Ultrasound radiomics for preoperative evaluation of Ki-67 proliferation index in papillary thyroid carcinoma.

Purpose: To explore the predictive value of radiomics features, ultrasound (US), and gene mutation status based on interpretable random forest (RF) models for predicting high Ki-67 expression in papillary thyroid carcinoma (PTC). Methods: This retrospective analysis included 627 patients with surgically confirmed PTC who underwent testing for BRAF V600E and TERT promoter mutations, as well as immunohistochemical assessment of Ki-67 expression from December 2015 to June 2023. The eligible patients were randomly divided into a training set and a testing set at a ratio of 7:3 according to their binary Ki-67 expression status. A random forest model was constructed using both individual and combined ultrasound radiomics features, conventional ultrasound features, and genetic mutation status to predict high Ki-67 expression in PTC. Using AUC, Brier score and decision curve analysis to verify the clinical utility of the model. Result: The Rad+US+Gene model demonstrated superior predictive accuracy for high Ki-67 expression, achieving the highest accuracy and AUC, along with the lowest Brier score. In the testing cohort, the Rad+US+Gene model attained an accuracy of 0.883, outperforming Rad+US, US and Rad (0.851). Its AUC reached 0.904, markedly exceeding those of Rad+US (0.854), US (0.823), and Rad (0.851). Regarding calibration, the Rad+US+Gene model also yielded the lowest Brier score (0.0822), compared with Rad+US (0.1073), US (0.1061), and Rad (0.1232), indicating superior predictive accuracy and stability. Conclusion: The integrated model combining radiomics, US features and genetic mutations achieves favorable predictive performance, presenting a new method for the preoperative assessment of Ki-67 expression level in PTC.