Automated CT-derived body composition predicts pathologic response to neoadjuvant immunotherapy in non–small cell lung cancer

Tumor-intrinsic biomarkers alone insufficiently predict pathological complete response (pCR) to neoadjuvant immunochemotherapy (NICT) in non-small cell lung cancer (NSCLC). Artificial intelligence (AI)-based three-dimensional CT-derived body composition may provide complementary predictive value. We evaluated its association with pCR following NICT in NSCLC. This multicenter retrospective study of NSCLC patients treated with NICT in China between July 2019 and July 2024. Pre- and post-treatment CT scans were used for automated T1–T12 localization and volumetric body composition segmentation. Metrics included skeletal muscle, intermuscular, visceral, and subcutaneous adipose volume index (SAVI), and their percentage changes between scans. Among 657 patients (mean age, 61.3 years; 87.4 % men), pCR rates were 39.7 % (training), 38.4 % (internal validation), and 34.9 % (external validation). In multivariable analysis, high baseline skeletal muscle volume index (SMVI) was independently associated with pCR (OR = 2.22). During NICT, each 1 % relative increase in SMVI was associated with a 16 % higher likelihood of pCR (OR = 1.16), whereas every 10 % relative increase in SAVI improved pCR probability (OR = 1.56). A machine learning model integrating clinical variables, baseline SMVI, %?SMVI, and %?SAVI demonstrated significantly better discrimination than models using clinical variables alone ( p < 0.05) in all cohorts. The performance was observed in the internal and external validation cohorts, with sensitivities of 62.1 % and 52.8 %, and specificities of 66.7 % and 74.7 %, respectively. AI-based CT–derived body composition quantification, particularly baseline SMVI and dynamic changes in SMVI and SAVI during NICT, are independently associated with pCR in NSCLC. Incorporating these modifiable biomarkers into predictive models improves performance beyond clinical variables alone.