Predicting PD-L1 expression in advanced EGFR-mutant lung adenocarcinoma patients using NECT, CECT radiomics and clinical features

Recent studies have applied machine-learning radiomics to predict EGFR mutations or PD-L1 expression, yet little is known about PD-L1 prediction specifically in EGFR-mutant lung adenocarcinoma, despite evidence linking high PD-L1 expression to TKI resistance. To address this gap, this study integrates CT radiomics features with clinical data to develop and validate interpretable models for predicting PD-L1 expression in advanced EGFR-mutant LUAD and identifying key predictive determinants. In this single-center study, we retrospectively collected the pretreatment CT images, including non-contrast enhanced CT (NECT) and contrast enhanced CT (CECT), as well as clinical information from a cohort consisting of 165 EGFR-Mutant LUAD patients consecutively staged at IIIB-IVB between January 2020 and May 2022. After feature extraction and selection, six machine learning algorithms (Logistic regression, Linear discriminate analysis, Supporter vector machine, Gradient boosting, Random forest and adaptive boosting) were employed to construct the models. Model performances were assessed using 5-fold cross-validation and then visualized using coefficient matrices and SHAP (SHapley Additive exPlanations) to interpret the models’ predictions. A total of 30 radiomics features and 4 clinical features were selected. Models using radiomics features alone achieved the highest area under the curve (AUC) value of 0.91, and the combination of radiomics and clinical features models showed similar predictive performance with an AUC value of 0.90. However, the clinical features models exhibited insufficient predictive ability and poor generalizability (AUC = 0.64). According to the coefficient matrices, the top 5 features associated with PD-L1 ≥ 1% were: cect-wavelet-LHH-gldm-DependenceVariance, nect-wavelet-LHL-glcm-MCC, nect-wavelet-LHL-glrlm-LRLGLE, cect-wavelet-LLH-glcm-Imc2 and cect-wavelet-LLH-firstorder-Skewness, which was consistent with the results form SHAP. Our study demonstrated that radiomics and clinical features via machine learning method offer a noninvasive approach to predict the PD-L1 expression status in EGFR-mutant LUAD patients. The model shows potential as a noninvasive tool to support immunotherapy decision-making and lays the groundwork for future clinical validation.