Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study

doi:10.1016/j.ebiom.2019.07.049

	Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study
	Sun, Caixia 2,3; Tian, Xin 4; Liu, Zhenyu3,5 ; Li, Weili 4; Li, Pengfei 4; Chen, Jiaming 4; Zhang, Weifeng 4; Fang, Ziyu 4; Du, Peiyan 4; Duan, Hui 4
刊名	EBIOMEDICINE
	2019-08-01
卷号	46 页码:160-169
关键词	Radiomics Magnetic resonance imaging Neoadjuvant chemotherapy Locally advanced cervical cancer
ISSN号	2352-3964
DOI	10.1016/j.ebiom.2019.07.049
通讯作者	Liu, Ping(lpivy@126.com) ; Wang, Lihui(wlh1984@gmail.com) ; Chen, Chunlin(ccl1@smu.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要	Background: We aimed to investigate whether pre-therapeutic radiomic features based on magnetic resonance imaging (MRI) can predict the clinical response to neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC). Methods: A total of 275 patients with LACC receiving NACT were enrolled in this study from eight hospitals, and allocated to training and testing sets (2:1 ratio). Three radiomic feature sets were extracted from the intratumoural region of T1-weighted images, intratumoural region of T2-weighted images, and peritumoural region T2-weighted images before NACT for each patient. With a feature selection strategy, three single sequence radiomic models were constructed, and three additional combined models were constructed by combining the features of different regions or sequences. The performance of all models was assessed using receiver operating characteristic curve. Findings: The combined model of the intratumoural zone of T1-weighted images, intratumoural zone of T2-weighted images,and peritumoural zone of T2-weighted images achieved an AUC of 0.998 in training set and 0.999 in testing set, which was significantly better (p < .05) than the other radiomic models. Moreover, no significant variation in performance was found if different training sets were used. Interpretation: This study demonstrated that MRI-based radiomic features hold potential in the pretreatment prediction of response to NACT in LACC, which could be used to identify rightful patients for receiving NACT avoiding unnecessary treatment. (C) 2019 The Authors. Published by Elsevier B.V.
资助项目	National Key Research andDevelopment Plan of China[2017YFA0205200] ; National Natural Science Foundation of China[81772012] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[66161010] ; Nature Science Foundation of Guizhou province[20152044] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[XDB32030200] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Beijing Natural Science Foundation[7182109] ; Youth Innovation Promotion Association CAS[2019136] ; National Natural Science Foundation of Guangdong[2015A030311024] ; Health and Medical Cooperation Innovation Special Program of Guangzhou Municipal Science and Technology[201508020264] ; National Key Technology Program of the Ministry of Science and Technology [863 program][2014BAI05B03] ; Medical Scientific Research Foundation of Guangdong Province of China[A2015063]
WOS关键词	MAGNETIC-RESONANCE ; TEXTURE FEATURES ; RADICAL SURGERY ; STAGE IB2 ; TUMOR ; MRI ; PET ; CHEMORADIATION ; EFFICACY ; IMAGES
WOS研究方向	General & Internal Medicine ; Research & Experimental Medicine
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000486592000028
资助机构	National Key Research andDevelopment Plan of China ; National Natural Science Foundation of China ; Nature Science Foundation of Guizhou province ; Chinese Academy of Sciences ; Beijing Natural Science Foundation ; Youth Innovation Promotion Association CAS ; National Natural Science Foundation of Guangdong ; Health and Medical Cooperation Innovation Special Program of Guangzhou Municipal Science and Technology ; National Key Technology Program of the Ministry of Science and Technology [863 program] ; Medical Scientific Research Foundation of Guangdong Province of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/26998]
专题	中国科学院自动化研究所
通讯作者	Liu, Ping; Wang, Lihui; Chen, Chunlin; Tian, Jie
作者单位	1.Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China 2.Guizhou Univ, Sch Comp Sci & Technol, Key Lab Intelligent Med Image Anal & Precise Diag, 2708 South Sect Huaxi Ave, Guiyang 550025, Guizhou, Peoples R China 3.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China 4.Southern Med Univ, Nanfang Hosp, Dept Obstet & Gynaecol, 1838 Guangzhou Ave North, Guangzhou 510515, Guangdong, Peoples R China 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 6.Xidian Univ, Sch Life Sci & Technol, Minist Educ, Engn Res Ctr Mol & NeSuro Imaging, Xian, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Sun, Caixia,Tian, Xin,Liu, Zhenyu,et al. Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study[J]. EBIOMEDICINE,2019,46:160-169.
APA	Sun, Caixia.,Tian, Xin.,Liu, Zhenyu.,Li, Weili.,Li, Pengfei.,...&Tian, Jie.(2019).Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study.EBIOMEDICINE,46,160-169.
MLA	Sun, Caixia,et al."Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study".EBIOMEDICINE 46(2019):160-169.