sMRI), deep learning modeling, and neuroanatomically informed feature enhancement guided by anatomical priors. Our key contribution is the design of a Global Explanation Optimizer (GEO), which significantly improves the fidelity and clinical interpretability of survival-associated imaging feature attributions. Furthermore, we provide the first quantitative evidence demonstrating the critical impact of preserving sensorimotor and cognitive functional regions on postoperative survival. Validation on a cohort of 49 patients shows a 0.12 improvement in C-index and a 37% increase in accuracy for localizing prognostic brain regions. The framework delivers interpretable, verifiable imaging biomarkers to support personalized surgical planning.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Identifying neuroimaging biomarkers for brain tumor surgery outcomes
• Developing AI-driven methods to predict patient survival post-surgery
• Enhancing interpretability of survival-related features in surgical decision-making

Innovation

• AI-driven neuroimaging biomarkers for tumor surgery
• Explainable AI with feature engineering integration
• Global explanation optimizer enhances model interpretability