Artificial intelligence to predict the BRAF(V600E) mutation in patients with thyroid cancer

PURPOSE: To investigate whether a computer-aided diagnosis (CAD) program developed using the deep learning convolutional neural network (CNN) on neck US images can predict the BRAF(V600E) mutation in thyroid cancer. METHODS: 469 thyroid cancers in 469 patients were included in this retrospective study. A CAD program recently developed using the deep CNN provided risks of malignancy (0–100%) as well as binary results (cancer or not). Using the CAD program, we calculated the risk of malignancy based on a US image of each thyroid nodule (CAD value). Univariate and multivariate logistic regression analyses were performed including patient demographics, the American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TIRADS) categories and risks of malignancy calculated through CAD to identify independent predictive factors for the BRAF(V600E) mutation in thyroid cancer. The predictive power of the CAD value and final multivariable model for the BRAF(V600E) mutation in thyroid cancer were measured using the area under the receiver operating characteristic (ROC) curves. RESULTS: In this study, 380 (81%) patients were positive and 89 (19%) patients were negative for the BRAF(V600E) mutation. On multivariate analysis, older age (OR = 1.025, p = 0.018), smaller size (OR = 0.963, p = 0.006), and higher CAD value (OR = 1.016, p = 0.004) were significantly associated with the BRAF(V600E) mutation. The CAD value yielded an AUC of 0.646 (95% CI: 0.576, 0.716) for predicting the BRAF(V600E) mutation, while the multivariable model yielded an AUC of 0.706 (95% CI: 0.576, 0.716). The multivariable model showed significantly better performance than the CAD value alone (p = 0.004). CONCLUSION: Deep learning-based CAD for thyroid US can help us predict the BRAF(V600E) mutation in thyroid cancer. More multi-center studies with more cases are needed to further validate our study results.