Machine learning algorithms integrating positron emission tomography/computed tomography features to predict pathological complete response after neoadjuvant chemoimmunotherapy in lung cancer

Abstract OBJECTIVES Reliable methods for predicting pathological complete response (pCR) in non-small cell lung cancer (NSCLC) patients undergoing neoadjuvant chemoimmunotherapy are still under exploration. Although Fluorine-18 fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG PET/CT) features reflect tumour response, their utility in predicting pCR remains controversial. METHODS This retrospective analysis included NSCLC patients who received neoadjuvant chemoimmunotherapy followed by 18F-FDG PET/CT imaging at Shanghai Pulmonary Hospital from October 2019 to August 2024. Eligible patients were randomly divided into training and validation cohort at a 7:3 ratio. Relevant 18F-FDG PET/CT features were evaluated as individual predictors and incorporated into five machine learning (ML) models. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), and Shapley additive explanation (SHAP) was applied for model interpretation. RESULTS A total of 205 patients were included, with 91 (44.4%) achieving pCR. Post-treatment tumour maximum standardized uptake value (SUVmax) demonstrated the highest predictive performance among individual predictors, achieving an AUC of 0.72 (95% CI: 0.65—0.79), while ΔT SUVmax achieved an AUC of 0.65 (95% CI: 0.53—0.77). The Light Gradient Boosting Machine (LightGBM) algorithm outperformed other models and individual predictors, achieving an average AUC of 0.87 (95% CI: 0.78—0.97) in training cohort and 0.83 (95% CI: 0.72—0.94) in validation cohort. SHAP analysis identified post-treatment tumour SUVmax and post-treatment nodal volume as key contributors. CONCLUSIONS This ML models offer a non-invasive and effective approach for predicting pCR after neoadjuvant chemoimmunotherapy in NSCLC.