Deep Learning Based on Computed Tomography Predicts Response to Chemoimmunotherapy in Lung Squamous Cell Carcinoma

Non-small-cell lung carcinoma (NSCLC) often carries a dire prognosis. The advent of neoadjuvant chemoimmunotherapy (NCI) has become a promising approach in NSCLC treatment, making the identification of reliable biomarkers for major pathological response (MPR) crucial. This study aimed to devise a deep learning (DL) model to estimate the MPR to NCI in lung squamous cell carcinoma (LUSC) patients and uncover its biological mechanism. We enrolled a cohort of 309 LUSC patients from various medical institutions. A ResNet50 model, trained on contrast-enhanced computed tomography images, was developed, and validated to predict MPR. We examined somatic mutations, genomic data, tumor-infiltrating immune cells, and intra-tumor microorganisms. Post-treatment, 149 (48.22%) patients exhibited MPR. The DL model demonstrated excellent predictive accuracy, evidenced by an area under the receiver operating characteristic curve (AUC) of 0.95 (95% CI: 0.98-1.00) and 0.90 (95% CI: 0.81-0.98) in the first and second validation sets, respectively. Multivariate logistic regression analysis identified the DL model score (low vs. high) as an independent predictor of MPR. The prediction of MPR (P-MPR) correlated with mutations in four genes, as well as gene ontology and pathways tied to immune response and antigen processing and presentation. Analysis also highlighted diversity in immune cells within the tumor microenvironment and in peripheral blood. Moreover, the presence of four distinct bacteria varied among intra-tumor microorganisms. Our DL model proved highly effective in predicting MPR in LUSC patients undergoing NCI, significantly advancing our understanding of the biological mechanisms involved.