Abstract
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.
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Data availability
The datasets generated and/or analyzed during the current study are not publicly available due to ethical restrictions related to the protection of participants’ privacy, but are available from the corresponding author on reasonable request.
Code availability
All custom code used for habitat segmentation, MSI matrix computation, ITH score calculation, and model development is available at https://github.com/CancerImageAI/WITH-LungCancer. The repository provides documented scripts and instructions to support reproducibility and reuse.
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Acknowledgements
This research was supported by the National Natural Science Foundation of China (No. 82001903 and 82001785), the Natural Science Foundation of Shanghai (No. 25ZR1402077), and the Excellent Young Talents Project of Shanghai Public Health Three-year (2023–2025) Action Plan (No. GWVI-11.2-YQ48).
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Q.P. curated the data, prepared the figures, and drafted the initial manuscript. Y.X. contributed to data processing, materials acquisition, and figure preparation. L.S. provided data support and contributed to model validation. X.B. contributed to model validation and project coordination. S.Z. contributed to the study design and supervised the research. X.Y. contributed to study conception, model implementation, and funding acquisition. Y.G. acquired funding, coordinated resources, and critically reviewed the manuscript. J.G. contributed to study design, data analysis, funding acquisition, and critically reviewed the manuscript. All authors commented on previous versions of the manuscript and approved the final manuscript.
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Peng, Q., Xu, Y., Shen, L. et al. Habitat-based CT radiomics profiling spatial-temporal heterogeneity in resectable NSCLC predict pathological response to neoadjuvant chemoimmunotherapy: a multi-center study. npj Precis. Onc. (2026). https://doi.org/10.1038/s41698-026-01388-z
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DOI: https://doi.org/10.1038/s41698-026-01388-z
