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

作者：Zifan Chen, Jie Zhao, Yanyan Li, Xujiao Feng, Yang Chen, Yilin Li, Xinyu Nan, Hui Li, Bin Dong, Lin Shen, Li Zhang · 发表于：Gastric Cancer · 年份：2025 · DOI：10.1007/s10120-025-01628-4 · 被引用次数：3 · 研究领域：AI in cancer detection、Cell Image Analysis Techniques、Cancer Genomics and Diagnostics

BACKGROUND: Gastric cancer (GC) presents challenges in predicting treatment responses due to its patient-specific heterogeneity. Recently, liquid biopsies have emerged as a valuable data modality, offering essential cellular and molecular insights while facilitating the capture of time-sensitive information. This study aimed to leverage artificial intelligence (AI) technology to analyze longitudinal liquid biopsy data. METHODS: We collected a dataset from longitudinal liquid biopsies of 91 patients at Peking Cancer Hospital, spanning from July 2019 to April 2022. This dataset included 1895 tumor-related cellular images and 1698 tumor marker indices. Subsequently, we introduced the Dynamic-Aware Model (DAM) to predict responses to GC treatment. DAM incorporates dynamic data through AI-engineered components, facilitating an in-depth longitudinal analysis. RESULTS: Utilizing threefold cross-validation, DAM exhibited superior performance compared to traditional cell-counting methods, achieving an AUC of 0.807 in predicting GC treatment responses. In the test set, DAM maintained stable efficacy with an AUC of 0.802. Besides, DAM showed the capability to accurately predict treatment responses based on early treatment data. Moreover, DAM's visual analysis of attention mechanisms identified six dynamic visual features related to focus areas, which were strongly associated with treatment-response. CONCLUSIONS: These findings represent a pioneering effort in applying AI technology to i...