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

ABSTRACT:

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 thr

SUBMITTER: Chen Z 

PROVIDER: S-EPMC12378481 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature