Multi-task Deep Learning Based on Longitudinal CT Images Facilitates Prediction of Lymph Node Metastasis and Survival in Chemotherapy-Treated Gastric Cancer.

Abstract

Accurate preoperative assessment of lymph node metastasis (LNM) and overall survival (OS) status is essential for patients with locally advanced gastric cancer (LAGC) receiving neoadjuvant chemotherapy (NAC), providing timely guidance for clinical decision-making. However, current approaches to evaluate LNM and OS have limited accuracy. In this study, we used longitudinal CT images from 1,021 LAGC patients to develop and validate a multi-task deep learning model named co-attention tri-oriented spatial Mamba (CTSMamba) to simultaneously predict LNM and OS. CTSMamba was trained and validated on 398 patients, and the performance was further validated on 623 patients at two additional centers. Notably, CTSMamba exhibited significantly more robust performance than a clinical model in predicting LNM across all of the cohorts. Additionally, integrating CTSMamba survival scores with clinical predictors further improved personalized OS prediction. These results support the potential of CTSMamba to accurately predict LNM and OS from longitudinal images, potentially providing clinicians with a tool to inform individualized treatment approaches and optimized prognostic strategies.