As a highly heterogeneous tumor, radiotherapy for Glioblastoma (GBM) is a complex and dynamic process. Traditional predictive methods of treatment response often rely on one or a few fixed time points, but this static approach may fail to capture the detailed, individualized changes occurring throughout the treatment process. To address these limitations, we proposed a novel approach called Evolutionary Heterogeneity Analysis Framework (EvoHAF), which integrates tumor heterogeneity and whole-process evolution of GBM radiotherapy. Our framework introduces an Image Heterogeneity Encoder, designed to capture the intricate spatial heterogeneity based on tumor subregions. Additionally, the Temporal Self-Attention Module (TSAM) mechanism integrates longitudinal imaging data throughout the course of radiotherapy, capturing the evolving nature of the tumor. We further introduce a Compensated Prediction Head (CPH) that dynamically refines predictions throughout the patient's radiotherapy. Experimental results on a cross-center cohort, including an internal dataset of 112 patients and an external validation dataset of 80 patients, demonstrate that EvoHAF achieves strong performance. For internal 5-fold validation, the AUC was 0.8519±0.0583, and for external validation, the AUC was 0.7675±0.0858. These results demonstrate the model's capability to provide accurate whole-process predictions. Moreover, the model's credibility is reinforced by providing visual explanations at both 2D and 3D subregional levels, establishing trust in its decisions and laying a strong foundation for clinical applications.
Zheng et al. (Thu,) studied this question.