{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T22:42:18Z","timestamp":1793313738134,"version":"4.5.0"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:00:00Z","timestamp":1761609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Predicting the intracranial efficacy of programmed death-1\/programmed death-ligand 1 (PD-1\/PD-L1) inhibitors in non-small cell lung cancer (NSCLC) patients with brain metastasis (BM) remains challenging. The objective of this study was to construct a habitat-peritumoral radiomics framework for immunotherapy response prediction, concurrently identifying the optimal peritumoral extent.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective multicenter study analyzed 378 NSCLC-BM patients receiving PD-1\/PD-L1 inhibitors. Participants were stratified into training (n=146), internal validation (n=63), and two external test cohorts (test 1: n=57; test 2: n=112). Logistic regression was conducted to determine significant clinical predictors. Habitat subregion segmentation was performed using K-means clustering with peritumoral extensions at incremental distances (1, 2, and 3\u00a0mm). Predictive models were developed using radiomic features extracted from intratumoral cores, habitat subregions, and peritumoral zones through machine learning approaches. A combined model integrated habitat signatures, peritumoral features, and clinical predictors. Model performance assessment employed the area under the curves (AUCs), calibration curves, and decision curve analyses (DCA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The habitat-based XGBoost model demonstrated superior predictive performance across all cohorts compared to alternative models, achieving AUCs of 0.900 (training), 0.886 (internal validation), 0.820 (test 1), and 0.804 (test 2). For peritumoral analysis, the peri-1 mm RandomForest model exceeded other regional configurations. Integrating peri-1 mm features and clinical factors yielded a marginal performance enhancement in the combined model, with corresponding AUCs of 0.898, 0.894, 0.837, and 0.814. The combined model demonstrated optimal calibration and significant clinical utility, as evidenced by calibration curves and DCA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The validated habitat-peritumoral radiomics framework, optimized at a 1-mm peritumoral extent, demonstrates robust predictive accuracy for intracranial immunotherapy response in NSCLC-BM patients and offers significant clinical utility.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2025.1657290","type":"journal-article","created":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T05:17:52Z","timestamp":1761628672000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Prediction of intracranial response to PD-1\/PD-L1 inhibitors therapy in brain metastases originating from non-small cell lung cancer using habitat 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