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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Oncol. | doi: 10.3389/fonc.2019.00829

Computed Tomography Radiomic Nomogram for Preoperative Prediction of Extrathyroidal Extension in Papillary Thyroid Carcinoma

Bin Chen1, Lianzhen Zhong2, 3,  Di Dong2, 3, Jianjun Zheng1,  Mengjie Fang2, 3, Chunyao Yu1, Qi Dai1, Liwen Zhang2, 3,  Jie Tian2, 3, 4*,  Wei Lu1* and  Yinhua Jin1*
  • 1Department of Medical Imaging, Hwa Mei Hospital, University of Chinese Academy of Sciences, China
  • 2CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, China
  • 3University of Chinese Academy of Sciences, China
  • 4Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, China

Objectives
Determining the presence of extrathyroidal extension (ETE) is important for patients with papillary thyroid carcinoma (PTC) in selecting the proper surgical approaches. This study aimed to explore a radiomic model for preoperatively prediction of ETE in patients with PTC.
Methods
The study included 624 PTC patients (without ETE, n = 448; with minimal ETE, n = 52; with gross ETE, n = 124) whom were divided randomly into training (n = 437) and validation (n = 187) cohorts; all data were gathered between January 2016 and November 2017. Radiomic features were extracted from computed tomography (CT) images of PTCs. Key radiomic features were identified and incorporated into a radiomic signature. Combining the radiomic signature with clinical risk factors, a radiomic nomogram was constructed using multivariable logistic regression. Delong test was used to compare different receiver operating characteristic curves.
Results
Five key radiomic features were incorporated into the radiomic signature, which was significantly associated with ETE (p < 0.001 for both cohorts) and slightly better than clinical model integrating significant clinical risk factors in the training cohort (AUC, 0.791 vs. 0.778; F1, 0.729 vs. 0.714) and validation cohort (AUC, 0.772 vs. 0.756; F1, 0.710 vs. 0.692). The radiomic nomogram significantly improved predictive value in the training cohort (AUC, 0.837, p < 0.001; F1, 0.766) and validation cohort (AUC, 0.812, p = 0.024; F1, 0.732).
Conclusions
The radiomic nomogram significantly improved the preoperative prediction of ETE in PTC patients. It indicated that radiomics could be a valuable method in PTC research.

Keywords: Thyroid cancer, computed tomography, Radiomics, Tumor staging, Normograms

Received: 11 Jun 2019; Accepted: 13 Aug 2019.

Edited by:

Bo Gao, Affiliated Hospital of Guizhou Medical University, China

Reviewed by:

Zhongxiang Ding, Hangzhou First People's Hospital, China
Seyedmehdi Payabvash, School of Medicine, Yale University, United States  

Copyright: © 2019 Chen, Zhong, Dong, Zheng, Fang, Yu, Dai, Zhang, Tian, Lu and Jin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Prof. Jie Tian, CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China, jie.tian@ia.ac.cn
Dr. Wei Lu, Department of Medical Imaging, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, Zhejiang Province, China, luwei19@ucas.ac.cn
Prof. Yinhua Jin, Department of Medical Imaging, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, Zhejiang Province, China, jinyh@ucas.ac.cn