Journal of Multidisciplinary Healthcare (Jan 2023)

MRI-Based Texture Analysis for Preoperative Prediction of BRAF V600E Mutation in Papillary Thyroid Carcinoma

  • Zheng T,
  • Hu W,
  • Wang H,
  • Xie X,
  • Tang L,
  • Liu W,
  • Wu PY,
  • Xu J,
  • Song B

Journal volume & issue
Vol. Volume 16
pp. 1 – 10

Abstract

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Tingting Zheng,1,* Wenjuan Hu,1,* Hao Wang,1 Xiaoli Xie,2 Lang Tang,3 Weiyan Liu,4 Pu-Yeh Wu,5 Jingjing Xu,1,* Bin Song1,* 1Department of Radiology, Minhang Hospital, Fudan University, Shanghai, People’s Republic of China; 2Department of Pathology, Minhang Hospital, Fudan University, Shanghai, People’s Republic of China; 3Department of Ultrasound, Minhang Hospital, Fudan University, Shanghai, People’s Republic of China; 4Department of General Surgery, Minhang Hospital, Fudan University, Shanghai, People’s Republic of China; 5GE Healthcare, MR Research China, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Bin Song; Jingjing Xu, Department of Radiology, Minhang Hospital, Fudan University, No. 170, Xinsong Road, Minhang District, Shanghai, 201199, People’s Republic of China, Email [email protected]; [email protected]: BRAF V600E mutation can compensate for the low detection rate by fine-needle aspiration (FNA) and is related to aggressiveness and lymph node metastasis. This study aimed to investigate the relationship between texture analysis features based on magnetic resonance imaging (MRI) and mutations.Methods: Retrospective analysis was performed on patients with postoperative pathology confirmed papillary thyroid carcinoma (PTC) from 2017 to 2021. One thousand one hundred and thirty-two texture features were extracted from T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) separately by outlining the tumor volume of interest (VOI). Univariate, minimum redundancy maximum relevance (mRMR), and multivariate analyses were used for feature selection to construct 3 models (T2WI, CE-T1WI, and combined model) to predict mutation. The reproducibility between observers was evaluated by intraclass correlation coefficient (ICC). Receiver operating characteristic (ROC) analysis was used to assess the performance of models. The diagnostic performance of the optimal cut-off value of models were calculated and validated by 10-fold cross-validation.Results: A total of 80 PTCs (22 BRAF V600E wild-type and 58 BRAF V600E mutant) were included in our study. Good interobserver agreement was found on texture features we selected (all ICCs > 0.75). The area under the ROC curves (AUCs) for the T2WI model, CE-T1WI model, and combined model were 0.83 (95% CI: 0.75– 0.91), 0.83 (95% CI: 0.73– 0.90), and 0.88 (95% CI: 0.81– 0.94), respectively. The accuracy, sensitivity, specificity, PPV, and NPV were 0.776, 0.679, 0.905, 0.905, and 0.679 for the T2WI model at a cut-off value of 0.674; 0.755, 0.750, 0.762, 0.808, and 0.696 for the CE-T1WI model at a cut-off value of 0.573; 0.816, 0.893, 0.714, 0.806, and 0.833 for the combined model at a cut-off value of 0.420.Conclusion: MRI-based texture analysis could be a potential method for predicting BRAF V600E mutation in PTC preoperatively.Keywords: magnetic resonance imaging, texture analysis, radiomics, papillary thyroid carcinoma, BRAF V600E

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