Nomogram based on radiomics analysis of ultrasound images can improve preoperative BRAF mutation diagnosis for papillary thyroid microcarcinoma

Abstract

BackgroundThe preoperative identification of BRAF mutation could assist to make appropriate treatment strategies for patients with papillary thyroid microcarcinoma (PTMC). This study aimed to establish an ultrasound (US) radiomics nomogram for the assessment of BRAF status.MethodsA total of 328 PTMC patients at the China-Japan Friendship Hospital between February 2019 and November 2021 were enrolled in this study. They were randomly divided into training (n = 232) and validation (n = 96) cohorts. Radiomics features were extracted from the US images. The least absolute shrinkage and selection operator (LASSO) regression was applied to select the BRAF status-related features and calculate the radiomics score (Rad-score). Univariate and multivariate logistic regression analyses were subsequently performed to identify the independent factors among Rad-score and conventional US features. The US radiomics nomogram was established and its predictive performance was evaluated via discrimination, calibration, and clinical usefulness in the training and validation sets.ResultsMultivariate analysis indicated that the Rad-score, composition, and aspect ratio were independent predictive factors of BRAF status. The US radiomics nomogram which incorporated the three variables showed good calibration. The discrimination of the US radiomics nomogram showed better discriminative ability than the conventional US model both in the training set (AUC 0.685 vs. 0.592) and validation set (AUC 0.651 vs. 0.622). Decision curve analysis indicated the superior clinical applicability of the nomogram compared to the conventional US model.ConclusionsThe US radiomics nomogram displayed better performance than the conventional US model in predicting BRAF mutation in patients with PTMC.

Ultrasound radiomics signature for predicting central lymph node metastasis in clinically node-negative papillary thyroid microcarcinoma

“…This study incorporated Rad score and conventional clinical and US characteristics into a nomogram as in a previous study [ 13 ]. In addition to the Rad score, age and capsule invasion were independent risk factors for CLNM.…”

“…However, they included only limited clinicopathological and US characteristics, preventing sufficient predictive accuracy. Radiomics is a novel technology that converts imaging data into a large panel of quantitative features [ 13 ]. Here, we used US radiomics features to develop and validate a predictive model for the individualized prediction of CLNM in patients with cN0 PTMC.…”

A nomogram model combining ultrasound-based radiomics features and clinicopathological factors to identify germline BRCA1/2 mutation in invasive breast cancer patients

“…Wang and colleagues [ 39 ] utilized US radiomics features of 138 papillary thyroid cancer to predict BRAF V600E mutation and achieved an AUC of 0.938. However, similar studies that included gray-scale images alone yielded limited AUCs of 0.651–0.685 [ 40 , 41 ]. For breast cancer, in a study utilizing gray-scale US images of 312 cases to predict PIK3CA mutation, several machine learning and deep learning models were constructed and showed relatively strong predictive power (AUC 0.741–0.775) [ 42 ].…”

Preoperative prediction of lymphovascular invasion in patients with T1 breast invasive ductal carcinoma based on radiomics nomogram using grayscale ultrasound

“…US-based radiomics nomogram had been widely performed and demonstrated to be of great clinical value in oncology ( 18 , 20 , 21 , 36 , 38 , 39 ). To our knowledge, our study is the first to utilize the US-based radiomics nomogram to preoperatively predict the LVI status in pT1 IDC.…”