Objective An artificial intelligence (AI) model based on deep learning algorithms was constructed using clinical data to evaluate the feasibility of predicting the efficacy of neoadjuvant immunotherapy combined with chemotherapy for non-small cell lung cancer (NSCLC).

Methods Clinical and pathological data of 132 patients with NSCLC who were diagnosed and treated with neoadjuvant immunotherapy combined with chemotherapy between January 2020 and January 2024 at Beijing Chest Hospital/Beijing Tuberculosis and Thoracic Tumor Research Institute were collected. Statistical analysis was conducted to identify the main factors affecting the efficacy of neoadjuvant immunotherapy combined with chemotherapy. Variables were selected based on statistical results and relevant literature, and a variable dataset was constructed. A deep learning model was established using a multi-layer perceptron (MLP) algorithm with 5-fold cross-validation, and the performance of the model was evaluated using receiver operating characteristic curve (ROC).

Results Among the 132 patients, univariate analysis demonstrated statistically significant differences in sex (P=0.020), smoking history (P=0.004), carcinoembryonic antigen (CEA) (P=0.038) and programmed death-ligand 1 (PD-L1) ≥1% (P=0.038) between the major pathological response (MPR) and non-MPR groups. Patients in the complete pathological response (pCR) group and non-pCR groups showed statistical differences in tumor size (P=0.007) and CEA levels (P=0.010). After 5-fold cross-validation, the average area under the curve (AUC) of the MPR prediction model in the validation and test sets was 0.72 and 0.71, respectively.

Conclusions The deep learning model can effectively predict the efficacy of neoadjuvant chemoimmunotherapy in patients with NSCLC.