Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
| Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study | |
Sun, Caixia1,2; Tian, Xin3; Liu, Zhenyu2,4 ; Li, Weili3; Li, Pengfei3; Chen, Jiaming3; Zhang, Weifeng3; Fang, Ziyu3; Du, Peiyan3; Duan, Hui3; Liu, Ping3; Wang, Lihui1; Chen, Chunlin3; Tian, Jie2,4,5,6
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| Source Publication | EBIOMEDICINE
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| ISSN | 2352-3964 |
| 2019-08-01 | |
| Volume | 46Pages:160-169 |
| Corresponding Author | Liu, Ping(lpivy@126.com) ; Wang, Lihui(wlh1984@gmail.com) ; Chen, Chunlin(ccl1@smu.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn) |
| Abstract | 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. |
| Keyword | Radiomics Magnetic resonance imaging Neoadjuvant chemotherapy Locally advanced cervical cancer |
| DOI | 10.1016/j.ebiom.2019.07.049 |
| WOS Keyword | MAGNETIC-RESONANCE ; TEXTURE FEATURES ; RADICAL SURGERY ; STAGE IB2 ; TUMOR ; MRI ; PET ; CHEMORADIATION ; EFFICACY ; IMAGES |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] ; 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] |
| Funding Organization | 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 |
| WOS Research Area | General & Internal Medicine ; Research & Experimental Medicine |
| WOS Subject | Medicine, General & Internal ; Medicine, Research & Experimental |
| WOS ID | WOS:000486592000028 |
| Publisher | ELSEVIER |
| Sub direction classification | 医学影像处理与分析 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/26999 |
| Collection | 中国科学院分子影像重点实验室 |
| Corresponding Author | Liu, Ping; Wang, Lihui; Chen, Chunlin; Tian, Jie |
| Affiliation | 1.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 2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China 3.Southern Med Univ, Nanfang Hosp, Dept Obstet & Gynaecol, 1838 Guangzhou Ave North, Guangzhou 510515, Guangdong, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 5.Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China 6.Xidian Univ, Sch Life Sci & Technol, Minist Educ, Engn Res Ctr Mol & NeSuro Imaging, Xian, Shaanxi, Peoples R China |
| First Author Affilication | Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China |
| Corresponding Author Affilication | Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China |
| Recommended Citation 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. |
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