CASIA OpenIR  > 中国科学院分子影像重点实验室
Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study
Wu, Qingxia1; Yao, Kuan2,3; Liu, Zhenyu3,4; Li, Longfei3,5; Zhao, Xin6; Wang, Shuo3,7; Shang, Honglei6; Lin, Yusong3; Wen, Zejun6; Tian, Jie3,4,7,8; Wang, Meiyun1
Source PublicationEBIOMEDICINE
ISSN2352-3964
2019-12-01
Volume50Pages:355-365
Corresponding AuthorWen, Zejun(zxa@vip.163.com) ; Tian, Jie(jie.tian@ia.ac.cn) ; Wang, Meiyun(mywang@ha.edu.cn)
AbstractBackground: Identification of pregnancies with postpartum haemorrhage (PPH) antenatally rather than intrapartum would aid delivery planning, facilitate transfusion requirements and decrease maternal complications. MRI has been increasingly used for placenta evaluation. Here, we aim to build a nomogram incorporating both clinical and radiomic features of placenta to predict the risk for PPH in pregnancies during caesarian delivery (CD). Methods: A total of 298 pregnant women were retrospectively enrolled from Henan Provincial People's Hospital (training cohort: n = 207) and from The Third Affiliated Hospital of Zhengzhou University (external validation cohort: n = 91). These women were suspected with placenta accreta spectrum (PAS) disorders and underwent MRI for placenta evaluation. All of them underwent CD and were singleton. PPH was defined as more than 1000 mL estimated blood loss (EBL) during CD. Radiomic features were selected based on their correlations with EBL. Radiomic, clinical, radiological, clinicoradiological and clinicoradiomic models were built to predict the risk of PPH for each patient. The model with the best prediction performance was validated with its discrimination ability, calibration curve and clinical application. Findings: Thirty-five radiomic features showed strong correlation with EBL. The clinicoradiomic model resulted in the best discrimination ability for risk prediction of PPH, with AUC of 0.888 (95% CI, 0.844-0.933) and 0.832 (95% CI, 0.746-0.913), sensitivity of 91.2% (95% CI, 85.8%-96.7%) and 97.6% (95% CI, 92.7%-100%) in the training and validation cohort respectively. For patients with severe PPH (EBL more than 2000 mL), 53 out of 55 pregnancies (96.4%) in the training cohort and 18 out of 18 (100%) pregnancies in the validation cohort were identified by the clinicoradiomic model. The model performed better in patients without placenta previa (PP) than in patients with PP, with AUC of 0.983 compared with 0.867, sensitivity of 100% compared with 90.8% in the training cohort, AUC of 0.832 compared with 0.815, sensitivity of 97.6% compared with 97.2% in the validation cohort. Interpretation: The clinicoradiomic model incorporating both prenatal clinical factors and radiomic signature of placenta on T2WI showed good performance for risk prediction of PPH. The predictive model can identify severe PPH with high sensitivity and can be applied in patients with and without PP. (C) 2019 The Authors. Published by Elsevier B.V.
KeywordRadiomics Placenta accreta spectrum Postpartum haemorrhage Estimated blood loss Magnetic resonance imaging
DOI10.1016/j.ebiom.2019.11.010
WOS KeywordFIGO CONSENSUS GUIDELINES ; INVASIVE PLACENTA ; SCORING MODEL ; RISK-FACTORS ; ACCRETA ; MRI ; TRANSFUSION ; PREGNANCIES ; ULTRASOUND ; DIAGNOSIS
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Plan of China[2016YFA0100900] ; National Key Research and Development Plan of China[2016YFA0100902] ; National Key Research and Development Plan of China[2017YFA0205200] ; National Key Research and Development Plan of China[2017YFE0103600] ; National Natural Science Foundation of China[81922040] ; National Natural Science Foundation of China[81772012] ; National Natural Science Foundation of China[81720108021] ; Beijing Natural Science Foundation[7182109] ; Key Project of Henan Province Medical Science and Technology Project[2018020422] ; Henan Key Scientific and Technological Research Project[192102310360] ; Youth innovation Promotion Association CAS[2019135] ; National Key Research and Development Plan of China[2016YFA0100900] ; National Key Research and Development Plan of China[2016YFA0100902] ; National Key Research and Development Plan of China[2017YFA0205200] ; National Key Research and Development Plan of China[2017YFE0103600] ; National Natural Science Foundation of China[81922040] ; National Natural Science Foundation of China[81772012] ; National Natural Science Foundation of China[81720108021] ; Beijing Natural Science Foundation[7182109] ; Key Project of Henan Province Medical Science and Technology Project[2018020422] ; Henan Key Scientific and Technological Research Project[192102310360] ; Youth innovation Promotion Association CAS[2019135]
Funding OrganizationNational Key Research and Development Plan of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Key Project of Henan Province Medical Science and Technology Project ; Henan Key Scientific and Technological Research Project ; Youth innovation Promotion Association CAS
WOS Research AreaGeneral & Internal Medicine ; Research & Experimental Medicine
WOS SubjectMedicine, General & Internal ; Medicine, Research & Experimental
WOS IDWOS:000503226300037
PublisherELSEVIER
Sub direction classification医学影像处理与分析
Citation statistics
Cited Times:38[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/29456
Collection中国科学院分子影像重点实验室
Corresponding AuthorWen, Zejun; Tian, Jie; Wang, Meiyun
Affiliation1.Henan Univ, Zhengzhou Univ, Peoples Hosp,Dept Med Imaging, Henan Prov Peoples Hosp,Henan Key Lab Neurol Imag, Zhengzhou, Henan, Peoples R China
2.Shanghai Jiao Tong Univ, Sch Biomed Engn, Shanghai, Peoples R China
3.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Beijing, Peoples R China
5.Zhengzhou Univ, Collaborat Innovat Ctr Internet Healthcare, Zhengzhou, Henan, Peoples R China
6.Zhengzhou Univ, Affiliated Hosp 3, Dept Radiol, Zhengzhou, Henan, Peoples R China
7.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China
8.Xidian Univ, Sch Life Sci & Technol, Engn Res Ctr Mol & Neuro Imaging, Minist Educ, Xian, Shanxi, Peoples R China
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wu, Qingxia,Yao, Kuan,Liu, Zhenyu,et al. Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study[J]. EBIOMEDICINE,2019,50:355-365.
APA Wu, Qingxia.,Yao, Kuan.,Liu, Zhenyu.,Li, Longfei.,Zhao, Xin.,...&Wang, Meiyun.(2019).Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study.EBIOMEDICINE,50,355-365.
MLA Wu, Qingxia,et al."Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study".EBIOMEDICINE 50(2019):355-365.
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