Postoperative CT habitat fusion model predicts prognosis in non-small cell lung cancer after neoadjuvant immunochemotherapy

Abstract Neoadjuvant immunochemotherapy (NAIC) has been widely adopted for non-small cell lung cancer (NSCLC); however, reliable tools for postoperative disease-free survival (DFS) risk stratification remain lacking. We developed and validated a habitat radiomics–clinicopathological fusion model, termed the Neoadjuvant Lung Joint Clustering (NeoLung-JC) model, for postoperative DFS stratification and prediction in patients with NSCLC who underwent surgery after NAIC. The model integrates dual-sequence CT-derived tumor habitat heterogeneity scores with key clinicopathological factors. The habitat heterogeneity scores were extracted from the tumor core and peripheral regions using a joint clustering approach. In a multicenter cohort of 508 patients, the NeoLung-JC model showed favorable and consistent predictive performance, achieving a concordance index of 0.750 (95% confidence interval [CI]: 0.680–0.807) in the external validation cohort. The model significantly stratified patients into high-, medium-, and low-risk DFS groups, with clearly separated Kaplan–Meier survival curves across all cohorts ( P < 0.0001). Its prognostic performance exceeded that of conventional clinicopathological indicators, including clinical TNM (cTNM) stage, post-treatment pathological TNM (ypTNM) stage, and pathological response. By integrating dual-sequence CT-derived tumor spatial heterogeneity with clinical risk characteristics, the NeoLung-JC model may provide a useful supplementary approach for individualized postoperative prognostic assessment in patients with NSCLC undergoing surgery after NAIC.