{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T16:09:16Z","timestamp":1792080556321,"version":"4.4.0"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009558","name":"University Natural Science Research Project of Anhui Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009558","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      BRAF\n                      <jats:sup>V600E<\/jats:sup>\n                      mutation detection enhances diagnostic accuracy in distinguishing benign from malignant thyroid nodules. This study aims to develop and validate a predictive model for the BRAF\n                      <jats:sup>V600E<\/jats:sup>\n                      mutation in C-TIRADS 3 or higher nodules.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A retrospective study was conducted involving 324 patients with C-TIRADS 3 or higher thyroid nodules. Based on BRAF\n                      <jats:sup>V600E<\/jats:sup>\n                      testing from ultrasound-guided fine needle aspiration biopsy (FNAB), patients were divided into wild-type (n=263) and mutation(n=61) groups. Predictive features were independently selected from ultrasonography (US), contrast-enhanced CT (CECT), and combined imaging using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Multivariate logistic regression analysis was employed to identify independent risk factors and then develop three predictive models. Model performance was evaluated through calibration curves, receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and Brier scores, respectively. The optimal model was subsequently converted into a visualized nomogram to facilitate clinical implementation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Ultrasonographic microcalcifications were the strongest independent predictor of BRAF\n                      <jats:sup>V600E<\/jats:sup>\n                      mutation (OR = 9.63, 95% CI: 3.62\u201325.63, P &amp;lt; 0.001). Higher C-TIRADS grades, irregular morphology on US, and blurred borders or capsule interruption on CECT were also significant independent risk factors. Notably, smaller nodule size on US correlated with higher mutation risk (OR = 0.93, 95% CI: 0.88\u20130.98, p=0.012). The multimodal model combining US and CECT (AUC = 0.937) outperformed individual US (AUC = 0.915) and CECT (AUC = 0.784) models.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      The nomogram integrating US and CECT features shows strong predictive performance and clinical utility for identifying BRAF\n                      <jats:sup>V600E<\/jats:sup>\n                      mutations in C-TIRADS 3 or higher thyroid nodules.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2025.1663456","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T05:14:45Z","timestamp":1764652485000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Nomogram model based on ultrasonography and contrast-enhanced CT for 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