Radiomic study of antenatal prediction of severe placenta accreta spectrum from MRI

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dc.contributor.authorBartels, Helena C.en
dc.contributor.authorWolsztynski, Ericen
dc.contributor.authorO'Doherty, Jimen
dc.contributor.authorBrophy, David P.en
dc.contributor.authorMacDermott, Roisinen
dc.contributor.authorAtallah, Daviden
dc.contributor.authorSaliba, Souhaen
dc.contributor.authorEl Kassis, Nadineen
dc.contributor.authorMoubarak, Malaken
dc.contributor.authorYoung, Constanceen
dc.contributor.authorDowney, Paulen
dc.contributor.authorDonnelly, Jenniferen
dc.contributor.authorGeoghegan, Tonyen
dc.contributor.authorBrennan, Donal J.en
dc.contributor.authorCurran, Kathleen M.en
dc.contributor.funderNational Maternity Hospital, Irelanden
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2024-10-10T14:36:34Z
dc.date.available2024-10-10T14:36:34Z
dc.date.issued2024-08-17en
dc.description.abstractObjectives: We previously demonstrated the potential of radiomics for the prediction of severe histological placenta accreta spectrum (PAS) subtypes using T2-weighted MRI. We aim to validate our model using an additional dataset. Secondly, we explore whether the performance is improved using a new approach to develop a new multivariate radiomics model. Methods: Multi-centre retrospective analysis was conducted between 2018 and 2023. Inclusion criteria: MRI performed for suspicion of PAS from ultrasound, clinical findings of PAS at laparotomy and/or histopathological confirmation. Radiomic features were extracted from T2-weighted MRI. The previous multivariate model was validated. Secondly, a 5-radiomic feature random forest classifier was selected from a randomized feature selection scheme to predict invasive placenta increta PAS cases. Prediction performance was assessed based on several metrics including area under the curve (AUC) of the receiver operating characteristic curve (ROC), sensitivity, and specificity. Results: We present 100 women [mean age 34.6 (±3.9) with PAS], 64 of whom had placenta increta. Firstly, we validated the previous multivariate model and found that a support vector machine classifier had a sensitivity of 0.620 (95% CI: 0.068; 1.0), specificity of 0.619 (95% CI: 0.059; 1.0), an AUC of 0.671 (95% CI: 0.440; 0.922), and accuracy of 0.602 (95% CI: 0.353; 0.817) for predicting placenta increta. From the new multivariate model, the best 5-feature subset was selected via the random subset feature selection scheme comprised of 4 radiomic features and 1 clinical variable (number of previous caesareans). This clinical-radiomic model achieved an AUC of 0.713 (95% CI: 0.551; 0.854), accuracy of 0.695 (95% CI 0.563; 0.793), sensitivity of 0.843 (95% CI 0.682; 0.990), and specificity of 0.447 (95% CI 0.167; 0.667). Conclusion: We validated our previous model and present a new multivariate radiomic model for the prediction of severe placenta increta from a well-defined, cohort of PAS cases. Advances in knowledge: Radiomic features demonstrate good predictive potential for identifying placenta increta. This suggests radiomics may be a useful adjunct to clinicians caring for women with this high-risk pregnancy condition.en
dc.description.sponsorshipNational Maternity Hospital, Ireland (Medical Funden
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleidtqae164en
dc.identifier.citationBartels, H.C., Wolsztynski, E., O’Doherty, J., Brophy, D.P., MacDermott, R., Atallah, D., Saliba, S., El Kassis, N., Moubarak, M., Young, C., Downey, P., Donnelly, J., Geoghegan, T., Brennan, D.J. and Curran, K.M. (2024) ‘Radiomic study of antenatal prediction of severe placenta accreta spectrum from MRI’, British Journal of Radiology, p. tqae164. Available at: https://doi.org/10.1093/bjr/tqae164en
dc.identifier.doihttps://doi.org/10.1093/bjr/tqae164en
dc.identifier.endpage10en
dc.identifier.issn0007-1285en
dc.identifier.issn1748-880Xen
dc.identifier.journaltitleBritish Journal of Radiologyen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/16512
dc.language.isoenen
dc.publisherOxford University Pressen
dc.relation.ispartofBritish Journal of Radiologyen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.rights© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectPlacenta accreta spectrumen
dc.subjectRadiomicsen
dc.subjectMachine learningen
dc.subjectMRIen
dc.subjectPregnancyen
dc.titleRadiomic study of antenatal prediction of severe placenta accreta spectrum from MRIen
dc.typeArticle (peer-reviewed)en
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