Predicting response to patients with gastric cancer via dynamic-aware model with longitudinal liquid biopsy data

Gastric cancer (GC) presents challenges in predicting treatment responses due to patient-specific heterogeneity. Recently, liquid biopsies have emerged as a valuable data modality, providing essential cellular and molecular insights and facilitating the capture of time-sensitive information. This study aimed to harness artificial intelligence (AI) technology to analyze longitudinal liquid biopsy data. We collected a dataset from longitudinal liquid biopsies of 91 patients at Peking Cancer Hospital, spanning from July 2019 to April 2022, including 1,895 tumor-related cellular images and 1,698 tumor marker indices. Subsequently, we introduced a Dynamic-Aware Model (DAM) to predict GC treatment responses. DAM incorporates dynamic data through AI components for in-depth longitudinal analysis. Using three-fold cross-validation, DAM exhibited superior performance in predicting treatment responses compared to traditional methods (AUCs: 0.807 vs. 0.582), maintained stable efficacy in the test set (AUC: 0.802), and accurately predicted responses from early treatment data. Moreover, DAM's visual analysis of attention mechanisms identified six key visual features associated strongly with treatment responses. These findings represent a pioneering effort in applying AI technology for interpreting longitudinal liquid biopsy data and employ visual analytics in GC, offering a promising avenue toward precise response prediction and tailored treatment strategies for patients with GC.