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

Yuguo Wang, Feiju Xu, Enock Adjei Agyekum, Hong Xiang, Yuandong Wang, Jin Zhang, Hui Sun, Guoliang Zhang, Xiang-shu Bo, Wenzhi Lv, Xian Wang, Shudong Hu, Xiaoqin Qian

2022Frontiers in Endocrinology18 citationsDOIOpen Access PDF

Abstract

BRAF V600E 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 BRAF V600E mutations in PTC. Methods 138 patients with PTC who underwent preoperative ultrasound between January 2014 and 2021 were retrospectively examined. Patients were divided into BRAF V600E mutation-free group (n=75) and BRAF V600E 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 BRAF V600E mutations. Results Eight 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. Conclusion The radiomic model based on grayscale and elasticity ultrasound had a good predictive value for BRAF V600E gene mutations in patients with PTC.

Topics & Concepts

Thyroid carcinomaMedical diagnosisMedicineGrayscaleUltrasoundRadiologyPathologyThyroidInternal medicineComputer scienceArtificial intelligencePixelThyroid Cancer Diagnosis and TreatmentRadiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and Education