Radiomics in predicting mutation status for thyroid cancer: A preliminary study using radiomics features for predicting BRAFV600E 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.