Predicting clinical prognosis in gastric cancer using deep learning-based analysis of tissue pathomics images

作者：Long Liu, Yu Zhang, Xiaohong Zhao, Min Zhu, Jiawei Liang, Zhenyu Jiang, Jin-qiu Zhang, Jiali Ma, Zhenzhen Wang, Meifu Gan, Xinli Mao, Shao-wei Li · 发表于：Computer Methods and Programs in Biomedicine · 年份：2025 · DOI：10.1016/j.cmpb.2025.108895 · 被引用次数：7 · 研究领域：Gastric Cancer Management and Outcomes、Radiomics and Machine Learning in Medical Imaging、Ferroptosis and cancer prognosis

OBJECTIVE: Evaluate the utility of a machine learning-based pathomics model in predicting overall survival (OS) post-surgery for gastric cancer patients. METHODS: A retrospective analysis of 160 gastric cancer patients undergoing radical surgery with continuous follow-up was conducted. Pathomic parameters of the primary lesion and surrounding area were assessed. Six machine learning methods were employed to develop an optimal pathomics model and calculate the pathomics score (Pathscore). The Pathscore was integrated with clinical parameters to construct a comprehensive nomogram for accurate prognosis prediction. The TCGA and GEO databases were utilized for model validation and underlying mechanism exploration. RESULTS: The combined GBM-based pathomics model exhibited superior predictive performance, with 1-, 3-, and 5-year area under the curve (AUC) values of 0.837, 0.970, and 0.963, respectively, in the training set. The pathomics model demonstrated significant incremental value for clinical predictors. The nomogram incorporating age, M stage, TNM stage, and Pathscore achieved even higher AUCs of 0.954, 0.939, and 0.898 for 1-, 3-, and 5-year OS prediction, respectively. Bioinformatics analysis suggested that the pathomics model could predict OS by reflecting tumor immune status and NRP1 expression. CONCLUSION: Machine learning-based pathomics models can effectively predict OS in gastric cancer patients post-surgery. The integration of pathomics and clinical parameters enhan...