Prediction of placenta accreta spectrum in patients with placenta previa using a clinical, US and MRI combined model: A retrospective study with external validation

Perspective in diagnostic accuracy of prenatal ultrasound and MRI for placenta accreta

PURPOSE

Placenta accreta (PA) significantly increases the risk of life-threatening maternal outcomes, and its rising prevalence, driven by the increase in cesarean deliveries, underscores the need for precise diagnostic tools to improve clinical management and outcomes. This study aims to evaluate the advanced diagnostic capabilities of prenatal ultrasound and magnetic resonance imaging (MRI) in the detection of PA, a severe obstetric complication characterized by abnormal adherence of the placenta to the myometrium.

MATERIALS AND METHODS

The study utilized a review of current literature and clinical studies to assess the diagnostic accuracy and clinical utility of ultrasound and MRI in identifying PA. Both imaging modalities were evaluated for their ability to assess the depth and extent of placental invasion, as well as their complementary roles in prenatal diagnosis. The experimental system included detailed imaging protocols for ultrasound and MRI, focusing on placental and uterine structures, and their application in real-world clinical settings.

RESULTS

The findings demonstrate that ultrasound and MRI are highly effective in diagnosing PA, with each modality offering unique advantages. Ultrasound is widely accessible and serves as the first-line diagnostic tool, providing detailed visualization of placental adherence and vascular patterns. MRI, on the other hand, offers superior soft tissue contrast and is particularly valuable in complex cases or when ultrasound findings are inconclusive. Together, these imaging techniques enable accurate evaluation of placental invasion, facilitating timely and targeted prenatal interventions. The study also highlights the potential for improved maternal and fetal outcomes through early diagnosis and optimized pregnancy management.

CONCLUSIONS

Prenatal ultrasound and MRI are indispensable tools in the diagnosis and management of placenta accreta, offering complementary insights that enhance diagnostic precision. Their combined use allows for detailed assessment of placental and uterine structures, guiding clinical decision-making and improving outcomes for both mothers and infants. Future advancements in imaging technology and research hold promise for further enhancing diagnostic accuracy and expanding clinical applications, ultimately contributing to safer and more effective care for patients with PA.

Ultrasound versus magnetic resonance imaging features in diagnosing placenta accreta: A systematic review and meta-analysis

OBJECTIVE

The purpose of this study is to conduct a complete analysis of the accuracy of ultrasound and MRI in detecting placenta accreta spectrum (PAS) disorders, as well as to investigate the accuracy of independent imaging findings in these diseases.

METHODS

Pubmed, Web of Science, Embase, The Cochrane Library, and Google Scholar databases were searched from their establishment to January 1, 2025. Included were all studies that used both ultrasonography and MRI to diagnose pregnant women with PAS disorder. The ability of ultrasonography, MRI, and their independent features to diagnose PAS was evaluated using pooled sensitivity, specificity, diagnostic odds ratio (DOR), and receiver operating curves (ROC). Heterogeneity was calculated using Cochran Q and I2 statistics, and the sources of heterogeneity were investigated using meta-regression and subgroup analysis.

RESULTS

Following a series of rigorous assessments, the meta-analysis comprised 1989 pregnant women from 30 studies. The sensitivity and specificity of ultrasonography for the diagnosis of PAS were 0.87 (95 % CI, 0.82-0.90) and 0.83 (95 % CI, 0.77-0.88), respectively, whereas the sensitivity and specificity of MRI for the same diagnostic were 0.87 (95 % CI, 0.82-0.90) and 0.84 (95 % CI, 0.80-0.88). Intraplacental lacunae has the best diagnostic accuracy of ultrasound, while placental bulge has the highest diagnostic accuracy of MRI, with their area under the curve (AUC) of the ROC being 0.76 (95 % CI, 0.72-0.79) and 0.89 (95 % CI, 0.85-0.91), respectively.

CONCLUSION

The diagnostic accuracy of ultrasound and MRI for PAS was similar. However, radiographic findings should not be utilized to make an independent diagnosis of PAS disorders.

Identification and stratification of placenta percreta with gynecologic oncologist management

OBJECTIVE

Gynecologic oncologist involvement in the surgical team of patients with placenta percreta has shown improved patient outcomes. Yet, stratification of cases is dependent on identification of placenta percreta by ultrasonography which has a poor detection rate. To allow patients to receive optimal team management by pre-operative stratification our objective was to identify the pre-operative characteristics of patients with previously underdiagnosed placenta percreta.

METHODS

A retrospective single institution case-control study was performed from January 2010 to December 2022 of singleton, non-anomalous pregnancies with suspicion for placenta accreta spectrum (PAS). Ultrasonography was used as the primary method of detection. Final inclusion was dependent on histology confirmation of PAS and degree of invasion. We explored the role of concurrent antenatal magnetic resonance imaging (MRI) on patients with previously unrecognized placenta percreta.

RESULTS

During the 13 year study period, 140 cases of histologically confirmed PAS were managed by our team and met inclusion criteria. A total of 72 (51.4%) cases were for placenta percreta and 27 (37.5%) of these were diagnosed pre-operatively while 45 (62.5%) were only diagnosed post-operatively. Comparison between these two groups revealed patient body mass index (BMI) >30 kg/m2 was independently associated with unrecognized placenta percreta (p=0.006). No findings by MRI were associated with mischaracterization of placenta percreta. Yet, concurrent MRI assessment of patients with BMI >30 kg/m2 (n=18), increased placenta percreta detection by 11 cases (61%).

CONCLUSION

The ability to determine pre-operatively which patients are more likely to have placenta percreta allows for gynecologic oncologists to be involved in the most complex cases in a planned manner. This study shows that women at risk for placenta accreta spectrum, who are obese (BMI >30 kg/m2), may benefit from further assessment with pre-operative MRI to facilitate appropriate staffing and team availability for cases of placenta percreta.

Radiomic study of antenatal prediction of severe placenta accreta spectrum from MRI

OBJECTIVES

We previously demonstrated the potential of radiomics for the prediction of severe histological placenta accreta spectrum (PAS) subtypes using T2-weighted MRI. We aim to validate our model using an additional dataset. Secondly, we explore whether the performance is improved using a new approach to develop a new multivariate radiomics model.

METHODS

Multi-centre retrospective analysis was conducted between 2018 and 2023. Inclusion criteria: MRI performed for suspicion of PAS from ultrasound, clinical findings of PAS at laparotomy and/or histopathological confirmation. Radiomic features were extracted from T2-weighted MRI. The previous multivariate model was validated. Secondly, a 5-radiomic feature random forest classifier was selected from a randomized feature selection scheme to predict invasive placenta increta PAS cases. Prediction performance was assessed based on several metrics including area under the curve (AUC) of the receiver operating characteristic curve (ROC), sensitivity, and specificity.

RESULTS

We present 100 women [mean age 34.6 (±3.9) with PAS], 64 of whom had placenta increta. Firstly, we validated the previous multivariate model and found that a support vector machine classifier had a sensitivity of 0.620 (95% CI: 0.068; 1.0), specificity of 0.619 (95% CI: 0.059; 1.0), an AUC of 0.671 (95% CI: 0.440; 0.922), and accuracy of 0.602 (95% CI: 0.353; 0.817) for predicting placenta increta. From the new multivariate model, the best 5-feature subset was selected via the random subset feature selection scheme comprised of 4 radiomic features and 1 clinical variable (number of previous caesareans). This clinical-radiomic model achieved an AUC of 0.713 (95% CI: 0.551; 0.854), accuracy of 0.695 (95% CI 0.563; 0.793), sensitivity of 0.843 (95% CI 0.682; 0.990), and specificity of 0.447 (95% CI 0.167; 0.667).

CONCLUSION

We validated our previous model and present a new multivariate radiomic model for the prediction of severe placenta increta from a well-defined, cohort of PAS cases.

ADVANCES IN KNOWLEDGE

Radiomic features demonstrate good predictive potential for identifying placenta increta. This suggests radiomics may be a useful adjunct to clinicians caring for women with this high-risk pregnancy condition.

External validation of and improvement upon a model for the prediction of placenta accreta spectrum severity using prospectively collected multicenter ultrasound data

INTRODUCTION

This study aimed to validate the Sargent risk stratification algorithm for the prediction of placenta accreta spectrum (PAS) severity using data collected from multiple centers and using the multicenter data to improve the model.

MATERIAL AND METHODS

We conducted a multicenter analysis using data collected for the IS-PAS database. The Sargent model's effectiveness in distinguishing between abnormally adherent placenta (FIGO grade 1) and abnormally invasive placenta (FIGO grades 2 and 3) was evaluated. A new model was developed using multicenter data from the IS-PAS database.

RESULTS

The database included 315 cases of suspected PAS, of which 226 had fully documented standardized ultrasound signs. The final diagnosis was normal placentation in 5, abnormally adherent placenta/FIGO grade 1 in 43, and abnormally invasive placenta/FIGO grades 2 and 3 in 178. The external validation of the Sargent model revealed moderate predictive accuracy in a multicenter setting (C-index 0.68), compared to its higher accuracy in a single-center context (C-index 0.90). The newly developed model achieved a C-index of 0.74.

CONCLUSIONS

The study underscores the difficulty in developing universally applicable PAS prediction models. While models like that of Sargent et al. show promise, their reproducibility varies across settings, likely due to the interpretation of the ultrasound signs. The findings support the need for updating the current ultrasound descriptors and for the development of any new predictive models to use data collected by different operators in multiple clinical settings.

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.

A novel MRI-based diagnostic model for predicting placenta accreta spectrum

Objective To develop and evaluate a diagnostic model based on MRI signs for predicting placenta accreta spectrum. Materials and Methods A total of 155 pregnant women were included in this study, randomly divided into 104 cases in the training set and 51 cases in the validation set. There were 93 Non-PAS cases, and 62 cases in the PAS group. The training set included 62 Non-PAS cases and 42 PAS cases. Clinical factors and MRI signs were collected for univariate analysis. Then, binary logistic regression analysis was used to develop independent diagnostic models with clinical relevant risk factors or MRI signs, as well as those combining clinical risk factors and MRI signs. The ROC curve analysis was used to evaluate the diagnostic performance of each diagnostic model. Finally, the validation was performed with the validation set. Results In the training set, four clinical factors (gestity, parity, uterine surgery history, placental position) and 11 MRI features (T2-dark bands, placental bulge, T2 hypointense interface loss, myometrial thinning, bladder wall interruption, focal exophytic mass, abnormal placental bed vascularization, placental heterogeneity, asymmetric placental thickening/shape, placental ischemic infarction, abnormal intraplacental vascularity) were considered as risk factors for PAS. The AUC of the clinical diagnostic model, MRI diagnostic model, and clinical + MRI model of PAS were 0.779, 0.854, and 0.874, respectively. In the validation set, the AUC of the clinical diagnostic model, MRI diagnostic model, and clinical + MRI model of PAS were 0.655, 0.728, and 0.735, respectively. Conclusion Diagnosis model based on MRI features in this study can well predict placenta accreta spectrum.

The value of the combined MR imaging features and clinical factors Nomogram model in predicting intractable postpartum hemorrhage due to placenta accreta

To explore the value of the combined MR imaging features and clinical factors Nomogram model in predicting intractable postpartum hemorrhage (IPH) due to placenta accreta (PA). We conducted a retrospective study with 270 cases of PA patients admitted to our hospital from January 2015 to December 2022. The clinical data of these patients were analyzed, and they were divided into 2 groups: the IPH group and the non-IPH group based on the presence of IPH. The differences in data between the 2 groups were compared, and the risk factors for IPH were analyzed. A Nomogram model was constructed using independent high-risk factors, and the predictive value of this model for IPH was analyzed. The results of multivariable binary Logistic regression analysis showed higher number of cesareans, placenta previa, placenta accreta type (implantation, penetration), low signal strip on T2 weighted image (T2WI) were independent high-risk factor for IPH (P < .05). ROC analysis and Hosmer-Lemeshow goodness-of-fit test showed the Nomogram predictive model constructed with the high-risk factor has good discrimination and calibration. Decision curve analysis (DCA) showed that when the probability threshold for the Nomogram model's prediction was in the range from 0.125 to 0.99, IPH patients could obtain more net benefits, making it suitable for clinical application. The higher number of cesareans, placenta previa, placental accreta type (implantation, penetration), and low signal strip on T2WI are independent high-risk factor for IPH. The Nomogram predictive model constructed with the high-risk factor demonstrates good clinical efficacy in predicting the occurrence of IPH due to PA.