External Validation of a Multivariable Prediction Model for Placenta Accreta Spectrum

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

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.

Animal models of the placenta accreta spectrum: current status and further perspectives

Placenta accreta spectrum disorder (PAS) is a kind of disease of placentation defined as abnormal trophoblast invasion of part or all of the placenta into the myometrium, even penetrating the uterus. Decidual deficiency, abnormal vascular remodeling in the maternal-fetal interface, and excessive invasion by extravillous trophoblast (EVT) cells contribute to its onset. However, the mechanisms and signaling pathways underlying such phenotypes are not fully understood, partly due to the lack of suitable experimental animal models. Appropriate animal models will facilitate the comprehensive and systematic elucidation of the pathogenesis of PAS. Due to the remarkably similar functional placental villous units and hemochorial placentation to humans, the current animal models of PAS are based on mice. There are various mouse models induced by uterine surgery to simulate different phenotypes of PAS, such as excessive invasion of EVT or immune disturbance at the maternal-fetal interface, which could define the pathological mechanism of PAS from the perspective of the "soil." Additionally, genetically modified mouse models could be used to study PAS, which is helpful to exploring the pathogenesis of PAS from the perspectives of both "soil" and "seed," respectively. This review details early placental development in mice, with a focus on the approaches of PAS modeling. Additionally, the strengths, limitations and the applicability of each strategy and further perspectives are summarized to provide the theoretical foundation for researchers to select appropriate animal models for various research purposes. This will help better determine the pathogenesis of PAS and even promote possible therapy.