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

doi:10.3389/fonc.2019.00829

	Computed Tomography Radiomic Nomogram for Preoperative Prediction of Extrathyroidal Extension in Papillary Thyroid Carcinoma
	Chen, Bin1,2 ; Zhong, Lianzhen 2,3; Dong, Di2,3 ; Zheng, Jianjun 1; Fang, Mengjie2,3 ; Yu, Chunyao 1; Dai, Qi 1; Zhang, Liwen2,3 ; Tian, Jie 2,3,4; Lu, Wei 1; Jin, Yinhua 1
发表期刊	FRONTIERS IN ONCOLOGY
ISSN	2234-943X
	2019-09-04
卷号	9 页码:9
通讯作者	Tian, Jie(jie.tian@ia.ac.cn) ; Lu, Wei(luwei19@ucas.ac.cn) ; Jin, Yinhua(jinyh@ucas.ac.cn)
摘要	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 preoperative 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 were significantly associated with ETE < 0.001 for both cohorts) and slightly better than clinical model integrating significant clinical risk factors in the training cohort (area under the receiver operating characteristic curve (AUC), 0.791 vs. 0.778; F-1 score, 0.729 vs. 0.714) and validation cohort (AUC, 0.772 vs. 0.756; F-1 score, 0.710 vs. 0.692). The radiomic nomogram significantly improved predictive value in the training cohort (AUC, 0.837, p < 0.001; F-1 score, 0.766) and validation cohort (AUC, 0.812, p = 0.024; F-1 score, 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.
关键词	thyroid cancer computed tomography radiomics tumor staging nomograms
DOI	10.3389/fonc.2019.00829
关键词[WOS]	PROGNOSTIC-FACTORS ; CANCER ; MANAGEMENT ; FEATURES ; IMPACT ; IMAGES ; AMES ; MRI
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1309100] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81501616] ; National Natural Science Foundation of China[81671851] ; National Natural Science Foundation of China[81527805] ; Beijing Natural Science Foundation[L182061] ; Bureau of International Cooperation of Chinese Academy of Sciences[173211KYSB20160053] ; Youth Innovation Promotion Association CAS[2017175] ; Key R&D project of Zhejiang Province[2017C03042] ; Major Medical and Health Program of Zhejiang Province[WKJ-ZJ-1807] ; Public Welfare Technology Application Research Project of Zhejiang Province[2017C35003] ; Natural Science Foundation of Zhejiang Province[LY18H180011] ; Public Welfare Technology Research Project of Zhejiang Province[LGF18H180017] ; Medical Science and Technology Project of Zhejiang Province[2019320334] ; Ningbo Municipal Leading and Top-notch Personnel Training Project[NBLJ201801030] ; National Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1309100] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81501616] ; National Natural Science Foundation of China[81671851] ; National Natural Science Foundation of China[81527805] ; Beijing Natural Science Foundation[L182061] ; Bureau of International Cooperation of Chinese Academy of Sciences[173211KYSB20160053] ; Youth Innovation Promotion Association CAS[2017175] ; Key R&D project of Zhejiang Province[2017C03042] ; Major Medical and Health Program of Zhejiang Province[WKJ-ZJ-1807] ; Public Welfare Technology Application Research Project of Zhejiang Province[2017C35003] ; Natural Science Foundation of Zhejiang Province[LY18H180011] ; Public Welfare Technology Research Project of Zhejiang Province[LGF18H180017] ; Medical Science and Technology Project of Zhejiang Province[2019320334] ; Ningbo Municipal Leading and Top-notch Personnel Training Project[NBLJ201801030]
项目资助者	National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Bureau of International Cooperation of Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS ; Key R&D project of Zhejiang Province ; Major Medical and Health Program of Zhejiang Province ; Public Welfare Technology Application Research Project of Zhejiang Province ; Natural Science Foundation of Zhejiang Province ; Public Welfare Technology Research Project of Zhejiang Province ; Medical Science and Technology Project of Zhejiang Province ; Ningbo Municipal Leading and Top-notch Personnel Training Project
WOS研究方向	Oncology
WOS类目	Oncology
WOS记录号	WOS:000483734500001
出版者	FRONTIERS MEDIA SA
引用统计	被引频次：16[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/27307
专题	中国科学院分子影像重点实验室复杂系统管理与控制国家重点实验室
通讯作者	Tian, Jie; Lu, Wei; Jin, Yinhua
作者单位	1.Univ Chinese Acad Sci, Hwa Mei Hosp, Dept Med Awing, Ningbo, Zhejiang, Peoples R China 2.Chinese Acad Sci, CAS Key Lab Mol Imaging, Inst Automat, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 4.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Chen, Bin,Zhong, Lianzhen,Dong, Di,et al. Computed Tomography Radiomic Nomogram for Preoperative Prediction of Extrathyroidal Extension in Papillary Thyroid Carcinoma[J]. FRONTIERS IN ONCOLOGY,2019,9:9.
APA	Chen, Bin.,Zhong, Lianzhen.,Dong, Di.,Zheng, Jianjun.,Fang, Mengjie.,...&Jin, Yinhua.(2019).Computed Tomography Radiomic Nomogram for Preoperative Prediction of Extrathyroidal Extension in Papillary Thyroid Carcinoma.FRONTIERS IN ONCOLOGY,9,9.
MLA	Chen, Bin,et al."Computed Tomography Radiomic Nomogram for Preoperative Prediction of Extrathyroidal Extension in Papillary Thyroid Carcinoma".FRONTIERS IN ONCOLOGY 9(2019):9.