Radiomics in predicting mutation status for thyroid cancer: A preliminary study using radiomics features for predicting BRAF^V600E mutations in papillary thyroid carcinoma

Purpose To evaluate whether if ultrasonography (US)-based radiomics enables prediction of the presence of BRAF^V600E mutations among patients diagnosed as papillary thyroid carcninoma (PTC). Methods From December 2015 to May 2017, 527 patients who had been treated surgically for PTC were included (training: 387, validation: 140). All patients had BRAF^V600E mutation analysis performed on surgical specimen. Feature extraction was performed using preoperative US images of the 527 patients (mean size of PTC: 16.4mm±7.9, range, 10–85 mm). A Radiomics Score was generated by using the least absolute shrinkage and selection operator (LASSO) regression model. Univariable/multivariable logistic regression analysis was performed to evaluate the factors including Radiomics Score in predicting BRAF^V600E mutation. Subgroup analysis including conventional PTC <20-mm (n = 389) was performed (training: 280, validation: 109). Results Of the 527 patients diagnosed with PTC, 428 (81.2%) were positive and 99 (18.8%) were negative for BRAF^V600E mutation. In both total 527 cancers and 389 conventional PTC<20-mm, Radiomics Score was the single factor showing significant association to the presence of BRAF^V600E mutation on multivariable analysis (all P<0.05). C-statistics for the validation set in the total cancers and the conventional PTCs<20-mm were lower than that of the training set: 0.629 (95% CI: 0.516–0.742) to 0.718 (95% CI: 0.650–0.786), and 0.567 (95% CI: 0.434–0.699) to 0.729 (95% CI: 0.632–0.826), respectively. Conclusion Radiomics features extracted from US has limited value as a non-invasive biomarker for predicting the presence of BRAF^V600E mutation status of PTC regardless of size.