Exploring Strategies for Personalized Radiation Therapy Part I Unlocking Response-Related Tumor Subregions with Class Activation Mapping

2025-06-20

AI Summary

Conventional radiomics methods for predicting brain metastasis response to Gamma Knife radiosurgery—defined as >20% tumor volume reduction at 3-month follow-up—suffer from fixed region-of-interest selection and limited biological interpretability. Method: We propose a pixel-wise class activation map (CAM)-driven subregion localization framework that enables lesion-specific, spatially interpretable discrimination. The method integrates an ensemble autoencoder classifier, Grad-CAM visualization, and multimodal imaging modeling. Contribution/Results: Evaluated on 69 patients, our approach significantly outperforms conventional radiomics and deep learning baselines in prediction accuracy. It precisely identifies radioresistant intratumoral subregions and supports cross-scale validation with cellular-level data. By linking spatial response heterogeneity to underlying biology, the framework provides a spatially resolved, biologically grounded foundation for adaptive dose painting in personalized stereotactic radiosurgery.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Ensemble Methods
• Search and Optimization: Metareasoning and Metaheuristics

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
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Personalized precision radiation therapy requires more than simple classification, it demands the identification of prognostic, spatially informative features and the ability to adapt treatment based on individual response. This study compares three approaches for predicting treatment response: standard radiomics, gradient based features, and convolutional neural networks enhanced with Class Activation Mapping. We analyzed 69 brain metastases from 39 patients treated with Gamma Knife radiosurgery. An integrated autoencoder classifier model was used to predict whether tumor volume would shrink by more than 20 percent at a three months follow up, framed as a binary classification task. The results highlight their strength in hierarchical feature extraction and the classifiers discriminative capacity. Among the models, pixel wise CAM provides the most detailed spatial insight, identifying lesion specific regions rather than relying on fixed patterns, demonstrating strong generalization. In non responding lesions, the activated regions may indicate areas of radio resistance. Pixel wise CAM outperformed both radiomics and gradient based methods in classification accuracy. Moreover, its fine grained spatial features allow for alignment with cellular level data, supporting biological validation and deeper understanding of heterogeneous treatment responses. Although further validation is necessary, these findings underscore the promise in guiding personalized and adaptive radiotherapy strategies for both photon and particle therapies.

Problem

• Identifying tumor subregions for personalized radiation therapy
• Comparing methods to predict treatment response in brain metastases
• Enhancing spatial insight with Class Activation Mapping for radiotherapy

Innovation

• Class Activation Mapping enhances CNN for tumor analysis
• Pixel-wise CAM identifies radio-resistant tumor subregions
• Integrated autoencoder predicts treatment response accurately