{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:48:16Z","timestamp":1785422896967,"version":"3.56.0"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T00:00:00Z","timestamp":1780358400000},"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. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Third-generation epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) resistance poses a significant therapeutic challenge in advanced lung adenocarcinoma. This study aimed to develop and validate a computed tomography (CT)-based habitat radiomics model for predicting response to immunochemotherapy in EGFR-mutant lung adenocarcinoma patients after TKI resistance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective multicenter study enrolled 475 patients from two medical centers. Patients were allocated to train (N\u00a0=\u00a0332) and external validation (N\u00a0=\u00a0143) cohorts. Habitat imaging was performed using K-means clustering to partition tumors into three distinct subregions. Radiomic features were extracted from both whole-tumor volumes and habitat subregions. A combined model combining clinical, conventional radiomics, and habitat features was constructed using machine learning algorithms and validated through cross-validation and external testing. The primary endpoint was objective response rate (ORR) based on Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 criteria, and overall survival (OS) was used as a secondary endpoint.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The combined model demonstrated superior predictive performance with area under the curve (AUC) of 0.904 (95% CI: 0.871\u20130.937) in the train cohort and 0.890 (95% CI: 0.838\u20130.942) in the validation cohort, significantly outperforming the clinical model, conventional whole-tumor radiomics model, and habitat model (all P &amp;lt; 0.001). Moreover, Kaplan\u2013Meier analysis based on the risk groups stratified by the combined model revealed significant survival differences, with high-risk groups showing markedly shorter overall survival in both cohorts (training HR\u00a0=\u00a03.688, validation HR\u00a0=\u00a02.823, both log-rank P &amp;lt; 0.0001).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study developed and externally validated a CT-based habitat radiomics model for predicting response to immunochemotherapy in EGFR-mutant lung adenocarcinoma after EGFR-TKI resistance. The combined model achieved improved predictive performance compared with single-modality approaches. These findings suggest that incorporating habitat-based features may enhance the characterization of intratumoral heterogeneity and improve treatment response prediction. Notably, the model demonstrated a high negative predictive value, suggesting its potential to reduce unnecessary treatment in predicted non-responders. Further prospective and multi-center validation is warranted.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1785968","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T05:42:50Z","timestamp":1780378970000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting response to immunochemotherapy in EGFR-mutant lung adenocarcinoma after third-generation TKI resistance using CT radiomics-based habitat imaging"],"prefix":"10.3389","volume":"17","author":[{"given":"Shuai","family":"Qie","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, Affiliated Hospital of Hebei University","place":["Baoding, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasong","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Affiliated Hospital of Hebei University","place":["Baoding, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyun","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Affiliated Hospital of Hebei University","place":["Baoding, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sicong","family":"Jia","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Affiliated Hospital of Hebei University","place":["Baoding, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoping","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Affiliated Hospital of Hebei University","place":["Baoding, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,2]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"10","DOI":"10.3322\/caac.21871","article-title":"Cancer 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