Development and validation of a habitat-based radiomics fusion model for predicting immunotherapy response in advanced NSCLC: a multicenter retrospective study.
6 affiliations
- 1Department of Radiology, the First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
- 2Anhui Key Laboratory of Digital Medicine and Intelligent Health, Bengbu Medical University, Bengbu, China.
- 3Department of Respiratory Disease, the First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
- 4Clinical Research Center for Respiratory Disease (Tumor) in Anhui Province, Bengbu, China.
- 5School of Instrument and Electronics, North University of China, Taiyuan, China.
- 6The 41st Research Institute of China Electronics Technology Group Corporation, Qingdao, China.
Abstract
Despite the efficacy of immune checkpoint inhibitors (ICIs) in advanced non-small cell lung cancer (NSCLC), reliable non-invasive predictors of response remain lacking, partly due to intratumoral heterogeneity.This multicenter study aimed to develop and validate a habitat-based radiomics model to predict ICI response in patients with advanced NSCLC by integrating clinical, whole-tumor radiomics, and habitat features. This multicenter retrospective study included 330 patients with stage IIIB-IV NSCLC who received ICIs between January 2019 and November 2024 across three Chinese hospitals. Patients from the First Affiliated Hospital of Bengbu Medical University were allocated to the training cohort (n=191); those from Shandong Cancer Hospital and the First Affiliated Hospital of Anhui Medical University formed the external validation cohorts (test1, n=70; test2, n=69). The primary endpoint was 3-month progression status per RECIST 1.1. Following feature extraction and a rigorous selection process, seven machine learning algorithms were employed to construct six model types: clinical, radiomics, habitat-based, and three fusion models [clinical-habitat (FCH), radiomics-habitat (FRH), and clinical-radiomics-habitat (FCRH)]. Model performance was assessed by receiver operating characteristic (ROC) analysis, calibration metrics, and decision curve analysis (DCA); DeLong's test, integrated discrimination improvement, and net reclassification improvement were used for model comparisons. Feature interpretability was assessed using SHapley Additive exPlanations (SHAP). The training cohort had 22 progressive disease events (11.52%), test1 had 14 (20.00%), and test2 had 16 (23.19%). The final FCRH model, using extreme gradient boosting (XGBoost), achieved areas under the curve of 0.937 [95% confidence interval (CI): 0.903-0.971] in training, 0.862 (95% CI: 0.773-0.951) in test1, and 0.823 (95% CI: 0.710-0.936) in test2. Sensitivities/specificities were 87.6%/88.9%, 76.8%/78.6%, and 83.0%/72.7%, respectively. Calibration was good (Hosmer-Lemeshow P>0.05 in all cohorts). SHAP analysis identified that habitat-derived features constituted 70.83% of the top 24 predictors. Habitat-based radiomics substantially improves prediction of ICI response in advanced NSCLC, with promising performance in external validation. Prospective studies are warranted before clinical translation.
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BibTeX
@article{li2026development,
title = {{Development and validation of a habitat-based radiomics fusion model for predicting immunotherapy response in advanced NSCLC: a multicenter retrospective study.}},
author = {Shuhua Li and Ling Jin and Qiuyang Zhu and Dezhi Zhu and Ying Meng and Xinyu Lu and Zhiya Zhang and Zongyu Xie and Wei Li},
journal = {Journal of thoracic disease},
year = {2026},
month = sep,
doi = {10.21037/jtd-2026-1443},
url = {https://europepmc.org/article/MED/42996971},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Development and validation of a habitat-based radiomics fusion model for predicting immunotherapy response in advanced NSCLC: a multicenter retrospective study.
AU - Shuhua Li
AU - Ling Jin
AU - Qiuyang Zhu
AU - Dezhi Zhu
AU - Ying Meng
AU - Xinyu Lu
AU - Zhiya Zhang
AU - Zongyu Xie
AU - Wei Li
PY - 2026
DA - 2026/09/28
JO - Journal of thoracic disease
DO - 10.21037/jtd-2026-1443
UR - https://europepmc.org/article/MED/42996971
AB - Despite the efficacy of immune checkpoint inhibitors (ICIs) in advanced non-small cell lung cancer (NSCLC), reliable non-invasive predictors of response remain lacking, partly due to intratumoral heterogeneity.This multicenter study aimed to develop and validate a habitat-based radiomics model to predict ICI response in patients with advanced NSCLC by integrating clinical, whole-tumor radiomics, and habitat features. This multicenter retrospective study included 330 patients with stage IIIB-IV NSCLC who received ICIs between January 2019 and November 2024 across three Chinese hospitals. Patients from the First Affiliated Hospital of Bengbu Medical University were allocated to the training cohort (n=191); those from Shandong Cancer Hospital and the First Affiliated Hospital of Anhui Medical University formed the external validation cohorts (test1, n=70; test2, n=69). The primary endpoint was 3-month progression status per RECIST 1.1. Following feature extraction and a rigorous selection process, seven machine learning algorithms were employed to construct six model types: clinical, radiomics, habitat-based, and three fusion models [clinical-habitat (FCH), radiomics-habitat (FRH), and clinical-radiomics-habitat (FCRH)]. Model performance was assessed by receiver operating characteristic (ROC) analysis, calibration metrics, and decision curve analysis (DCA); DeLong's test, integrated discrimination improvement, and net reclassification improvement were used for model comparisons. Feature interpretability was assessed using SHapley Additive exPlanations (SHAP). The training cohort had 22 progressive disease events (11.52%), test1 had 14 (20.00%), and test2 had 16 (23.19%). The final FCRH model, using extreme gradient boosting (XGBoost), achieved areas under the curve of 0.937 [95% confidence interval (CI): 0.903-0.971] in training, 0.862 (95% CI: 0.773-0.951) in test1, and 0.823 (95% CI: 0.710-0.936) in test2. Sensitivities/specificities were 87.6%/88.9%, 76.8%/78.6%, and 83.0%/72.7%, respectively. Calibration was good (Hosmer-Lemeshow P>0.05 in all cohorts). SHAP analysis identified that habitat-derived features constituted 70.83% of the top 24 predictors. Habitat-based radiomics substantially improves prediction of ICI response in advanced NSCLC, with promising performance in external validation. Prospective studies are warranted before clinical translation.
SN - 2072-1439
SN - 2077-6624
ER -