{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T21:25:37Z","timestamp":1787865937777,"version":"build-2784847793"},"reference-count":48,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T00:00:00Z","timestamp":1774569600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T00:00:00Z","timestamp":1774569600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82260356"],"award-info":[{"award-number":["82260356"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["8226070308"],"award-info":[{"award-number":["8226070308"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>\n                      To identify independent determinants influencing therapeutic outcomes of initial radioactive iodine (\n                      <jats:sup>1<\/jats:sup>\n                      <jats:sup>3<\/jats:sup>\n                      <jats:sup>1<\/jats:sup>\n                      I) therapy in differentiated thyroid carcinoma (DTC) and establish an interpretable predictive framework for clinical decision\u2010making.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>\n                      A retrospective cohort of 950 treatment\u2010na\u00efve DTC patients undergoing primary\n                      <jats:sup>1<\/jats:sup>\n                      <jats:sup>3<\/jats:sup>\n                      <jats:sup>1<\/jats:sup>\n                      I therapy was randomly allocated into training (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a0664) and testing (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a0286) cohorts. Multivariable logistic regression (LR) analysis identified response\u2010associated variables, followed by the development of seven machine learning (ML) architectures including decision trees (DT), LR, random forests (RF), support vector machines (SVM), adaptive boosting (AdaBoost), eXtreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). Model performance was systematically evaluated through ROC curves (AUC), calibration plots, and decision curve analysis (DCA), complemented by SHapley Additive exPlanationgs (SHAP) interpretability framework for optimal model explanation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Multivariable analysis demonstrated age,\n                      <jats:sup>1<\/jats:sup>\n                      <jats:sup>3<\/jats:sup>\n                      <jats:sup>1<\/jats:sup>\n                      I therapeutic interval (TI), tumor multiplicity, maximum tumor diameter (MTD), lymph node metastasis (LNM) topography, stimulated thyroglobulin (sTg), thyroglobulin antibody (TgAb) levels, administered activity (AA), and post\u2010therapy whole\u2010body scan (Rx\u2010WBS) findings as independent predictors of non\u2010excellent response (N\u2010ER).The LightGBM architecture achieved superior predictive accuracy (AUC\u00a0=\u00a00.896) in the testing cohort, outperforming conventional LR (AUC\u00a0=\u00a00.842).SHAP interpretation identified sTg (mean absolute SHAP value\u00a0=\u00a01.285), TgAb (mean absolute SHAP value\u00a0=\u00a00.642), and LNM topography (mean absolute SHAP value\u00a0=\u00a00.308) as principal predictive determinants. DCA confirmed that the model had a net benefit higher than the \u201cfull treatment\u201d and \u201cno treatment\u201d strategies at multiple threshold probabilities, indicating its certain application value in clinical decision\u2010making.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      The developed LightGBM framework precisely predicts primary\n                      <jats:sup>1<\/jats:sup>\n                      <jats:sup>3<\/jats:sup>\n                      <jats:sup>1<\/jats:sup>\n                      I therapeutic efficacy in DTC patients, with SHAP\u2010driven elucidation of clinical risk factor contributions enabling personalized therapeutic paradigms.Future integration of multicenter prospective data and molecular biomarkers is required to enhance model generalizability.\n                    <\/jats:p>\n                  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