Identification of suspicious invasive placentation based on clinical MRI data using textural features and automated machine learning.
Objective: The aim of this study was to investigate whether intraplacental texture features from routine placental MRI can objectively and accurately predict invasive placentation.Material and Methods: This retrospective study includes 99 pregnant women with pathologically confirmed placental invasi...
| Publicado en: | European Radiology Vol. 29; no. 11; pp. 6152 - 6163 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Nov2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=139163699&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139163699 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Nov2019 vid: 29 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 139163699 139163699 NLM31444599 139163699 10.1007/s00330-019-06372-9 NLM31444599 139163699 ppf: 6152 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Identification of suspicious invasive placentation based on clinical MRI data using textural features and automated machine learning. aug: au: Sun, Huaiqiang Qu, Haibo Chen, Lu Wang, Wei Liao, Yi Zou, Ling Zhou, Ziyi Wang, Xiaodong Zhou, Shu affil: Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China sug: subj: Magnetic Resonance Imaging Methods Placenta Praevia Diagnosis Pregnancy Physiology Placenta Pathology Prenatal Diagnosis Methods Algorithms Female Pregnancy Young Adult Adult Retrospective Design Funding Source Human Adult: 19-44 years Female ab: Objective: The aim of this study was to investigate whether intraplacental texture features from routine placental MRI can objectively and accurately predict invasive placentation.Material and Methods: This retrospective study includes 99 pregnant women with pathologically confirmed placental invasion and 56 pregnant women with simple placenta previa. All participants underwent magnetic resonance imaging after 24 gestational weeks. The placenta was segmented in sagittal images from both turbo spin echo (TSE) and balanced turbo field echo (bTFE) sequences. Textural features were extracted from the both original and Laplacian of Gaussian (LoG)-filtered MRI images. An automated machine learning algorithm was applied to the extracted feature sets to obtain the optimal preprocessing steps, classification algorithm, and corresponding hyper-parameters.Results: A gradient boosting classifier using all textual features from original and LoG-filtered TSE images and bTFE images identified by the automated machine learning algorithm achieved the optimal performance with sensitivity, specificity, accuracy, and area under ROC curve (AUC) of 100%, 88.5%, 95.2%, and 0.98 in the prediction of placental invasion. In addition, textural features that contributed to the prediction of placental invasion differ from the features significantly affected by normal placenta maturation.Conclusions: Quantifying intraplacental heterogeneity using LoG filtration and texture analysis highlights the different heterogeneous appearance caused by abnormal placentation relative to normal maturation. The predictive model derived from automated machine learning yielded good performance, indicating the proposed radiomic analysis pipeline can accurately predict placental invasion and facilitate clinical decision-making for pregnant women with suspicious placental invasion.Key Points: • The intraplacental texture features have high efficiency in prediction of invasive placentation after 24 gestational weeks. • The features with dominated predictive power did not overlap with the features significantly affected by gestational age. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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