Prenatal Diagnosis of Placenta Accreta Spectrum Disorders: Deep Learning Radiomics of Pelvic MRI

• Published in: Journal of magnetic resonance imaging Vol. 59; no. 2; pp. 496 - 509
• Main Authors: Peng, Lulu, Yang, Zehong, Liu, Jue, Liu, Yi, Huang, Jianwei, Chen, Junwei, Su, Yun, Zhang, Xiang, Song, Ting
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.02.2024; Wiley Subscription Services, Inc
• Subjects: Adult; Auditory discrimination; Chi-square test; Correlation coefficient; Correlation coefficients; Datasets; Decision analysis; Deep Learning; Diagnosis; Female; Field strength; Humans; Magnetic Resonance Imaging; Placenta; placenta accreta; Placenta Accreta - diagnostic imaging; Placenta Diseases; Population studies; Pregnancy; Prenatal Diagnosis; Radiomics; Reclassification; Regression analysis; Retrospective Studies; Statistical analysis; Statistical tests; Training; placenta accreta; deep learning; radiomics
• DOI: 10.1002/jmri.28787
• ISSN: 1053-1807; 1522-2586

Background Diagnostic performance of placenta accreta spectrum (PAS) by prenatal MRI is unsatisfactory. Deep learning radiomics (DLR) has the potential to quantify the MRI features of PAS. Purpose To explore whether DLR from MRI can be used to identify pregnancies with PAS. Study Type Retrospective. Population 324 pregnant women (mean age, 33.3 years) suspected PAS (170 training and 72 validation from institution 1, 82 external validation from institution 2) with clinicopathologically proved PAS (206 PAS, 118 non‐PAS). Field Strength/Sequence 3‐T, turbo spin‐echo T2‐weighted images. Assessment The DLR features were extracted using the MedicalNet. An MRI‐based DLR model incorporating DLR signature, clinical model (different clinical characteristics between PAS and non‐PAS groups), and MRI morphologic model (radiologists' binary assessment for the PAS diagnosis) was developed. These models were constructed in the training dataset and then validated in the validation datasets. Statistical Tests The Student t‐test or Mann–Whitney U, χ2 or Fisher exact test, Kappa, dice similarity coefficient, intraclass correlation coefficients, least absolute shrinkage and selection operator logistic regression, multivariate logistic regression, receiver operating characteristic (ROC) curve, DeLong test, net reclassification improvement (NRI) and integrated discrimination improvement (IDI), calibration curve with Hosmer–Lemeshow test, decision curve analysis (DCA). P < 0.05 indicated a significant difference. Results The MRI‐based DLR model had a higher area under the curve than the clinical model in three datasets (0.880 vs. 0.741, 0.861 vs. 0.772, 0.852 vs. 0.675, respectively) or MRI morphologic model in training and independent validation datasets (0.880 vs. 0.760, 0.861, vs. 0.781, respectively). The NRI and IDI were 0.123 and 0.104, respectively. The Hosmer–Lemeshow test had nonsignificant statistics (P = 0.296 to 0.590). The DCA offered a net benefit at any threshold probability. Data Conclusion An MRI‐based DLR model may show better performance in diagnosing PAS than a clinical or MRI morphologic model. Level of Evidence 3 Technical Efficacy Stage 2