Affiliation:
1. School of Computer Science Peking University Beijing China
2. Department of Radiology Peking University Third Hospital Beijing China
3. School of Software and Microelectronics Peking University Beijing China
4. Department of Obstetrics and Gynecology Peking University Third Hospital Beijing China
5. National Engineering Research Center for Software Engineering Peking University Beijing China
6. Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of Education Beijing China
7. National Clinical Research Center for Obstetrics and Gynecology Beijing China
Abstract
BackgroundPreoperative assessment of adverse outcomes risk in placenta accreta spectrum (PAS) disorders is of high clinical relevance for perioperative management and prognosis.PurposeTo investigate the association of preoperative MRI multisequence images and adverse pregnancy outcomes by establishing a deep learning model in patients with PAS.Study TypeRetrospective.Population323 pregnant women (age from 20 to 46, the median age is 33), suspected of PAS, underwent MRI to assess the PAS, divided into the training (N = 227) and validation datasets (N = 96).Field Strength/Sequence1.5T scanner/fast imaging employing steady‐state acquisition sequence and single shot fast spin echo sequence.AssessmentDifferent deep learning models (i.e., with single MRI input sequence/two sequences/multisequence) were compared to assess the risk of adverse pregnancy outcomes, which defined as intraoperative bleeding ≥1500 mL and/or hysterectomy. Net reclassification improvement (NRI) was used for quantitative comparison of assessing adverse pregnancy outcome between different models.Statistical TestsThe AUC, sensitivity, specificity, and accuracy were used for evaluation. The Shapiro–Wilk test and t‐test were used. A P value of <0.05 was considered statistically significant.Results215 cases were invasive placenta accreta (67.44% of them with adverse outcomes) and 108 cases were non‐invasive placenta accreta (9.25% of them with adverse outcomes). The model with four sequences assessed adverse pregnancy outcomes with AUC of 0.8792 (95% CI, 0.8645–0.8939), with ACC of 85.93% (95%, 84.43%–87.43%), with SEN of 86.24% (95% CI, 82.46%–90.02%), and with SPC of 85.62% (95%, 82.00%–89.23%) on the test cohort. The performance of model with four sequences improved above 0.10 comparing with that of model with two sequences and above 0.20 comparing with that of model with single sequence in terms of NRI.Data ConclusionThe proposed model showed good diagnostic performance for assessing adverse pregnancy outcomes.Level of Evidence3Technical EfficacyStage 2
Funder
National Key Research and Development Program of China
Beijing Nova Program
National Natural Science Foundation of China
Subject
Radiology, Nuclear Medicine and imaging
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