A multicenter multimodel habitat radiomics model for predicting immunotherapy response in advanced NSCLC

Xiaoxiao Huang; Yurun Xie; Haiyang Nong; Xiaoxin Huang; Donglian Gu; Kui Wang; Deyou Huang; Guanqiao Jin

doi:10.1016/j.isci.2025.114522

A multicenter multimodel habitat radiomics model for predicting immunotherapy response in advanced NSCLC

iScience. 2025 Dec 24;29(2):114522. doi: 10.1016/j.isci.2025.114522. eCollection 2026 Feb 20.

Authors

Xiaoxiao Huang^{1

2}, Yurun Xie³, Haiyang Nong¹, Xiaoxin Huang⁴, Donglian Gu², Kui Wang², Deyou Huang^{1

5}, Guanqiao Jin²

Affiliations

¹ Department of Radiology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi 533000, China.
² Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi 530000, China.
³ Department of Surgery, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi 533000, China.
⁴ Department of Radiology, Jiangbin Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi 530000, China.
⁵ Life Science and Clinical Medicine Research Center, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi 533000, China.

Abstract

A robust predictive biomarker is critical for identifying patients with NSCLC who may benefit from immunotherapy. This study developed a CT-based habitat model using 590 advanced NSCLC cases. The model was constructed in contrast-enhanced CT images and validated on an independent cohort with non-contrast CT. Tumor volumes were segmented into three subregions via K-means clustering. Radiomic features were extracted from each habitat and used to build predictive models with six machine learning classifiers. The ExtraTrees-based habitat model demonstrated superior predictive performance in the test cohort (AUC = 0.814). Compared to traditional radiomics, 3D deep learning, clinical, and PD-L1 expression models, the habitat model maintained strong predictive advantages, enabling efficient prediction of immunotherapy benefit and aiding in the identification of suitable patients for personalized.

Keywords: Health sciences; Internal medicine; Medical specialty; Medicine; Oncology.