Habitat-based CT radiomics profiling spatial-temporal heterogeneity in resectable NSCLC predict pathological response...

The profound spatial and temporal heterogeneity of non-small cell lung cancer (NSCLC) drives unpredictable responses to neoadjuvant chemoimmunotherapy (NCI), highlighting the need for effective predictive biomarkers to optimize treatment. In this multicenter study, we evaluated the ability of habitat imaging to predict major pathological response (MPR) to NCI by capturing spatial-temporal tumor heterogeneity, using pre- and post-treatment CT scans from 394 patients with resectable non-small cell lung cancer across three institutions. A radiomics-based predictive framework integrating global texture descriptors, spatial heterogeneity features, and longitudinal imaging information was constructed to distinguish pathological responders from non-responders. Models based on global texture or spatial heterogeneity features alone achieved areas under the receiver operating characteristic curve (AUCs) ranging from 0.71 to 0.80 across validation cohorts, whereas the integrated model further improved discrimination, achieving an AUC of up to 0.85 in external validation. These findings demonstrate that habitat imaging provides a robust approach for predicting MPR and supporting patient stratification and personalized treatment planning in NSCLC.