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

Abstract

PURPOSE:

To evaluate whether if ultrasonography (US)-based radiomics enables prediction of the presence of BRAFV600E 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 BRAFV600E 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 BRAFV600E 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 BRAFV600E 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 BRAFV600E 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 BRAFV600E mutation status of PTC regardless of size.