Radiomics analysis of T2 -weighted images for differentiating invasive placentas in women at high risks

MR Imaging of Placenta Accreta Spectrum: A Comprehensive Literature Review of the Most Recent Advancements

This article delves into the latest MR imaging developments dedicated to diagnosing placenta accreta spectrum (PAS). PAS, characterized by abnormal placental adherence to the uterine wall, is of paramount concern owing to its association with maternal morbidity and mortality, particularly in high-risk pregnancies featuring placenta previa and prior cesarean sections. Although ultrasound (US) remains the primary screening modality, limitations have prompted heightened emphasis on MR imaging. This review underscores the utility of quantitative MR imaging, especially where US findings prove inconclusive or when maternal body habitus poses challenges, acknowledging, however, that interpreting placenta MR imaging demands specialized training for radiologists.

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.