{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T04:30:47Z","timestamp":1786681847660,"version":"build-2736575974"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:00:00Z","timestamp":1786665600000},"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 and purpose<\/jats:title>\n                    <jats:p>Accurate prediction of short-term response to induction chemotherapy (ICT) in nasopharyngeal carcinoma (NPC) remains a critical unmet clinical need. We aimed to construct a multimodal, interpretable machine learning (ML) framework integrating MR dynamic radiomics (delta radiomics) with clinical parameters to predict ICT response.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A multicenter retrospective study enrolled 216 pathologically confirmed NPC patients from two institutions (Xiangyang No. 1 People\u2019s Hospital and Xiangyang Central Hospital, January 2015\u2013September 2024; training cohort n=151, test cohort n=65) and an independent external validation cohort of 87 patients (Affiliated Hospital of Hubei University of Chinese Medicine, February 2017\u2013June 2024). A Delta Radscore was computed from 2,637 candidate features extracted from paired pre- and arterial-phase post-contrast enhanced T1-weighted imaging (CE-T1WI) MRI, quantifying intratumoral microvascular dynamics. After two-stage feature selection (intraclass correlation coefficient [ICC] \u22650.80 stability screening followed by least absolute shrinkage and selection operator (LASSO)\u2013random forest ensemble), eight ML algorithms\u2014XGBoost, CatBoost, support vector machine (SVM), k-nearest neighbors (KNN), and Logistic Regression,etc\u2014were systematically evaluated. SHAP (SHapley Additive exPlanations) analysis provided model interpretability. Independent clinical predictors were identified via uni- and multivariable logistic regression and integrated into a dynamic nomogram.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Multivariable logistic regression identified five independent predictors: T stage (OR\u00a0=\u00a04.29, 95% CI: 1.43\u201312.82, P\u00a0=\u00a00.009), lymphocyte count (OR\u00a0=\u00a02.08, 95% CI: 1.07\u20134.05, P\u00a0=\u00a00.031), lactate dehydrogenase (LDH; OR\u00a0=\u00a01.02, 95% CI: 1.01\u20131.03, P&amp;lt;0.001), Dynamic Rad_Score (OR\u00a0=\u00a00.65, 95% CI: 0.57\u20130.76, P&amp;lt;0.001), and Music Therapy and Psychological Counseling score (MTPC; OR\u00a0=\u00a00.33, 95% CI: 0.16\u20130.68, P\u00a0=\u00a00.026). XGBoost achieved the best performance across all cohorts: training AUC\u00a0=\u00a00.962 (95% CI: 0.937\u20130.998), test AUC\u00a0=\u00a00.861 (95% CI: 0.767\u20130.954), and external validation AUC\u00a0=\u00a00.926; significantly outperforming logistic regression (DeLong P&amp;lt;0.05). The nomogram C-index was 0.876, with a calibration Brier score of 0.082. SHAP analysis ranked Dynamic Rad_Score (mean |SHAP|=0.219), MTPC (0.199), LDH (0.177), and T stage (0.156) as the four most impactful predictors. Decision curve analysis confirmed net clinical benefit of XGBoost across threshold probabilities of 20%\u201385%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>An XGBoost-based multimodal interpretable model integrating MR dynamic radiomics with clinical parameters effectively predicts short-term ICT response in NPC, providing a quantifiable, explainable decision-support tool to guide individualized treatment strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1899153","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T04:22:53Z","timestamp":1786681373000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrating MR dynamic radiomics and clinical parameters for machine learning-based prediction of short-term response to induction chemotherapy in nasopharyngeal carcinoma"],"prefix":"10.3389","volume":"16","author":[{"given":"Huizhen","family":"Guo","sequence":"first","affiliation":[{"name":"Department of Music and Dance, Hubei University of Arts and Science","place":["Xiangyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Music and Dance, Hubei University of Arts and Science","place":["Xiangyang, China"]},{"name":"Department of Internal Medicine and Oncology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, and Xiangyang No. 1 People's Hospital, Hubei University of Medicine","place":["Xiangyang, China"]},{"name":"Department of Radiology, Oncology and Epidemiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine","place":["Xiangyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Oncology and Epidemiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine","place":["Xiangyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Shang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Oncology and Epidemiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine","place":["Xiangyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Oncology and Epidemiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine","place":["Xiangyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Xu","sequence":"additional","affiliation":[{"name":"Affiliated Hospital of Hubei University of Chinese Medicine","place":["Wuhan, Hubei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"An","sequence":"additional","affiliation":[{"name":"Department of Radiology, Oncology and Epidemiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine","place":["Xiangyang, China"]},{"name":"Department of Oncology, Hubei Provincial Clinical Medical Research Center for Pediatric Oncology, Xiangyang No.1 People's Hospital, Hubei University of Medicine","place":["Xiangyang, Hubei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingjian","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Radiology, Oncology and Epidemiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine","place":["Xiangyang, China"]},{"name":"Department of Oncology, Hubei Provincial Clinical Medical Research Center for Pediatric Oncology, Xiangyang No.1 People's Hospital, Hubei University of 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