To explore the performance of an interpretable machine learning model for predicting immunotherapy responsiveness in lung cancer based on serum cytokines.

A total of 84 lung cancer patients receiving immunotherapy at our hospital from January 2022 to December 2025 were enrolled. Among them, 58 responders were assigned to the observation group and 26 nonresponders to the control group. Clinical data were collected and compared between the two groups. Least absolute shrinkage and selection operator (LASSO) regression was used to screen core predictive features. Four machine learning models, namely logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), were constructed. The discriminative performance of the models was evaluated by the area under the receiver operating characteristic curve (AUC). Shapley additive explanations (SHAP) was applied for clinical interpretability analysis of the optimal model.

Compared with the control group, the observation group showed in age [ (59.33±9.12)years vs. (57.82±7.93)years, P=0.445], proportion of smokers (34.48% vs. 61.54%, P=0.021), and ECOG performance status score (score 0: 67.24% vs. 38.46%; score 1: 32.76% vs. 61.54%, P=0.013). Baseline serum levels of interleukin (IL)-6 [(46.78±10.32)pg/ml vs. (56.47±11.23)pg/ml, P=0.001], IL-10[(5.20±1.64) pg/ml vs. (6.48±1.56) pg/ml, P=0.001], tumor necrosis factor α (TNF-α) [(12.33±3.00) pg/ml vs. (14.22±3.34) pg/ml, P=0.017], IL-1β [(11.28±3.21) pg/ml vs. (14.75±2.89) pg/ml, P<0.001], as well as carcinoembryonic antigen (CEA) [(20.53±6.36) ng/ml vs. (25.98±7.15)ng/ml, P=0.002], squamous cell carcinoma antigen (SCC) [(1.65±0.42) ng/ml vs. (1.89±0.51) ng/ml, P=0.042], and cytokeratin 19 fragment (CYFRA21-1) [(7.33±2.15) ng/ml vs. (8.56±2.34)ng/ml, P=0.027] were significantly lower in the observation group than in the control group. No statistically significant difference was observed in IL-8 between the two groups [(7.67±2.10) pg/ml vs. (8.48±2.37) pg/ml, P=0.141]. LASSO screening identified five predictive factors: IL-1β, IL-8, IL-6, TNF-α, and ECOG score. Among the four machine learning models, XGBoost performed best, with an AUC of 0.956, accuracy of 91.2%, recall of 89.0%, precision of 90.5%, and F1 score of 0.896. SHAP analysis revealed that the order of feature contribution was IL-1β, IL-8, IL-6, TNF-α, and ECOG score.

The XGBoost machine learning model based on serum cytokines can efficiently predict immunotherapy responsiveness in lung cancer patients. The SHAP method clarifies the contribution of key features, providing clinical significance.