It is not known how radiomics using ultrasound images contribute to the detection of BRAF mutation. This study aimed to evaluate whether a radiomics study of gray-scale ultrasound can predict the presence or absence of ( ) mutation in papillary thyroid cancer. The study retrospectively included 96 thyroid nodules that were surgically confirmed papillary thyroid cancers between January 2012 and June 2013. mutation was positive in 48 nodules and negative in 48 nodules. For analysis, ROIs from the nodules were demarcated manually on both longitudinal and transverse sonographic images. We extracted a total of 86 radiomics features derived from histogram parameters, gray-level co-occurrence matrix, intensity size zone matrix, and shape features. These features were used to build 3 different classifier models, including logistic regression, support vector machine, and random forest using 5-fold cross-validation. The performance including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve, of the different models was evaluated. The incidence of high-suspicion nodules diagnosed on ultrasound was higher in the mutation-positive group than in the mutation-negative group ( =  .004). The radiomics approach demonstrated that all classification models showed moderate performance for predicting the presence of mutation in papillary thyroid cancers with an area under the curve value of 0.651, accuracy of 64.3%, sensitivity of 66.8%, and specificity of 61.8%, on average, for the 3 models. Radiomics study using thyroid sonography is limited in predicting the mutation status of papillary thyroid carcinoma. Further studies will be needed to validate our results using various diagnostic methods.