CASIA OpenIR
Computed Tomography Radiomic Nomogram for Preoperative Prediction of Extrathyroidal Extension in Papillary Thyroid Carcinoma
Chen, Bin1,2; Zhong, Lianzhen2,3; Dong, Di2,3; Zheng, Jianjun1; Fang, Mengjie2,3; Yu, Chunyao1; Dai, Qi1; Zhang, Liwen2,3; Tian, Jie2,3,4; Lu, Wei1; Jin, Yinhua1
Source PublicationFRONTIERS IN ONCOLOGY
ISSN2234-943X
2019-09-04
Volume9Pages:9
Corresponding AuthorTian, Jie(jie.tian@ia.ac.cn) ; Lu, Wei(luwei19@ucas.ac.cn) ; Jin, Yinhua(jinyh@ucas.ac.cn)
AbstractObjectives: 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.
Keywordthyroid cancer computed tomography radiomics tumor staging nomograms
DOI10.3389/fonc.2019.00829
WOS KeywordPROGNOSTIC-FACTORS ; CANCER ; MANAGEMENT ; FEATURES ; IMPACT ; IMAGES ; AMES ; MRI
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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 Research AreaOncology
WOS SubjectOncology
WOS IDWOS:000483734500001
PublisherFRONTIERS MEDIA SA
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/27307
Collection中国科学院自动化研究所
Corresponding AuthorTian, Jie; Lu, Wei; Jin, Yinhua
Affiliation1.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
Recommended Citation
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.
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