A Computed Tomography-Based Radiomic Prognostic Marker of Advanced High-Grade Serous Ovarian Cancer Recurrence: A Multicenter Study

doi:10.3389/fonc.2019.00255

	A Computed Tomography-Based Radiomic Prognostic Marker of Advanced High-Grade Serous Ovarian Cancer Recurrence: A Multicenter Study
	Wei, Wei1,2,3 ; Liu, Zhenyu2,4 ; Rong, Yu 5; Zhou, Bin 6; Bei, Yan 7; Wei, Wei 7; Wang, Shuo2,4 ; Wang, Meiyun 7; Guo, Yingkun 8; Tian, Jie 1,2,4,9
发表期刊	FRONTIERS IN ONCOLOGY
ISSN	2234-943X
	2019-04-09
卷号	9 页码:12
通讯作者	Wang, Meiyun(marian9999@163.com) ; Guo, Yingkun(gykpanda@163.com) ; Tian, Jie(jie.tian@ia.ac.cn)
摘要	Objectives: We used radiomic analysis to establish a radiomic signature based on preoperative contrast enhanced computed tomography (CT) and explore its effectiveness as a novel recurrence risk prognostic marker for advanced high-grade serous ovarian cancer (HGSOC). Methods: This study had a retrospective multicenter (two hospitals in China) design and a radiomic analysis was performed using contrast enhanced CT in advanced HGSOC (FIGO stage III or IV) patients. We used a minimum 18-month follow-up period for all patients (median 38.8 months, range 18.8-81.8 months). All patients were divided into three cohorts according to the timing of their surgery and hospital stay: training cohort (TC) and internal validation cohort (IVC) were fromone hospital, and independent external validation cohort (IEVC) was from another hospital. A total of 620 3-D radiomic features were extracted and a Lasso-Cox regression was used for feature dimension reduction and determination of radiomic signature. Finally, we combined the radiomic signature with seven common clinical variables to develop a novel nomogram using a multivariable Cox proportional hazards model. Results: A final 142 advanced HGSOC patients were enrolled. Patients were successfully divided into two groups with statistically significant differences based on radiomic signature, consisting of four radiomic features (log-rank test P = 0.001, < 0.001, < 0.001 for TC, IVC, and IEVC, respectively). The discrimination accuracies of radiomic signature for predicting recurrence risk within 18 months were 82.4% (95% CI, 77.8-87.0%), 77.3% (95% CI, 74.4-80.2%), and 79.7% (95% CI, 73.8-85.6%) for TC, IVC, and IEVC, respectively. Further, the discrimination accuracies of radiomic signature for predicting recurrence risk within 3 years were 83.4%(95% CI, 77.3-89.6%), 82.0% (95% CI, 78.9-85.1%), and 70.0% (95% CI, 63.6-76.4%) for TC, IVC, and IEVC, respectively. Finally, the accuracy of radiomic nomogram for predicting 18-month and 3-year recurrence risks were 84.1% (95% CI, 80.5-87.7%) and 88.9% (95% CI, 85.8-92.5%), respectively. Conclusions: Radiomic signature and radiomic nomogram may be low-cost, non-invasive means for successfully predicting risk for postoperative advanced HGSOC recurrence before or during the perioperative period. Radiomic signature is a potential prognostic marker that may allow for individualized evaluation of patients with advanced HGSOC.
关键词	advanced high-grade serous ovarian cancer CT prognosis radiomics recurrence
DOI	10.3389/fonc.2019.00255
关键词[WOS]	FREE SURVIVAL ; PREDICTION ; CHEMOTHERAPY ; VALIDATION ; SIGNATURE ; MORTALITY ; NOMOGRAM ; SURGERY ; 5-YEAR ; IMAGES
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Plan of China[2017YFA0205200] ; National Key Research and Development Plan of China[2016YFC0103001] ; National Key Research and Development Plan of China[YS2017YFGH000397] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81772012] ; National Natural Science Foundation of China[81720108021] ; National Natural Science Foundation of China[81641168] ; Beijing Natural Science Foundation[7182109] ; Beijing Municipal Science & Technology Commission[Z171100000117023] ; Beijing Municipal Science & Technology Commission[Z161100002616022] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Henan Province Scientific and Technological Cooperation Project[152106000014] ; National Key Research and Development Plan of China[2017YFA0205200] ; National Key Research and Development Plan of China[2016YFC0103001] ; National Key Research and Development Plan of China[YS2017YFGH000397] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81772012] ; National Natural Science Foundation of China[81720108021] ; National Natural Science Foundation of China[81641168] ; Beijing Natural Science Foundation[7182109] ; Beijing Municipal Science & Technology Commission[Z171100000117023] ; Beijing Municipal Science & Technology Commission[Z161100002616022] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Henan Province Scientific and Technological Cooperation Project[152106000014]
项目资助者	National Key Research and Development Plan of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Beijing Municipal Science & Technology Commission ; Chinese Academy of Sciences ; Henan Province Scientific and Technological Cooperation Project
WOS研究方向	Oncology
WOS类目	Oncology
WOS记录号	WOS:000463924700001
出版者	FRONTIERS MEDIA SA
七大方向——子方向分类	医学影像处理与分析
引用统计	被引频次：41[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/173211/24953
专题	中国科学院分子影像重点实验室
通讯作者	Wang, Meiyun; Guo, Yingkun; Tian, Jie
作者单位	1.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Shaanxi, Peoples R China 2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China 3.Xian Polytech Univ, Sch Appl Technol, Xian, Shaanxi, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 5.Guizhou Prov Peoples Hosp, Dept Radiol, Key Lab Intelligent Med Image Anal & Precis Diag, Guiyang, Guizhou, Peoples R China 6.Sichuan Univ, Key Lab Birth Defects & Related Dis Women & Child, Minist Educ, West China Univ Hosp 2, Chengdu, Sichuan, Peoples R China 7.Henan Prov Peoples Hosp, Dept Radiol, Zhengzhou, Henan, Peoples R China 8.Sichuan Univ, Dept Radiol, Key Lab Birth Defects & Related Dis Women & Child, Minist Educ,West China Univ Hosp 2, Chengdu, Sichuan, Peoples R China 9.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Wei, Wei,Liu, Zhenyu,Rong, Yu,et al. A Computed Tomography-Based Radiomic Prognostic Marker of Advanced High-Grade Serous Ovarian Cancer Recurrence: A Multicenter Study[J]. FRONTIERS IN ONCOLOGY,2019,9:12.
APA	Wei, Wei.,Liu, Zhenyu.,Rong, Yu.,Zhou, Bin.,Bei, Yan.,...&Tian, Jie.(2019).A Computed Tomography-Based Radiomic Prognostic Marker of Advanced High-Grade Serous Ovarian Cancer Recurrence: A Multicenter Study.FRONTIERS IN ONCOLOGY,9,12.
MLA	Wei, Wei,et al."A Computed Tomography-Based Radiomic Prognostic Marker of Advanced High-Grade Serous Ovarian Cancer Recurrence: A Multicenter Study".FRONTIERS IN ONCOLOGY 9(2019):12.