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Diagnostic value of machine learning based on multi-parameters of MRI radiomics to predict cervical lymph node status of papillary thyroid carcinoma
MA Weiqiong  CHEN Kangyin  YANG Ning  JIANG Guihua  LAN Bowen  ZENG Yujing 

Cite this article as: Ma WQ, Chen KY, Yang N, et al. Diagnostic value of machine learning based on multi-parameters of MRI radiomics to predict cervical lymph node status of papillary thyroid carcinoma[J]. Chin J Magn Reson Imaging, 2022, 13(10): 108-113. DOI:10.12015/issn.1674-8034.2022.10.016.

[Abstract] Objective To explore the diagnostic value of machine learning based on multi-parameters of MRI radiomics to predict cervical lymph node status in patients with papillary thyroid carcinoma (PTC).Materials and Methods The imaging and clinical data of 182 patients with PTC diagnosed by operation and pathology were retrospectively analyzed, according to the result of surgical pathology, the patients were divided into lymph node metastasis group (91 cases) and no lymph node metastasis group (91 cases). Radiomics analysis was performed on multi-parameters of MRI [T1WI, T2WI, fat-saturated T2WI, enhanced T1WI, fat-saturated and enhanced T1WI, diffusion weighted imaging (DWI)] to acquire texture features and histogram features. Based on the above features, a support vector machine (SVM) model was constructed to classify cervical metastatic and non-metastatic lymph nodes.Results The diagnostic performance of machine learning models which were based multi-parameters of MRI radiomics was superior,with a classification accuracy of 79.61%, a sensitivity of 75.00%, a specificity of 83.00%, and an area under curve (AUC) value of 0.911.Conclusions The machine learning method based on multi-parameters of MRI radiomics can effectively predict cervical lymph node status in patients with PTC.
[Keywords] thyroid carcinoma;papillary thyroid carcinoma;cervical lymph node status;multi-parameters;magnetic resonance imaging;radiomics;histogram features;support vector machine

MA Weiqiong1   CHEN Kangyin1   YANG Ning2   JIANG Guihua2   LAN Bowen1   ZENG Yujing1*  

1 Department of Radiology, Huizhou Central People's Hospital, Huizhou 516000, China

2 Department of imaging, Guangdong Second Provincial General Hospital, Guangzhou 510000, China

Zeng YJ, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS  ANKNOWLEDGMENTS Guangdong Medical Research Foundation B2019201 Huizhou Science and Technology Bureau 2018Y025 Key Support Project of Huizhou Central People's Hospital in 2018 (No.〔2018〕118)
Received  2022-06-09
Accepted  2022-09-30
DOI: 10.12015/issn.1674-8034.2022.10.016
Cite this article as: Ma WQ, Chen KY, Yang N, et al. Diagnostic value of machine learning based on multi-parameters of MRI radiomics to predict cervical lymph node status of papillary thyroid carcinoma[J]. Chin J Magn Reson Imaging, 2022, 13(10): 108-113.DOI:10.12015/issn.1674-8034.2022.10.016

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