Radiomic Model for Determining the Value of Elasticity and Grayscale Ultrasound Diagnoses for Predicting BRAFV600E Mutations in Papillary Thyroid Carcinoma

Abstract

BRAFV600E is the most common mutated gene in thyroid cancer and is most closely related to papillary thyroid carcinoma(PTC). We investigated the value of elasticity and grayscale ultrasonography for predicting BRAFV600E mutations in PTC.Methods138 patients with PTC who underwent preoperative ultrasound between January 2014 and 2021 were retrospectively examined. Patients were divided into BRAFV600E mutation-free group (n=75) and BRAFV600E mutation group (n=63). Patients were randomly divided into training (n=96) and test (n=42) groups. A total of 479 radiomic features were extracted from the grayscale and elasticity ultra-sonograms. Regression analysis was done to select the features that provided the most information. Then, 10-fold cross-validation was used to compare the performance of different classification algorithms. Logistic regression was used to predict BRAFV600E mutations.ResultsEight radiomics features were extracted from the grayscale ultrasonogram, and five radiomics features were extracted from the elasticity ultrasonogram. Three models were developed using these radiomic features. The models were derived from elasticity ultrasound, grayscale ultrasound, and a combination of grayscale and elasticity ultrasound, with areas under the curve (AUC) 0.952 [95% confidence interval (CI), 0.914−0.990], AUC 0.792 [95% CI, 0.703−0.882], and AUC 0.985 [95% CI, 0.965−1.000] in the training dataset, AUC 0.931 [95% CI, 0.841−1.000], AUC 0. 725 [95% CI, 0.569−0.880], and AUC 0.938 [95% CI, 0.851−1.000] in the test dataset, respectively.ConclusionThe radiomic model based on grayscale and elasticity ultrasound had a good predictive value for BRAFV600E gene mutations in patients with PTC.

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

“…Radiogenomics is an emerging research field that aims to correlate imaging features with the underlying genes or mutated genes [ 102 , 103 ]. Though most of these studies have been based on CT or MRI radiomics tools, ultrasomics has also been used especially for breast cancer, not only for the diagnosis of the lesions, but also for the prediction of the molecular subtype, with a reported accuracy of 95% [ 104 , 105 , 106 , 107 ].…”

Ultrasound images-based deep learning radiomics nomogram for preoperative prediction of RET rearrangement in papillary thyroid carcinoma

“…Radiomics has been used to predict the molecular characteristics of various tumors ( 27 ). Concerning thyroid, radiomics has been proposed based on ultrasound images to predict the BRAF mutation ( 19 , 20 , 28 ); however, the models showed limited ability. Moreover, the BRAF mutation is the most representative mutation in PTC and has low specificity among all molecular features.…”

“…Radiomics has recently shown clinical importance in the thyroid ( 17 ). Radiomics based on ultrasound images has been used to predict molecular properties in thyroid cancer ( 18 – 20 ).…”

A Review of the Role of Ultrasound Radiomics and Its Application and Limitations in the Investigation of Thyroid Disease

“…The results showed that in PTC, all classifier models had moderate performance in predicting BRAF gene mutations. Wang et al [ 51 ] retrospectively collected preoperative grayscale and elastography images of 138 patients with PTC and selected ultrasound radiomics features related to BRAF V600E mutation. Finally, 8 radiomics features were extracted from grayscale ultrasound images, and 5 radiomics features were extracted from elastic ultrasound images.…”