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.

A Deep Learning Pipeline Using Prior Knowledge for Automatic Evaluation of Placenta Accreta Spectrum Disorders With MRI

BACKGROUND

The diagnosis of prenatal placenta accreta spectrum (PAS) with magnetic resonance imaging (MRI) is highly dependent on radiologists' experience. A deep learning (DL) method using the prior knowledge that PAS-related signs are generally found along the utero-placental borderline (UPB) may help radiologists, especially those with less experience, to mitigate this issue.

PURPOSE

To develop a DL tool for antenatal diagnosis of PAS using T2-weighted MR images.

STUDY TYPE

Retrospective.

SUBJECTS

Five hundred and forty pregnant women with clinically suspected PAS disorders from two institutions, divided into training (409), internal test (103), and external test (28) datasets.

FIELD STRENGTH/SEQUENCE

Sagittal T2-weighted fast spin echo sequence at 1.5 T and 3 T.

ASSESSMENT

An nnU-Net was trained for placenta segmentation. The UPB straightening approach was used to extract the utero-placental boundary region. The UPB image was then fed into DenseNet-PAS for PAS diagnosis. DenseNet-PP learnt placental position information to improve the PAS diagnosis performance. Three radiologists with 8, 10, and 12 years of experience independently evaluated the images. Two radiologists marked the placenta tissue. Histopathological findings were the reference standard.

STATISTICAL TESTS

Area under the curve (AUC) was used to evaluate the classification. Dice coefficient evaluated the segmentation between radiologists and the model performance. The Mann-Whitney U-test or the chi-squared test assessed the significance of differences. Decision curve analysis was used to determine clinical effectiveness. DeLong's test was used to compare AUCs.

RESULTS

Of the 540 patients, 170 had PAS disorders confirmed by histopathology. The DL model using UPB images and placental position yielded the highest AUC of 0.860 and 0.897 in internal test and external test cohorts, respectively, significantly exceeding the performance of three radiologists (internal test AUC, 0.737-0.770).

DATA CONCLUSION

By extracting the UPB image, this fully automatic DL pipeline achieved high accuracy and may assist radiologists in PAS diagnosis using MRI.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

A Multicenter, Retrospective Comparison Study of Pregnancy Outcomes According to Placental Location in Placenta Previa

Background: We investigated the association between placental location and pregnancy outcomes in placenta previa. Methods: This multi-center retrospective study enrolled 781 women who delivered between May 1999 and February 2020. We divided the dataset into anterior (n = 209) and posterior (n = 572) groups and compared the baseline characteristics and obstetric and neonatal outcomes. The adverse obstetric outcomes associated with placenta location were evaluated using a multivariate logistic analysis. Results: Gestational age at delivery in the anterior group (253.0 ± 21.6) was significantly lower than that in the posterior group (257.6 ± 19.1) (p = 0.008). The anterior group showed significantly higher parity, rates of previous cesarean section, non-vertex fetal positions, admissions for bleeding, emergency cesarean sections, transfusions, estimated blood loss, and combined placenta accrete spectrum (p < 0.05). In the multivariate analysis, the anterior group had higher rates of transfusion (OR 2.23; 95% CI 1.50-3.30), placenta accreta spectrum (OR 2.16; 95% CI 1.21-3.97), and non-vertex fetal positions (OR 2.47; 95% CI 1.09-5.88). Conclusions: These findings suggest that more caution is required in the treatment of patients with anterior placenta previa. Therefore, if placenta previa is diagnosed prenatally, it is important to determine the location of the body and prepare for massive bleeding in the anterior group.

Features of MR signals of retroplacental basal decidual space and its diagnostic significance

BACKGROUND

With more pregnant women undergoing cesarean section, the number of women with scarring in the uterus undergoing uterine magnetic resonance (MR) examination in the second and third trimesters following a subsequent pregnancy, has increased.

OBJECTIVE

To investigate features of MR signals in retroplacental basal decidual space.

METHODS

The MR imaging data of patients with clinically and pathologically confirmed placenta implantation and complete placental abruption were retrospectively analyzed.

RESULTS

Patients with high-intensity signals in T2-weighted images (T2WI) of the retroplacental basal decidual space did not suffer placenta implantation after delivery, while high-intensity signals in T2WI of the retroplacental basal decidual space was not observed in patients with different degrees of placenta implantation.

CONCLUSION

As the retroplacental basal decidual space is the barrier between the placenta and myometrium, high-intensity signals in T2WI can improve the confidence of MR exclusion diagnostics of placenta implantation, and can be used as exclusion criteria for MR diagnosis of placenta implantation.

Radiomics-based prediction of FIGO grade for placenta accreta spectrum

BACKGROUND

Placenta accreta spectrum (PAS) is a rare, life-threatening complication of pregnancy. Predicting PAS severity is critical to individualise care planning for the birth. We aim to explore whether radiomic analysis of T2-weighted magnetic resonance imaging (MRI) can predict severe cases by distinguishing between histopathological subtypes antenatally.

METHODS

This was a bi-centre retrospective analysis of a prospective cohort study conducted between 2018 and 2022. Women who underwent MRI during pregnancy and had histological confirmation of PAS were included. Radiomic features were extracted from T2-weighted images. Univariate regression and multivariate analyses were performed to build predictive models to differentiate between non-invasive (International Federation of Gynecology and Obstetrics [FIGO] grade 1 or 2) and invasive (FIGO grade 3) PAS using R software. Prediction performance was assessed based on several metrics including sensitivity, specificity, accuracy and area under the curve (AUC) at receiver operating characteristic analysis.

RESULTS

Forty-one women met the inclusion criteria. At univariate analysis, 0.64 sensitivity (95% confidence interval [CI] 0.0-1.00), specificity 0.93 (0.38-1.0), 0.58 accuracy (0.37-0.78) and 0.77 AUC (0.56-.097) was achieved for predicting severe FIGO grade 3 PAS. Using a multivariate approach, a support vector machine model yielded 0.30 sensitivity (95% CI 0.18-1.0]), 0.74 specificity (0.38-1.00), 0.58 accuracy (0.40-0.82), and 0.53 AUC (0.40-0.85).

CONCLUSION

Our results demonstrate a predictive potential of this machine learning pipeline for classifying severe PAS cases.

RELEVANCE STATEMENT

This study demonstrates the potential use of radiomics from MR images to identify severe cases of placenta accreta spectrum antenatally.

KEY POINTS

• Identifying severe cases of placenta accreta spectrum from imaging is challenging. • We present a methodological approach for radiomics-based prediction of placenta accreta. • We report certain radiomic features are able to predict severe PAS subtypes. • Identifying severe PAS subtypes ensures safe and individualised care planning for birth.

Imaging of Acute Abdominopelvic Pain in Pregnancy and Puerperium-Part I: Obstetric (Non-Fetal) Complications

Acute abdominopelvic pain in pregnant and postpartum patients presents clinical and therapeutic challenges, often requiring quick and accurate imaging diagnosis. Ultrasound remains the primary imaging investigation. Magnetic resonance imaging (MRI) has been shown to be a powerful diagnostic tool in the setting of acute abdominal pain during pregnancy and puerperium. MRI overcomes some drawbacks of US, avoiding the ionizing radiation exposure of a computed tomography (CT) scan. Although CT is not usually appropriate in pregnant patients, it is crucial in the emergency evaluation of postpartum complications. The aim of this article is to provide radiologists with a thorough familiarity with the common and uncommon pregnancy and puerperium abdominal emergencies by illustrating their imaging appearances. The present first section will review and discuss the imaging findings for acute abdominopelvic pain of obstetric (non-fetal) etiology.

Prediction of placenta accreta spectrum with nomogram combining radiomic and clinical factors: A novel developed and validated integrative model

OBJECTIVE

To develop and validate a clinicoradiomic nomogram based on sagittal T2WI images to predict placenta accreta spectrum (PAS).

METHODS

Between October 2016 and April 2022, women suspected of PAS by ultrasound were enrolled. After taking into account exclusion criteria, 132 women were retrospectively included in the study. The variance threshold SelectKBest and the least absolute shrinkage and selection operator were applied to select radiomic features, which was further used to calculate the Rad-score. Multivariable logistic regression was used to screen clinical factor.

RESULTS

Based on 13 radiomic features, five radiomic models were constructed. A clinical factor of intraplacental T2-hypointense bands was obtained by multivariate logistic regression. The area under the curve (AUC) value of the stochastic gradient descent (SGD) radiomic model was 0.82 in the training cohort and 0.78 in the test cohort. After adding clinical factors to the SGD radiomic model, the AUC value of the clinicoradiomic model was significantly increased from 0.82 and 0.78 to 0.84 in both the training and test cohorts. The nomogram of the clinicoradiomic model was constructed, which had good performance verified by calibration and a decision curve.

CONCLUSION

The presented nomogram could be useful for predicting PAS.

Efficacy of resuscitative endovascular balloon occlusion of the aorta for hemorrhage control in patients with abnormally invasive placenta: a historical cohort study

BACKGROUND

Patients with abnormally invasive placenta (AIP) are at high risk of massive postpartum hemorrhage. Resuscitative endovascular balloon occlusion of the aorta (REBOA), as an adjunct therapeutic strategy for hemostasis, offers the obstetrician an alternative for treating patients with AIP. This study aimed to evaluate the role of REBOA in hemorrhage control in patients with AIP.

METHODS

This was a historical cohort study with prospectively collected data between January 2014 to July 2021 at a single tertiary center. According to delivery management, 364 singleton pregnant AIP patients desiring uterus preservation were separated into two groups. The study group (balloon group, n = 278) underwent REBOA during cesarean section, whereas the reference group (n = 86) did not undergo REBOA. Surgical details and maternal outcomes were collected. The primary outcome was estimated blood loss and the rate of uterine preservation.

RESULTS

A total of 278 (76.4%) participants experienced REBOA during cesarean section. The patients in the balloon group had a smaller blood loss during cesarean Sect. (1370.5 [752.0] ml vs. 3536.8 [1383.2] ml; P < .001) and had their uterus salvaged more often (264 [95.0%] vs. 23 [26.7%]; P < .001). These patients were also less likely to be admitted to the intensive care unit after delivery (168 [60.4%] vs. 67 [77.9%]; P = .003) and had a shorter operating time (96.3 [37.6] min vs. 160.6 [45.5] min; P < .001). The rate of neonatal intensive care unit admission (176 [63.3%] vs. 52 [60.4%]; P = .70) and total maternal medical costs ($4925.4 [1740.7] vs. $5083.2 [1705.1]; P = .13) did not differ between the two groups.

CONCLUSIONS

As a robust hemorrhage-control technique, REBOA can reduce intraoperative hemorrhage in patients with AIP. The next step is identifying associated risk factors and defining REBOA inclusion criteria to identify the subgroups of AIP patients who may benefit more.

Influence of maternal psychological distress during COVID-19 pandemic on placental morphometry and texture

The Coronavirus Disease 2019 (COVID-19) pandemic has been accompanied by increased prenatal maternal distress (PMD). PMD is associated with adverse pregnancy outcomes which may be mediated by the placenta. However, the potential impact of the pandemic on in vivo placental development remains unknown. To examine the impact of the pandemic and PMD on in vivo structural placental development using advanced magnetic resonance imaging (MRI), acquired anatomic images of the placenta from 63 pregnant women without known COVID-19 exposure during the pandemic and 165 pre-pandemic controls. Measures of placental morphometry and texture were extracted. PMD was determined from validated questionnaires. Generalized estimating equations were utilized to compare differences in PMD placental features between COVID-era and pre-pandemic cohorts. Maternal stress and depression scores were significantly higher in the pandemic cohort. Placental volume, thickness, gray level kurtosis, skewness and run length non-uniformity were increased in the pandemic cohort, while placental elongation, mean gray level and long run emphasis were decreased. PMD was a mediator of the association between pandemic status and placental features. Altered in vivo placental structure during the pandemic suggests an underappreciated link between disturbances in maternal environment and perturbed placental development. The long-term impact on offspring is currently under investigation.

MRI-based radiomics nomogram in patients with high-risk placenta accreta spectrum: can it aid in the prenatal diagnosis of intraoperative blood loss?

PURPOSE

To develop and validate the nomogram by combining MRI-derived radiomics and clinical features for preoperatively predicting massive intraoperative blood loss (IBL) in high-risk placenta accreta spectrum (PAS) patients.

METHODS

A total of 152 high-risk PAS patients from Hospital A were enrolled and constituted the training cohort, and 64 patients from Hospital B constituted the validation cohort. Clinical features were analyzed retrospectively. Placental regions of interest were manually positioned on sagittal T₂-weighted HASTE images for each patient to extract quantitative radiomics features. Clinical model, radiomics model, and nomogram were built to predict the risk of massive IBL. The diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC) and the DeLong test. Decision curve analysis (DCA) was performed to determine the performance of the best predictive model.

RESULTS

The nomogram (AUC = 0.866 and 0.876, respectively) and radiomics model (AUC = 0.821 and 0.855, respectively) outperformed the clinical model (AUC = 0.685 and 0.619, respectively) both in the training and validation cohorts (Delong test, P < 0.05). Furthermore, the nomogram performed best with an accuracy of 0.844, sensitivity of 0.882, and specificity of 0.830 for differentiating massive IBL in the validation cohort. DCA confirmed the clinical utility of the nomogram.

CONCLUSION

The nomogram can be used to noninvasively predict massive IBL patients and guide obstetricians to make reasonable preoperative treatment plans.

Prediction of placenta accreta spectrum by combining deep learning and radiomics using T2WI: a multicenter study

PURPOSE

To achieve prenatal prediction of placenta accreta spectrum (PAS) by combining clinical model, radiomics model, and deep learning model using T2-weighted images (T2WI), and to objectively evaluate the performance of the prediction through multicenter validation.

METHODS

A total of 407 pregnant women from two centers undergoing preoperative magnetic resonance imaging (MRI) were retrospectively recruited. The patients from institution I were divided into a training cohort (n = 298) and a validation cohort (n = 75), while patients from institution II served as the external test cohort (n = 34). In this study, we built a clinical prediction model using patient clinical data, a radiomics model based on selected key features, and a deep learning model by mining deep semantic features. Based on this, we developed a combined model by ensembling the prediction results of the three models mentioned above to achieve prenatal prediction of PAS. The performance of these predictive models was evaluated with respect to discrimination, calibration, and clinical usefulness.

RESULTS

The combined model achieved AUCs of 0.872 (95% confidence interval, 0.843 to 0.908) in the validation cohort and 0.857 (0.808 to 0.894) in the external test cohort, both of which outperformed the other models. The calibration curves demonstrated excellent consistency in the validation cohort and the external test cohort, and the decision curves indicated high clinical usefulness.

CONCLUSION

By using preoperative clinical information and MRI images, the combined model can accurately predict PAS by ensembling clinical model, radiomics model, and deep learning model.

MRI-radiomics-clinical-based nomogram for prenatal prediction of the placenta accreta spectrum disorders

OBJECTIVES

To investigate whether an MRI-radiomics-clinical-based nomogram can be used to prenatal predict the placenta accreta spectrum (PAS) disorders.

METHODS

The pelvic MR images and clinical data of 156 pregnant women with pathologic-proved PAS (PAS group) and 115 pregnant women with no PAS (non-PAS group) identified by clinical and prenatal ultrasonic examination were retrospectively collected from two centers. These pregnancies were divided into a training (n = 133), an independent validation (n = 57), and an external validation (n = 81) cohort. Radiomic features were extracted from images of transverse oblique T2-weighted imaging. A radiomics signature was constructed. A nomogram, composed of MRI morphological findings, radiomic features, and prenatal clinical characteristics, was developed. The discrimination and calibration of the nomogram were conducted to assess its performance.

RESULTS

A radiomics signature, including three PAS-related features, was associated with the presence of PAS in the three cohorts (p < 0.001 to p = 0.001). An MRI-radiomics-clinical nomogram incorporating radiomics signature, two prenatal clinical features, and two MRI morphological findings was developed, yielding a higher area under the curve (AUC) than that of the MRI morphological-determined PAS in the training cohort (0.89 vs. 0.78; p < 0.001) and external validation cohort (0.87 vs. 0.75; p = 0.003), while a comparable AUC value in the validation cohort (0.91 vs. 0.81; p = 0.09). The calibration was good.

CONCLUSIONS

An MRI-radiomics-clinical nomogram had a robust performance in antenatal predicting the PAS in pregnancies.

KEY POINTS

• An MRI-radiomics-clinical-based nomogram might serve as an adjunctive approach for the treatment decision-making in pregnancies suspicious of PAS. • The radiomic score provides a mathematical formula that predicts the possibility of PAS by using the MRI data, and pregnant women with PAS had higher radiomic scores than those without PAS.

Placenta Accreta Spectrum Disorders and Radiomics: Systematic review and quality appraisal

PURPOSE

Ultrasound and magnetic resonance imaging are the imaging modalities of choice for placenta accrete spectrum (PAS) disorders assessment. Radiomics could further increase the value of medical images and allow to overcome the limitations linked to their visual assessment. Aim of this systematic review was to identify and appraise the methodological quality of radiomics studies focused PAS disorders applications.

METHOD

Three online databases (PubMed, Scopus and Web of Science) were searched to identify original research articles on human subjects published in English. For the qualitative synthesis of results, data regarding study design (e.g., retrospective or prospective), purpose, patient population (e.g., sample size), imaging modalities and radiomics pipelines (e.g., segmentation and feature extraction strategy) were collected. The appraisal of methodological quality was performed using the Radiomics Quality Score (RQS).

RESULTS

10 articles were finally included and analyzed. All were retrospective and MRI-powered. The majority included more than 100 patients (6/10). Four were prognostic (focused on either the prediction of bleeding volume or the prediction of needed management) while six diagnostic (PAS vs not PAS classification) studies. The median RQS was 8, with maximum and minimum respectively equal to 17/36 and - 6/36. Major methodological concerns were the lack of feature stability to multiple segmentation testing and poor data openness.

CONCLUSIONS

Radiomics studies focused on PAS disorders showed a heterogeneous methodological quality, overall lower than desirable. Furthermore, many relevant research questions remain unexplored. More robust investigations are needed to foster advancements in the field and possibly clinical translation.

Texture analysis of myometrium-derived T2WI in the evaluation of placenta increta: An observational retrospective study

INTRODUCTION

MRI has demonstrated its potential in the diagnosis of placenta percreta. Texture analysis is a novel technique to quantify tissue heterogeneity. The study aimed to evaluate the feasibility of using texture analysis based on myometrium-derived T2WI to differentiate placenta accreta from increta.

METHODS

Participants with MRI and clinical or histopathological diagnosis of placenta increta were retrospectively enrolled. Texture analysis of T2WI was implemented on normal myometrium and placenta increta by MaZda software. With the Fisher discriminant method, parameter selection and reduction were done automatically. Multivariate analysis was used for the comparison of response variables between two groups. The contours of multivariable average vectors were compared using profile analysis. Two-step clustering was performed to assess the importance of parameters.

RESULTS

There were a total of 23 participants (median age 29 years, range 22-43 years). The pixel intensity distribution was narrow and wide in two first-order histograms taken from normal myometrium and placenta increta, respectively. Multivariate analysis showed nine second-order parameters derived from the histogram were statistically significant (P < 0.05). The results of two-step clustering indicated that three second-order parameters (Mean, Percentile 90%, and Percentile 99%) were important (predictor importance > 0.8). Multivariate analysis of three second-order parameters further showed they were different between normal myometrium and placenta increta.

DISCUSSION

Texture analysis based on myometrium-derived T2WI may be a useful add-on to MRI in diagnosing placenta increta.

TRIAL REGISTRATION

Registration number: ChiCTR2000038604 and name of registry: Evaluation of diagnostic accuracy of MRI multi-parameter imaging combined with texture analysis for placenta accreta spectrum disorders (PAD).

A dual-path neural network fusing dual-sequence magnetic resonance image features for detection of placenta accrete spectrum (PAS) disorder

With the increase of various risk factors such as cesarean section and abortion, placenta accrete spectrum (PAS) disorder is happening more frequently year by year. Therefore, prenatal prediction of PAS is of crucial practical significance. Magnetic resonance imaging (MRI) quality will not be affected by fetal position, maternal size, amniotic fluid volume, etc., which has gradually become an important means for prenatal diagnosis of PAS. In clinical practice, T2-weighted imaging (T2WI) magnetic resonance (MR) images are used to reflect the placental signal and T1-weighted imaging (T1WI) MR images are used to reflect bleeding, both plays a key role in the diagnosis of PAS. However, it is difficult for traditional MR image analysis methods to extract multi-sequence MR image features simultaneously and assign corresponding weights to predict PAS according to their importance. To address this problem, we propose a dual-path neural network fused with a multi-head attention module to detect PAS. The model first uses a dual-path neural network to extract T2WI and T1WI MR image features separately, and then combines these features. The multi-head attention module learns multiple different attention weights to focus on different aspects of the placental image to generate highly discriminative final features. The experimental results on the dataset we constructed demonstrate a superior performance of the proposed method over state-of-the-art techniques in prenatal diagnosis of PAS. Specifically, the model we trained achieves 88.6% accuracy and 89.9% F1-score on the independent validation set, which shows a clear advantage over methods that only use a single sequence of MR images.

MRI-Based Radiomics Analysis for Intraoperative Risk Assessment in Gravid Patients at High Risk with Placenta Accreta Spectrum

Background: Gravid patients at high risk with placenta accreta spectrum (PAS) face life-threatening risk at delivery. Intraoperative risk assessment for patients is currently insufficient. We aimed to develop an assessment system of intraoperative risks through MRI-based radiomics. Methods: A total of 131 patients enrolled were randomly grouped according to a ratio of 7:3. Clinical data were analyzed retrospectively. Radiomic features were extracted from sagittal Fast Imaging Employing State-sate Acquisition images. Univariate and multivariate regression analyses were performed to build models using R software. A receiver operating characteristic curve and decision curve analysis (DCA) were performed to determine the predictive performance of models. Results: Six radiomic features and two clinical variables were used to construct the combined model for selection of removal protocols of the placenta, with an area under the curve (AUC) of 0.90 and 0.91 in the training and test cohorts, respectively. Nine radiomic features and two clinical variables were obtained to establish the combined model for prediction of intraoperative blood loss, with an AUC of 0.90 and 0.88 in the both cohorts, respectively. The DCA confirmed the clinical utility of the combined model. Conclusion: The analysis of combined MRI-based radiomics with clinics could be clinically beneficial for patients.

Neuroplacentology in congenital heart disease: placental connections to neurodevelopmental outcomes

Children with congenital heart disease (CHD) are living longer due to effective medical and surgical management. However, the majority have neurodevelopmental delays or disorders. The role of the placenta in fetal brain development is unclear and is the focus of an emerging field known as neuroplacentology. In this review, we summarize neurodevelopmental outcomes in CHD and their brain imaging correlates both in utero and postnatally. We review differences in the structure and function of the placenta in pregnancies complicated by fetal CHD and introduce the concept of a placental inefficiency phenotype that occurs in severe forms of fetal CHD, characterized by a myriad of pathologies. We propose that in CHD placental dysfunction contributes to decreased fetal cerebral oxygen delivery resulting in poor brain growth, brain abnormalities, and impaired neurodevelopment. We conclude the review with key areas for future research in neuroplacentology in the fetal CHD population, including (1) differences in structure and function of the CHD placenta, (2) modifiable and nonmodifiable factors that impact the hemodynamic balance between placental and cerebral circulations, (3) interventions to improve placental function and protect brain development in utero, and (4) the role of genetic and epigenetic influences on the placenta-heart-brain connection. IMPACT: Neuroplacentology seeks to understand placental connections to fetal brain development. In fetuses with CHD, brain growth abnormalities begin in utero. Placental microstructure as well as perfusion and function are abnormal in fetal CHD.

Qualitative evaluation of MR images for assessing placenta accreta spectrum disorders in patients with placenta previa: A pilot validation study

PURPOSE

To validate a qualitative imaging method using magnetic resonance (MR) for predicting placental accreta spectrum (PAS) in patients with placenta previa (PP).

METHOD

Two MR imaging methods built in our previous experience was tested in an external comparable group of sixty-five patients with PP; these methods consisted of presence of at least one (Method 1) or two (Method 2) of the following abnormal MR imaging signs: intraplacental dark bands, focal interruption of myometrial border and abnormal placental vascularity. Three groups of radiologists with different level of expertise evaluated MR images: at least 5 years of experience in body imaging (Group 1); at least 10 (Group 2) or 20 (Group 3) years of experience in genito-urinary MR. While radiologists of Group 1 routinely evaluated MR images, those of Groups 2 and 3 used both Methods 1 and 2.

RESULTS

A significant (p < 0.005) difference was found between the diagnostic accuracy values of imaging evaluation performed by Group 3 using Method 1 (63%) and Method 2 (89%); of note, the accuracy of Method 2 by Group 3 was also significantly (p < 0.005) higher compared to that of both Methods 1 (46%) and 2 (63%) by Group 2 as well as to that of the routine evaluation by Group 1 (60%).

CONCLUSIONS

The qualitative identification of at least two abnormal MR signs (Method 2) represents an accurate method for predicting PAS in patients with PP particularly when this method was used by more experienced radiologists; thus, imaging expertise and methodology is required for this purpose.

Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2-weighted imaging

PURPOSE

To separately perform visual and texture analyses of the axial, coronal, and sagittal planes of T2-weighted images and identify the optimal method for differentiating between the normal placenta and placenta accreta spectrum (PAS).

METHODS

Eighty consecutive patients (normal group, n = 50; PAS group, n = 30) underwent preoperative MRI. A scoring system (0-2) was used to evaluate the degree of abnormality observed in visual analysis (bulging, abnormal vascularity, T2 dark band, placental heterogeneity). The axial, coronal, and sagittal planes were manually segmented separately to obtain texture features, and seven combinations were obtained: axial; coronal; sagittal; axial and coronal; axial and sagittal; coronal and sagittal; and axial, coronal, and sagittal. Feature selection using the least absolute shrinkage and selection operator method and model construction using a support vector machine algorithm with k-fold cross-validation were performed. AUC was used to evaluate diagnostic performance.

RESULTS

The AUC of visual analysis was 0.75. The model 'coronal and sagittal' had the highest AUC (0.98) amongst the seven combinations. The fivefold cross-validation for the model 'coronal and sagittal' showed AUCs of 0.85 and 0.97 in training and validation sets, respectively. The AUC of the model 'coronal and sagittal' for all subjects was significantly higher than that of visual analysis (0.98 vs. 0.75; p < 0.0001).

CONCLUSION

The model 'coronal and sagittal' can accurately differentiate between the normal placenta and PAS, with a significantly better diagnostic performance than visual analysis. Texture analysis is an optimal method for differentiating between the normal placenta and PAS.

Emerging placental biomarkers of health and disease through advanced magnetic resonance imaging (MRI)

Placental dysfunction is a major cause of fetal demise, fetal growth restriction, and preterm birth, as well as significant maternal morbidity and mortality. Infant survivors of placental dysfunction are at elevatedrisk for lifelong neuropsychiatric morbidity. However, despite the significant consequences of placental disease, there are no clinical tools to directly and non-invasively assess and measure placental function in pregnancy. In this work, we will review advanced MRI techniques applied to the study of the in vivo human placenta in order to better detail placental structure, architecture, and function. We will discuss the potential of these measures to serve as optimal biomarkers of placental dysfunction and review the evidence of these tools in the discrimination of health and disease in pregnancy. Efforts to advance our understanding of in vivo placental development are necessary if we are to optimize healthy pregnancy outcomes and prevent brain injury in successive generations. Current management of many high-risk pregnancies cannot address placental maldevelopment or injury, given the standard tools available to clinicians. Once accurate biomarkers of placental development and function are constructed, the subsequent steps will be to introduce maternal and fetal therapeutics targeting at optimizing placental function. Applying these biomarkers in future studies will allow for real-time assessments of safety and efficacy of novel interventions aimed at improving maternal-fetal well-being.

Deep learning-based segmentation of the placenta and uterus on MR images

Purpose: Magnetic resonance imaging has been recently used to examine the abnormalities of the placenta during pregnancy. Segmentation of the placenta and uterine cavity allows quantitative measures and further analyses of the organs. The objective of this study is to develop a segmentation method with minimal user interaction. Approach: We developed a fully convolutional neural network (CNN) for simultaneous segmentation of the uterine cavity and placenta in three dimensions (3D) while a minimal operator interaction was incorporated for training and testing of the network. The user interaction guided the network to localize the placenta more accurately. In the experiments, we trained two CNNs, one using 70 normal training cases and the other using 129 training cases including normal cases as well as cases with suspected placenta accreta spectrum (PAS). We evaluated the performance of the segmentation algorithms on two test sets: one with 20 normal cases and the other with 50 images from both normal women and women with suspected PAS. Results: For the normal test data, the average Dice similarity coefficient (DSC) was 92% and 82% for the uterine cavity and placenta, respectively. For the combination of normal and abnormal cases, the DSC was 88% and 83% for the uterine cavity and placenta, respectively. The 3D segmentation algorithm estimated the volume of the normal and abnormal uterine cavity and placenta with average volume estimation errors of 4% and 9%, respectively. Conclusions: The deep learning-based segmentation method provides a useful tool for volume estimation and analysis of the placenta and uterus cavity in human placental imaging.

Normative placental structure in pregnancy using quantitative Magnetic Resonance Imaging

INTRODUCTION

To characterize normative morphometric, textural and microstructural placental development by applying advanced and quantitative magnetic resonance imaging (qMRI) techniques to the in-vivo placenta.

METHODS

We enrolled 195 women with uncomplicated, healthy singleton pregnancies in a prospective observational study. Women underwent MRI between 16- and 40-weeks' gestation. Morphometric and textural metrics of placental growth were calculated from T2-weighted (T2W) images, while measures of microstructural development were calculated from diffusion-weighted images (DWI). Normative tables and reference curves were constructed for each measured index across gestation and according to fetal sex.

RESULTS

Data from 269 MRI studies from 169 pregnant women were included in the analyses. During the study period, placentas undergo significant increases in morphometric measures of volume, thickness, and elongation. Placental texture reveals increasing variability with advancing gestation as measured by grey level non uniformity, run length non uniformity and long run high grey level emphasis. Placental microstructure did not vary with gestational age. Placental elongation was the only metric that differed significantly between male and female fetuses.

DISCUSSION

We report quantitative metrics of placental morphometry, texture and microstructure in a large cohort of healthy controls during the second and third trimesters of pregnancy. These measures can serve as normative references of in-vivo placental development to better understand placental function in high-risk conditions and allow for the early detection of placental mal-development.

Intravoxel incoherent motion MR imaging analysis for diagnosis of placenta accrete spectrum disorders: A pilot feasibility study

BACKGROUND

Placenta accreta spectrum (PAS) disorders occur when the placenta adheres abnormally to the uterine myometrium and can have devastating effects on maternal health due to risks of massive postpartum hemorrhage and possible need for emergency hysterectomy. PAS can be difficult to diagnose using routine clinical imaging with ultrasound and structural MRI.

OBJECTIVE

To determine feasibility of using intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) analysis in the diagnosis of the placenta accreta spectrum disorders in pregnant women.

METHODS

A total of 49 pregnant women were recruited including 14 with pathologically confirmed cases of PAS and 35 health controls without prior cesarean delivery and no suspected PAS by ultrasound. All women underwent diffusion-weighted imaging with an 8 b-value scanning sequence. A semi-automated method for image processing was used, creating a 3D object map, which was then fit to a biexponential signal decay curve for IVIM modeling to determine slow diffusion (D_s), fast diffusion (D_f), and perfusion fraction (P_f).

RESULTS

Our results demonstrated a high degree of model fitting (R² ≥ 0.98), with P_f significantly higher in those with PAS compared to healthy controls (0.451 ± 0.019 versus 0.341 ± 0.022, p = 0.002). By contrast, no statistical difference in the D_f (1.70 × 10^-2 ± 0.38 × 10^-2 versus 1.48 × 10^-2 ± 0.08 × 10^-2 mm²/s, p = 0.211) or D_s (1.34 × 10^-3 ± 0.10 × 10^-3 versus 1.45 × 10^-3 ± 0.007 × 10^-3 mm²/s, p = 0.215) was found between subjects with PAS and healthy controls.

CONCLUSIONS

The use of MRI, and IVIM modeling in particular, may have potential in aiding in the diagnosis of PAS when other imaging modalities are equivocal. However, the widespread use of these techniques will require generation of large normative data sets, consistent sequencing protocols, and streamlined analysis techniques.

Serum miRNA biomarker discovery for placenta accreta spectrum

Placenta accreta spectrum (PAS) disorder is a major cause of maternal and fetal morbidity, and in vitro biomarkers are highly desired in clinic. This study enrolled three phases of 186 pregnant women, including controls, PAS patients, placenta previa (PP) patients, and pre-eclamptic (PE) patients. Initial miRNA array screened 42 out of 768 serum miRNAs in the screening phase, and then validated four miRNAs by quantitative RT-PCR in the training phase and validation phase. Their performance for PAS prenatal screening was analyzed by the receiver operating characteristic (ROC) curve, sensitivity, and specificity. Data validated that four miRNAs (miR-139-3p, miR-196a-5p, miR-518a-3p, and miR-671-3p) were down-regulated in PAS group comparing with controls in three phases of subjects. Except for miR-518a-3p, the expression levels of these miRNAs also were significantly different between the PAS and the group including PP and PE. In addition, these biomarkers demonstrated modest screening efficiency, as the AUC ranged from 0.59 to 0.74, sensitivity 0.54 to 0.80, and specificity 0.62 to 0.76. However, the AUC and specificity can improve greatly (AUC 0.91, specificity 0.92) using a 'diagnostic signature' that combined the four miRNAs and four clinical parameters into one panel. GO and KEGG signaling pathway analysis indicated their target genes were involved in angiogenesis, embryonic development, cell migration and adhesion, and tumor-related pathways. In conclusion, the four miRNAs discovered in this study not only can be used for future non-invasive prenatal PAS screening, but also provide a new experimental basis for future research on PAS etiology.

Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study

BACKGROUND

Identification of pregnancies with postpartum haemorrhage (PPH) antenatally rather than intrapartum would aid delivery planning, facilitate transfusion requirements and decrease maternal complications. MRI has been increasingly used for placenta evaluation. Here, we aim to build a nomogram incorporating both clinical and radiomic features of placenta to predict the risk for PPH in pregnancies during caesarian delivery (CD).

METHODS

A total of 298 pregnant women were retrospectively enrolled from Henan Provincial People's Hospital (training cohort: n = 207) and from The Third Affiliated Hospital of Zhengzhou University (external validation cohort: n = 91). These women were suspected with placenta accreta spectrum (PAS) disorders and underwent MRI for placenta evaluation. All of them underwent CD and were singleton. PPH was defined as more than 1000 mL estimated blood loss (EBL) during CD. Radiomic features were selected based on their correlations with EBL. Radiomic, clinical, radiological, clinicoradiological and clinicoradiomic models were built to predict the risk of PPH for each patient. The model with the best prediction performance was validated with its discrimination ability, calibration curve and clinical application.

FINDINGS

Thirty-five radiomic features showed strong correlation with EBL. The clinicoradiomic model resulted in the best discrimination ability for risk prediction of PPH, with AUC of 0.888 (95% CI, 0.844-0.933) and 0.832 (95% CI, 0.746-0.913), sensitivity of 91.2% (95% CI, 85.8%-96.7%) and 97.6% (95% CI, 92.7%-100%) in the training and validation cohort respectively. For patients with severe PPH (EBL more than 2000 mL), 53 out of 55 pregnancies (96.4%) in the training cohort and 18 out of 18 (100%) pregnancies in the validation cohort were identified by the clinicoradiomic model. The model performed better in patients without placenta previa (PP) than in patients with PP, with AUC of 0.983 compared with 0.867, sensitivity of 100% compared with 90.8% in the training cohort, AUC of 0.832 compared with 0.815, sensitivity of 97.6% compared with 97.2% in the validation cohort.

INTERPRETATION

The clinicoradiomic model incorporating both prenatal clinical factors and radiomic signature of placenta on T2WI showed good performance for risk prediction of PPH. The predictive model can identify severe PPH with high sensitivity and can be applied in patients with and without PP.