{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T13:12:16Z","timestamp":1773061936121,"version":"3.50.1"},"reference-count":27,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T00:00:00Z","timestamp":1643587200000},"content-version":"vor","delay-in-days":30,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Xiangyang Science and Technology Bureau: Development","award":["2020ZD01"],"award-info":[{"award-number":["2020ZD01"]}]},{"name":"Xiangyang Science and Technology Bureau: Development","award":["XYY2021Q16"],"award-info":[{"award-number":["XYY2021Q16"]}]},{"name":"Science and Technology Innovation Project of Xiangyang No.1 People\u2019s Hospital","award":["2020ZD01"],"award-info":[{"award-number":["2020ZD01"]}]},{"name":"Science and Technology Innovation Project of Xiangyang No.1 People\u2019s Hospital","award":["XYY2021Q16"],"award-info":[{"award-number":["XYY2021Q16"]}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Clinical Practice"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:p><jats:italic>Aim<\/jats:italic>. This study aimed to investigate the ability of ultrasound\/magnetic resonance imaging (MRI) signature and clinical data\u2010based model for preoperatively predicting the degree of placenta accreta spectrum disorders and develop combined prediction models. <jats:italic>Methods<\/jats:italic>. The clinicopathological characteristics, prenatal ultrasound images, and MRI features of 132 pregnant women with placenta accreta spectrum disorders at Xiangyang No. 1 People\u2019s Hospital were retrospectively reviewed from January 2016 to December 2020. In the training set of 99 patients, the ultrasound\/MRI features model, clinical characteristics model, and combined model were developed by multivariate logistic regression analysis to predict the degree of placenta accreta spectrum disorders. The prediction performance of different models was compared using the Delong test. The developed models were validated by assessing their prediction performance in a test set of 33 patients. <jats:italic>Results<\/jats:italic>. The multivariate logistic regression analysis identified history of abortion, history of endometrial injury, and blurred boundary between the placenta and the myometrium\/between the uterine serosa and the bladder to construct a combined model for predicting the degree of placenta accreta spectrum disorders (area under the curve (AUC)\u2009=\u20090.931; 95% confidence interval (CI): 0.882\u20130.980). The AUC of the clinical characteristics model and ultrasound\/MRI features model was 0.858 (95% CI 0.794\u20130.921) and 0.709 (95% CI 0.624\u20130.798), respectively. The AUC of the combined model was significantly higher than that of the ultrasound\/MRI features model (<jats:italic>P<\/jats:italic> &lt; 0.001) or clinical characteristics model (<jats:italic>P<\/jats:italic> &lt; 0.0015) in the training set. In the test set, the combined model also showed higher prediction performance. <jats:italic>Conclusions<\/jats:italic>. Ultrasound\/MRI\u2010based signature is a powerful predictor for the degree of placenta accreta spectrum disorders in an early stage. A combined model (constructed with history of abortion, history of endometrial injury, and blurred boundary between the placenta and the myometrium\/between the uterine serosa and the bladder) can improve the accuracy for predicting the degree of placenta accreta spectrum disorders in an early stage.<\/jats:p>","DOI":"10.1155\/2022\/9527412","type":"journal-article","created":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T06:44:57Z","timestamp":1644389097000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["USMRI Features and Clinical Data\u2010Based Model for Predicting the Degree of Placenta Accreta Spectrum Disorders and Developing Prediction Models"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0048-2738","authenticated-orcid":false,"given":"Peng","family":"An","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3486-6625","authenticated-orcid":false,"given":"Junyan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0390-5628","authenticated-orcid":false,"given":"Feng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2871-7567","authenticated-orcid":false,"given":"Zhongqiu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6304-7912","authenticated-orcid":false,"given":"Yan","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9672-9431","authenticated-orcid":false,"given":"Xiumei","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,1,31]]},"reference":[{"key":"e_1_2_12_1_2","first-page":"204","article-title":"A case OF placenta accreta","volume":"17","author":"Forster D. 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