Journal Article

An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer

Tang, Yongfang, Zhang, Purong, Chen, Yuanlin, Xia, Xiaohong, Huang, Jie, Xin, Xin, Yin, Jinxue, Zhong, Xi, Lu, Guoliang, Yao, Chun, Lai, Shuying, Chen, Xiangguang, Li, Jiansheng, Dai, Lihuan

2025

Overview

Background Programmed death ligand 1 (PD-L1) expression status, closely related to immunotherapy outcomes, is a reliable biomarker for screening patients who may benefit from immunotherapy. Here, we developed and validated an interpretable machine learning (ML) model based on contrast-enhanced computed tomography (CECT) radiomics for preoperatively predicting PD-L1 expression status in patients with gastric cancer (GC). Methods We retrospectively recruited 285 GC patients who underwent CECT and PD-L1 detection from two medical centers. A PD-L1 combined positive score (CPS) of ≥ 5 was considered to indicate a high PD-L1 expression status. Patients from center 1 were divided into training ( n  = 143) and validation sets ( n  = 62), and patients from center 2 were considered a test set ( n  = 80). Radiomics features were extracted from venous-phase CT images. After feature reduction and selection, 11 ML algorithms were employed to develop predictive models, and their performance in predicting PD-L1 expression status was evaluated using areas under receiver operating characteristic curves (AUCs). SHapley Additive exPlanations (SHAP) were used to interpret the optimal model and visualize the decision-making process for a single individual. Results Nine features significantly associated with PD-L1 expression status were ultimately selected to construct the predictive model. The light gradient-boosting machine (LGBM) model demonstrated the best performance for PD-L1 high expression status prediction in the training, validation, and test sets, with AUCs of 0.841(95% CI: 0.773, 0.908), 0.834 (95% CI:0.729, 0.939), and 0.822 (95% CI: 0.718, 0.926), respectively. The SHAP summary and bar plots illustrated that a feature’s value affected the feature’s impact attributed to the model. The SHAP waterfall plots were used to visualize the decision-making process for a single individual. Conclusion Our CT radiomics–based LGBM model may aid in preoperatively predicting PD-L1 expression status in GC patients, and the SHAP method may improve the interpretability of this model.

• Publisher: BioMed Central,BioMed Central Ltd,BMC
• Subject: Adult / Aged / Algorithms / B7-H1 Antigen - metabolism / Biomarkers, Tumor - metabolism / Cancer Research / Care and treatment / Computed tomography / CT imaging / Data mining / Diagnosis / Diagnostic imaging / Female / Gastric cancer / Health aspects / Humans / Imaging / Machine Learning / Male / Measurement / Medicine / Medicine & Public Health / Membrane proteins / Methods / Middle Aged / Neural networks / Nuclear Medicine / Oncology / Programmed death ligand 1 / Radiology / Radiomics / Retrospective Studies / SHapley additive explanations / Stomach cancer / Stomach Neoplasms - diagnostic imaging / Stomach Neoplasms - metabolism / Stomach Neoplasms - pathology / Tomography, X-Ray Computed - methods