{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T06:10:46Z","timestamp":1790143846381,"version":"4.0.1"},"reference-count":50,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82302180"],"award-info":[{"award-number":["82302180"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"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>Pathological complete response (pCR) after neoadjuvant chemoimmunotherapy (NACI) varies among patients with non-small cell lung cancer (NSCLC). This study aimed to quantify intratumoral heterogeneity using habitat analysis and to develop a pretreatment model for predicting pCR.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this retrospective multicenter study, 287 patients with NSCLC who received NACI followed by surgery were included. Patients were randomly divided into a training cohort (n = 147) and an internal validation cohort (n = 63), with an independent external test cohort (n = 77). K-means clustering was used to partition tumors into three habitat subregions based on entropy. Radiomics features were extracted from pretreatment CT images at the whole-tumor and habitat levels. Whole-tumor, habitat-based, and combined models were developed to predict pCR. Model performance was evaluated using area under the curve (AUC) analysis, and model interpretability was assessed using Shapley Additive exPlanations (SHAP).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The habitat-based model showed acceptable predictive performance across the training, internal validation, and external test cohorts, with AUCs of 0.74, 0.71, and 0.67, respectively, and showed higher AUCs compared with the whole-tumor radiomics model, which achieved AUCs of 0.72, 0.68, and 0.65. The combined model achieved performance comparable to the habitat-based model. SHAP analysis suggested that, among the habitat-derived features, the most influential predictive features were primarily associated with the high-entropy habitat subregion.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The habitat-based model shows potential performance for predicting pCR, and features from a high-entropy habitat subregion may provide complementary imaging information for preoperative assessment of treatment response.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1933861","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T05:50:14Z","timestamp":1790142614000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Pretreatment CT radiomics for prediction of pathological complete response after neoadjuvant chemoimmunotherapy in NSCLC: a multicenter study incorporating habitat analysis"],"prefix":"10.3389","volume":"17","author":[{"given":"Sunyi","family":"Zheng","sequence":"first","affiliation":[{"name":"Department of Radiology, Medical Artificial General Intelligence for Computation (MAGIC) Lab, National Clinical Research Center for Cancer, Tianjin\u2019s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuyuan","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Medical Artificial General Intelligence for Computation (MAGIC) Lab, National Clinical Research Center for Cancer, Tianjin\u2019s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Lung Cancer, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center of Cancer, Lung Cancer Center, Tianjin Medical University Cancer Institute and Hospital","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaomeng","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Medical Artificial General Intelligence for Computation (MAGIC) Lab, National Clinical Research Center for Cancer, Tianjin\u2019s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"PengChun","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Radiology, Medical Artificial General Intelligence for Computation (MAGIC) Lab, National Clinical Research Center for Cancer, Tianjin\u2019s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiping","family":"Wang","sequence":"additional","affiliation":[{"name":"Academy of Medical Engineering and Translational Medicine, Tianjin University","place":["Tianjin, China"]},{"name":"National Clinical Research Center for Cancer, Tianjin\u2019s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhua","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology, Ordos Central Hospital","place":["Ordos, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Houpu","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Medical Artificial General Intelligence for Computation (MAGIC) Lab, National Clinical Research Center for Cancer, Tianjin\u2019s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunlin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Medical Artificial General Intelligence for Computation (MAGIC) Lab, National Clinical Research Center for Cancer, Tianjin\u2019s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoqing","family":"Liao","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Public Health, Tianjin University of Traditional Chinese Medicine","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaonan","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Radiology, Medical Artificial General Intelligence for Computation (MAGIC) Lab, National Clinical Research Center for Cancer, Tianjin\u2019s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongsheng","family":"Yue","sequence":"additional","affiliation":[{"name":"Department of Lung Cancer, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center of Cancer, Lung Cancer Center, Tianjin Medical University Cancer Institute and Hospital","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21834","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J Clin"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1158\/2159-8290.Cd-16-0577","article-title":"Improved efficacy of neoadjuvant compared to adjuvant immunotherapy to eradicate metastatic disease","volume":"6","author":"Liu","year":"2016","journal-title":"Cancer Discov"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/j.esmoop.2023.102040","article-title":"Neoadjuvant nivolumab plus chemotherapy versus chemotherapy for resectable NSCLC: subpopulation analysis of Chinese patients in CheckMate 816","volume":"8","author":"Wang","year":"2023","journal-title":"ESMO Open"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1111\/1759-7714.15052","article-title":"Predicting pathological response to neoadjuvant or conversion chemoimmunotherapy in stage IB\u2013III non\u2010small cell lung cancer patients using radiomic features","volume":"14","author":"Yang","year":"2023","journal-title":"Thorac Cancer"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.2147\/itt.S437911","article-title":"Comparative efficacy and safety of neoadjuvant immunotherapy with chemotherapy versus chemotherapy alone in non-small cell lung cancer: A propensity score and inverse probability treatment weighting analysis","volume":"12","author":"Zhao","year":"2023","journal-title":"Immunotargets Ther"},{"key":"B6","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1007\/s44178-025-00193-1","article-title":"Efficacy and safety of neoadjuvant chemoimmunotherapy in elderly patients with resectable non-small cell lung cancer: a network meta-analysis and systematic review","volume":"4","author":"Liu","year":"2025","journal-title":"Holist Integr Oncol"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2202170","article-title":"Neoadjuvant nivolumab plus chemotherapy in resectable lung cancer","volume":"386","author":"Forde","year":"2022","journal-title":"N Engl J Med"},{"key":"B8","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1056\/NEJMoa2302983","article-title":"Perioperative pembrolizumab for early-stage non\u2013small-cell lung cancer","volume":"389","author":"Wakelee","year":"2023","journal-title":"N Engl J Med"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.21037\/tlcr-2025-584","article-title":"A narrative review of pathologic response in non-small cell lung cancer: challenges, implications, and future directions","volume":"14","author":"Li","year":"2025","journal-title":"Transl Lung Cancer Res"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1016\/j.critrevonc.2022.103582","article-title":"PD-L1 expression and tumor mutation burden as pathological response biomarkers of neoadjuvant immunotherapy for early-stage non-small cell lung cancer: a systematic review and meta-analysis","volume":"170","author":"Deng","year":"2022","journal-title":"Crit Rev Oncol Hematol"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1097\/jto.0000000000000526","article-title":"Programmed death-ligand 1 immunohistochemistry in lung cancer: In what state is this art","volume":"10","author":"Kerr","year":"2015","journal-title":"J Thorac Oncol"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1097\/js9.0000000000000650","article-title":"The predictive role of hematologic markers in resectable NSCLC patients treated with neoadjuvant chemoimmunotherapy: a retrospective cohort study","volume":"109","author":"Liu","year":"2023","journal-title":"Int J Surg"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1007\/s00262-022-03262-w","article-title":"The predictive value of inflammatory biomarkers for major pathological response in non-small cell lung cancer patients receiving neoadjuvant chemoimmunotherapy and its association with the immune-related tumor microenvironment: a multi-center study","volume":"72","author":"Li","year":"2022","journal-title":"Cancer Immunol Immunother"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.21.02660","article-title":"Overall survival and biomarker analysis of neoadjuvant nivolumab plus chemotherapy in operable stage IIIA non-small-cell lung cancer (NADIM phase II trial)","volume":"40","author":"Provencio","year":"2022","journal-title":"J Clin Oncol"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1097\/rlu.0000000000005863","article-title":"Prognostic value of 18F-FDG PET\/CT metabolic parameters in resectable non\u2013small cell lung cancer treated with neoadjuvant immunotherapy plus chemotherapy","volume":"50","author":"Cheng","year":"2025","journal-title":"Clin Nucl Med"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: Images are more than pictures, they are data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: Extracting more information from medical images using advanced feature analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur J Cancer"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1186\/s40644-020-00376-1","article-title":"Radiomic signature based on CT imaging to distinguish invasive adenocarcinoma from minimally invasive adenocarcinoma in pure ground-glass nodules with pleural contact","volume":"21","author":"Jiang","year":"2021","journal-title":"Cancer Imaging"},{"key":"B19","doi-asserted-by":"publisher","first-page":"9028","DOI":"10.1038\/s41598-024-58551-4","article-title":"Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence","volume":"14","author":"Louis","year":"2024","journal-title":"Sci Rep"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1007\/s00259-019-04625-9","article-title":"Radiomics of 18F-FDG PET\/CT images predicts clinical benefit of advanced NSCLC patients to checkpoint blockade immunotherapy","volume":"47","author":"Mu","year":"2019","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"B21","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1186\/s40644-025-00864-2","article-title":"Differentiating high-grade patterns and predominant subtypes for IASLC grading in invasive pulmonary adenocarcinoma using radiomics and clinical-semantic features","volume":"25","author":"Zheng","year":"2025","journal-title":"Cancer Imaging"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.937277","article-title":"CT-based radiomics in predicting pathological response in non-small cell lung cancer patients receiving neoadjuvant immunotherapy","volume":"12","author":"Lin","year":"2022","journal-title":"Front Oncol"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.21037\/tlcr-24-973","article-title":"Short-term peri- and intra-tumoral CT radiomics to predict immunotherapy response in advanced non-small cell lung cancer","volume":"14","author":"Wang","year":"2025","journal-title":"Transl Lung Cancer Res"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1038\/s41568-020-0241-6","article-title":"Tumour heterogeneity and the evolutionary trade-offs of cancer","volume":"20","author":"Hausser","year":"2020","journal-title":"Nat Rev Cancer"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.31630","article-title":"Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats","volume":"124","author":"Napel","year":"2018","journal-title":"Cancer"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2025.1528836","article-title":"Risk stratification in neuroblastoma patients through machine learning in the multicenter PRIMAGE cohort","volume":"15","author":"Lozano-Montoya","year":"2025","journal-title":"Front Oncol"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.222830","article-title":"MRI-based quantification of intratumoral heterogeneity for predicting treatment response to neoadjuvant chemotherapy in breast cancer","volume":"308","author":"Shi","year":"2023","journal-title":"Radiology"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1002\/mp.17892","article-title":"Thymoma habitat segmentation and risk prediction model using CT imaging and K\u2010means clustering","volume":"52","author":"Liang","year":"2025","journal-title":"Med Phys"},{"key":"B29","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1186\/s12967-024-06057-y","article-title":"Habitat radiomics analysis for progression free survival and immune-related adverse reaction prediction in non-small cell lung cancer treated by immunotherapy","volume":"23","author":"Wu","year":"2025","journal-title":"J Transl Med"},{"key":"B30","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1186\/s12967-024-04904-6","article-title":"A novel sub-regional radiomics model to predict immunotherapy response in non-small cell lung carcinoma","volume":"22","author":"Peng","year":"2024","journal-title":"J Transl Med"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2005.159","article-title":"Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy","volume":"27","author":"Peng","year":"2005","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2011.00771.x","article-title":"Regression shrinkage and selection via the lasso: a retrospective","volume":"73","author":"Tibshirani","year":"2011","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1111\/1759-7714.70149","article-title":"Three\u2010year follow\u2010up of the phase II trial for resectable non\u2010small\u2010cell lung cancer treated with perioperative sintilimab and neoadjuvant anlotinib plus chemotherapy: TD\u2010NeoFOUR trial","volume":"16","author":"Gao","year":"2025","journal-title":"Thorac Cancer"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.21037\/tlcr-2024-1131","article-title":"Integrative radiomics analysis of peri-tumoral and habitat zones for predicting major pathological response to neoadjuvant immunotherapy and chemotherapy in non-small cell lung cancer","volume":"14","author":"Han","year":"2025","journal-title":"Transl Lung Cancer Res"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-023-10241-x","article-title":"Delta-radiomics features for predicting the major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer","volume":"34","author":"Han","year":"2023","journal-title":"Eur Radiol"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.1136\/jitc-2024-009348","article-title":"Non-invasive multimodal CT deep learning biomarker to predict pathological complete response of non-small cell lung cancer following neoadjuvant immunochemotherapy: a multicenter study","volume":"12","author":"Ye","year":"2024","journal-title":"J Immunother Cancer"},{"key":"B37","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1038\/s41576-022-00553-x","article-title":"Spatial biology of cancer evolution","volume":"24","author":"Seferbekova","year":"2022","journal-title":"Nat Rev Genet"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.21037\/qims-20-1182","article-title":"A subregion-based positron emission tomography\/computed tomography (PET\/CT) radiomics model for the classification of non-small cell lung cancer histopathological subtypes","volume":"11","author":"Shen","year":"2021","journal-title":"Quant Imaging Med Surg"},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.24.31675","article-title":"Habitat imaging with tumoral and peritumoral radiomics for prediction of lung adenocarcinoma invasiveness on preoperative chest CT: a multicenter study","volume":"223","author":"Shang","year":"2024","journal-title":"AJR Am J Roentgenol"},{"key":"B40","doi-asserted-by":"publisher","first-page":"15877","DOI":"10.1038\/s41598-024-66751-1","article-title":"Habitat radiomics and deep learning fusion nomogram to predict EGFR mutation status in stage I non-small cell lung cancer: a multicenter study","volume":"14","author":"Wu","year":"2024","journal-title":"Sci Rep"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2021210109","article-title":"Radiomics for survival risk stratification of clinical and pathologic stage IA pure-solid non\u2013small cell lung cancer","volume":"302","author":"Wang","year":"2022","journal-title":"Radiology"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2025.1619704","article-title":"Intratumoral habitat and peritumor radiomics for progression risk stratification of patients with soft tissue sarcoma: a multicenter study","volume":"15","author":"Liang","year":"2026","journal-title":"Front Oncol"},{"key":"B43","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/s12880-025-01551-1","article-title":"High-risk habitat radiomics model based on ultrasound images for predicting lateral neck lymph node metastasis in differentiated thyroid cancer","volume":"25","author":"Liu","year":"2025","journal-title":"BMC Med Imaging"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.3390\/cancers12102881","article-title":"Radiomics of liver metastases: a systematic review","volume":"12","author":"Fiz","year":"2020","journal-title":"Cancers (Basel)"},{"key":"B45","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-021-08146-8","article-title":"Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on MRI","volume":"32","author":"Lee","year":"2021","journal-title":"Eur Radiol"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13233569","article-title":"Tumor heterogeneity of breast cancer assessed with computed tomography texture analysis: association with disease-free survival and clinicopathological prognostic factor","volume":"13","author":"Yoo","year":"2023","journal-title":"Diagnostics (Basel)"},{"key":"B47","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1007\/s00330-011-2319-8","article-title":"Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival","volume":"22","author":"Ganeshan","year":"2011","journal-title":"Eur Radiol"},{"key":"B48","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1016\/j.acra.2025.12.012","article-title":"MRI-based intratumoral and peritumoral habitat radiomics for early prediction of pathologic complete response in breast cancer: a multicenter study","volume":"33","author":"Zhang","year":"2025","journal-title":"Acad Radiol"},{"key":"B49","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2023.103200","article-title":"Ct-based subregional radiomics using hand-crafted and deep learning features for prediction of therapeutic response to anti-PD1 therapy in NSCLC","volume":"117","author":"Hu","year":"2024","journal-title":"Phys Med"},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2025.103551","article-title":"AI-derived longitudinal and multi-dimensional CT classifier for non-small cell lung cancer to optimize neoadjuvant chemoimmunotherapy decision: a multicentre retrospective study","volume":"89","author":"Ye","year":"2025","journal-title":"eClinicalMedicine"}],"container-title":["Frontiers in Immunology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fimmu.2026.1933861\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T05:50:15Z","timestamp":1790142615000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fimmu.2026.1933861\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,23]]},"references-count":50,"alternative-id":["10.3389\/fimmu.2026.1933861"],"URL":"https:\/\/doi.org\/10.3389\/fimmu.2026.1933861","relation":{},"ISSN":["1664-3224"],"issn-type":[{"value":"1664-3224","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,23]]},"article-number":"1933861"}}