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

Abstract

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.