{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T05:13:46Z","timestamp":1783660426662,"version":"3.55.0"},"reference-count":17,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"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>Objective<\/jats:title>\n                    <jats:p>To explore influencing factors and construct predictive models for the efficacy of postoperative adjuvant \u00b9\u00b3\u00b9I therapy in patients with differentiated thyroid cancer (DTC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 376 DTC patients who underwent total thyroidectomy and received initial adjuvant \u00b9\u00b3\u00b9I therapy at a fixed activity of 100 mCi were retrospectively enrolled. All patients were divided into excellent response (ER) and non-excellent response (nER) groups according to the 2025 ATA evaluation criteria at the 6-month follow-up. Relevant statistical methods and multivariate binary logistic regression were adopted to screen independent influencing factors. ROC curve analysis, support vector machine (SVM) and random forest (RF) were applied to establish predictive models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The incidence of nER was 48.40%. BMI, number of metastatic lymph nodes (LNM), stimulated thyroglobulin (s-Tg), BRAF mutation (BRAF) and mean residual \u00b9\u00b3\u00b9I uptake count (C-mean) were independent risk factors (OR\u00a0=\u00a01.112, 1.061, 1.198, 3.041, 1.103, respectively), while maximum residual \u00b9\u00b3\u00b9I uptake count (C-max) was an independent protective factor (OR\u00a0=\u00a00.995). The AUC of logistic regression model was 86.30%, and optimal cut-off values were determined as follows: BMI 29.345 kg\/m\u00b2, LNM 9.5 nodes, s-Tg 3.3 ng\/ml, C-max 18.5 counts, and C-mean 38 counts. The AUC of SVM model was 86.33%, and the RF model showed the optimal predictive performance with an AUC of 90.77%. S-Tg, LNM and BMI were the top three important predictors. Patients with concurrent high s-Tg, multiple metastatic lymph nodes and elevated BMI had a 100% risk of nER.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A considerable proportion of DTC patients fail to achieve ER after 100 mCi adjuvant \u00b9\u00b3\u00b9I therapy. BMI, LNM, s-Tg, BRAF and C-mean are independent risk factors for nER, while C-max are independent protective factors against nER. The RF model possesses excellent predictive ability for adjuvant \u00b9\u00b3\u00b9I treatment response. The coexistence of s-Tg \u2265 3.3 ng\/ml, LNM \u2265 9.5 nodes, and BMI \u2265 29.345 kg\/m\u00b2 indicates an extremely high risk of nER, which offers evidence-based support for the judicious escalation of \u00b9\u00b3\u00b9I activity in selected patients undergoing adjuvant therapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1887096","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T04:25:22Z","timestamp":1783657522000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Influencing factors and model predictive analysis of the efficacy of adjuvant \u00b9\u00b3\u00b9I therapy after DTC surgery"],"prefix":"10.3389","volume":"16","author":[{"given":"Honghong","family":"Pan","sequence":"first","affiliation":[{"name":"Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin\u2019s Clinical Research Center 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