Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2-weighted imaging.
Purpose: To separately perform visual and texture analyses of the axial, coronal, and sagittal planes of T2-weighted images and identify the optimal method for differentiating between the normal placenta and placenta accreta spectrum (PAS). Methods: Eighty consecutive patients (normal group, n = 50;...
| Publicado en: | Abdominal Radiology Vol. 46; no. 11; pp. 5344 - 5353 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2021
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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=152928177&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152928177 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Nov2021 vid: 46 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 152928177 151658785 10.1007/s00261-021-03226-1 152928177 ppf: 5344 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2-weighted imaging. aug: au: Ren, Hainan Mori, Naoko Mugikura, Shunji Shimizu, Hiroaki Kageyama, Sakiko Saito, Masatoshi Takase, Kei affil: Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, 980-8574, Sendai, Japan sug: ab: Purpose: To separately perform visual and texture analyses of the axial, coronal, and sagittal planes of T2-weighted images and identify the optimal method for differentiating between the normal placenta and placenta accreta spectrum (PAS). Methods: Eighty consecutive patients (normal group, n = 50; PAS group, n = 30) underwent preoperative MRI. A scoring system (0–2) was used to evaluate the degree of abnormality observed in visual analysis (bulging, abnormal vascularity, T2 dark band, placental heterogeneity). The axial, coronal, and sagittal planes were manually segmented separately to obtain texture features, and seven combinations were obtained: axial; coronal; sagittal; axial and coronal; axial and sagittal; coronal and sagittal; and axial, coronal, and sagittal. Feature selection using the least absolute shrinkage and selection operator method and model construction using a support vector machine algorithm with k-fold cross-validation were performed. AUC was used to evaluate diagnostic performance. Results: The AUC of visual analysis was 0.75. The model 'coronal and sagittal' had the highest AUC (0.98) amongst the seven combinations. The fivefold cross-validation for the model 'coronal and sagittal' showed AUCs of 0.85 and 0.97 in training and validation sets, respectively. The AUC of the model 'coronal and sagittal' for all subjects was significantly higher than that of visual analysis (0.98 vs. 0.75; p < 0.0001). Conclusion: The model 'coronal and sagittal' can accurately differentiate between the normal placenta and PAS, with a significantly better diagnostic performance than visual analysis. Texture analysis is an optimal method for differentiating between the normal placenta and PAS. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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