Texture analysis of placental MRI: can it aid in the prenatal diagnosis of placenta accreta spectrum?

Placental T2WI MRI-based radiomics-clinical nomogram predicts suspicious placenta accreta spectrum in patients with placenta previa

BACKGROUND

The incidence of placenta accreta spectrum (PAS) increases in women with placenta previa (PP). Many radiologists sometimes cannot completely and accurately diagnose PAS through the simple visual feature analysis of images, which can affect later treatment decisions. The study is to develop a T2WI MRI-based radiomics-clinical nomogram and evaluate its performance for non-invasive prediction of suspicious PAS in patients with PP.

METHODS

The preoperative MR images and related clinical data of 371 patients with PP were retrospectively collected from our hospital, and the intraoperative examination results were used as the reference standard of the PAS. Radiomics features were extracted from sagittal T2WI MR images and further selected by LASSO regression analysis. The radiomics score (Radscore) was calculated with logistic regression (LR) classifier. A nomogram integrating Radscore and selected clinical factors was also developed. The model performance was assessed with respect to discrimination, calibration and clinical usefulness.

RESULTS

A total of 6 radiomics features and 1 clinical factor were selected for model construction. The Radscore was significantly associated with suspicious PAS in both the training (p < 0.001) and validation (p < 0.001) datasets. The AUC of the nomogram was also higher than that of the Radscore in the training dataset (0.891 vs. 0.803, p < 0.001) and validation dataset (0.897 vs. 0.780, p < 0.001), respectively. The calibration was good, and the decision curve analysis demonstrated the nomogram had higher net benefit than the Radscore.

CONCLUSIONS

The T2WI MRI-based radiomics-clinical nomogram showed favorable diagnostic performance for predicting PAS in patients with PP, which could potentially facilitate the obstetricians for making clinical decisions.

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.

Deep Learning Radiomic Analysis of MRI Combined with Clinical Characteristics Diagnoses Placenta Accreta Spectrum and its Subtypes

BACKGROUND

Different placenta accreta spectrum (PAS) subtypes pose varying surgical risks to the parturient. Machine learning model has the potential to diagnose PAS disorder.

PURPOSE

To develop a cascaded deep semantic-radiomic-clinical (DRC) model for diagnosing PAS and its subtypes based on T2-weighted MRI.

STUDY TYPE

Retrospective.

POPULATION

361 pregnant women (mean age: 33.10 ± 4.37 years), suspected of PAS, divided into segment training cohort (N = 40), internal training cohort (N = 139), internal testing cohort (N = 60), and external testing cohort (N = 122).

FIELD STRENGTH/SEQUENCE

Coronal T2-weighted sequence at 1.5 T and 3.0 T.

ASSESSMENT

Clinical characteristics such as history of uterine surgery and the presence of placenta previa, complete placenta previa and dangerous placenta previa were extracted from clinical records. The DRC model (incorporating radiomics, deep semantic features, and clinical characteristics), a cumulative radiological score method performed by radiologists, and other models (including a radiomics and clinical, the clinical, radiomics and deep learning models) were developed for PAS disorder diagnosing (existence of PAS and its subtypes).

STATISTICAL TESTS

AUC, ACC, Student's t-test, the Mann-Whitney U test, chi-squared test, dice coefficient, intraclass correlation coefficients, least absolute shrinkage and selection operator regression, receiver operating characteristic curve, calibration curve with the Hosmer-Lemeshow test, decision curve analysis, DeLong test, and McNemar test. P < 0.05 indicated a significant difference.

RESULTS

In PAS diagnosis, the DRC-1 outperformed than other models (AUC = 0.850 and 0.841 in internal and external testing cohorts, respectively). In PAS subtype classification (abnormal adherent placenta and abnormal invasive placenta), DRC-2 model performed similarly with radiologists (P = 0.773 and 0.579 in the internal testing cohort and P = 0.429 and 0.874 in the external testing cohort, respectively).

DATA CONCLUSION

The DRC model offers efficiency and high diagnostic sensitivity in diagnosis, aiding in surgical planning.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Enhancing postpartum hemorrhage prediction in pernicious placenta previa: a comparative study of magnetic resonance imaging and ultrasound nomogram

Objective: To explore the risk factors of postpartum hemorrhage (PPH) in patients with pernicious placenta previa (PPP) and to develop and validate a clinical and imaging-based predictive model. Methods: A retrospective analysis was conducted on patients diagnosed surgically and pathologically with PPP between January 2018 and June 2022. All patients underwent PPP magnetic resonance imaging (MRI) and ultrasound scoring in the second trimester and before delivery, and were categorized into two groups according to PPH occurrence. The total imaging score and sub-item prediction models of the MRI risk score/ultrasound score were used to construct Models A and B/Models C and D. Models E and F were the total scores of the MRI combined with the ultrasound risk and sub-item prediction model scores. Model G was based on the subscores of MRI and ultrasound with the introduction of clinical data. Univariate logistic regression analysis and the logical least absolute shrinkage and selection operator (LASSO) model were used to construct models. The receiver operating characteristic curve andision curve analysis (DCA) were drawn, and the model with the strongest predictive ability and the best clinical effect was selected to construct a nomogram. Internal sampling was used to verify the prediction model's consistency. Results: 158 patients were included and the predictive power and clinical benefit of Models B and D were better than those of Models A and C. The results of the area under the curve of Models B, D, E, F, and G showed that Model G was the best, which could reach 0.93. Compared with Model F, age, vaginal hemorrhage during pregnancy, and amniotic fluid volume were independent risk factors for PPH in patients with PPP (p < 0.05). We plotted the DCA of Models B, D, E, F, and G, which showed that Model G had better clinical benefits and that the slope of the calibration curve of Model G was approximately 45°. Conclusion: LASSO regression nomogram based on clinical risk factors and multiple conventional ultrasound plus MRI signs has a certain guiding significance for the personalized prediction of PPH in patients with PPP before delivery.

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.

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.

Predicting the risk of fetal growth restriction by radiomics analysis of the placenta on T2WI: A retrospective case-control study

INTRODUCTION

Fetal growth restriction (FGR) is associated with placental abnormalities, and its precise diagnosis is challenging. This study aimed to explore the role of radiomics based on placental MRI in predicting FGR.

METHODS

A retrospective study using T2-weighted placental MRI data were conducted. A total of 960 radiomic features were automatically extracted. Features were selected using three-step machine learning methods. A combined model was constructed by combining MRI-based radiomic features and ultrasound-based fetal measurements. The receiver operating characteristic curves (ROC) were conducted to assess model performance. Additionally, decision curves and calibration curves were performed to evaluate prediction consistency of different models.

RESULTS

Among the study participants, pregnant women who delivered from January 2015 to June 2021 were randomly divided into training (n = 119) and test (n = 40) sets. Forty-three other pregnant women who delivered from July 2021 to December 2021 were used as the time-independent validation set. After training and testing, three radiomic features that were strongly correlated with FGR were selected. The area under the ROC curves (AUCs) of the MRI-based radiomics model reached 0.87 (95% confidence interval [CI]: 0.74-0.96) and 0.87 (95% CI: 0.76-0.97) in the test and validation sets, respectively. Moreover, the AUCs for the model comprising MRI-based radiomic features and ultrasound-based measurements were 0.91 (95% CI: 0.83-0.97) and 0.94 (95% CI: 0.86-0.99) in the test and validation sets, respectively.

DISCUSSION

MRI-based placental radiomics could accurately predict FGR. Moreover, combining placental MRI-based radiomic features with ultrasound indicators of the fetus could improve the diagnostic accuracy of FGR.

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.

Quantitative Analysis of Signal Heterogeneity in the Hepatobiliary Phase of Pretreatment Gadoxetic Acid-Enhanced MRI as a Prognostic Imaging Biomarker in Transarterial Chemoembolization for Intermediate-Stage Hepatocellular Carcinoma

BACKGROUND

In the era of local and systemic therapies for intermediate-stage hepatocellular carcinoma (HCC), personalized therapy has become available. The aim of our study was to evaluate the usefulness of quantitative analysis of pretreatment gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI) to predict prognosis following transarterial chemoembolization (TACE).

METHODS

This retrospective study included patients with treatment-naïve intermediate-stage HCC who underwent EOB-MRI before the initial TACE and were treated by initial TACE between February 2007 and January 2016. Signal heterogeneity in the hepatobiliary phase (HBP) of EOB-MRI was quantitatively evaluated by the coefficient of variation (CV). The cutoff CV value was determined using the Classification and Regression Tree algorithm.

RESULTS

A total of 64 patients were enrolled. In multivariate analysis, High CV (≥0.16) was significantly associated with poor prognosis (p = 0.038). In a subgroup analysis of patients within up-to-7 criteria, MST was significantly shorter in the High CV group than in the Low CV group (37.7 vs. 82.9 months, p = 0.024). In patients beyond up-to-7 criteria, MST was 18.0 and 38.3 months in the High CV and Low CV groups, respectively (p = 0.182). In both groups scanned at 1.5 T or 3.0 T, High CV was significantly associated with poor prognosis (p = 0.001 and 0.003, respectively).

CONCLUSION

CV of the tumor in the HBP of EOB-MRI is a valuable prognostic factor of TACE.

Texture analysis of sonographic image of placenta in pregnancies with normal and adverse outcomes, a pilot study

INTRODUCTION

Many maternal and fetal morbidity and mortality from complications of pregnancy have been attributed to placenta abnormality. Placenta assessment in developing countries is mainly through ultrasonography which is subjective and prone to error. Objective assessment of placental abnormalities through texture analysis has been frequently done using magnetic resonance images with little done on ultrasound generated images, thus, the need for this study. The study is aimed at using statistical texture analysis in characterizing placenta tissue into normal and abnormal placenta as well as testing the accuracy of different texture analysis algorithms in differentiating placenta into normal and abnormal placental tissues.

METHODS

This longitudinal study involved 500 ultrasound-generated placenta images from patients screened for adverse pregnancy outcomes in a private hospital in Enugu. These images were loaded onto an HP laptop for viewing. Two regions of interest were selected from the placenta tissue where texture features were extracted and were classified into normal and abnormal placentas using MaZda® software version 47 while the accuracy of the classification descriptors was assessed using WEKA classification algorithms.

RESULTS

Co-occurrence matrix, run length matrix and histogram parameters differentiated normal placenta tissue from abnormal placental tissues (p-value <0.05) while variance is the only absolute gradient parameter that can differentiate normal placenta tissue from abnormal placenta tissues. All feature descriptors show high classification accuracy using KNN and ANN algorithms.

CONCLUSION

Texture analysis can differentiate normal placenta tissues from abnormal placenta tissue which will reduce the errors associated with subjective assessment of the placenta echogenicity.

IMPLICATIONS FOR PRACTICE

Integrating these computer-aided algorithms into our ultrasound machines will lead to early detection of abnormal placenta tissues as early management results in better pregnancy outcomes.

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.

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

PURPOSE

To develop and validate an MRI-based radiomics model for differentiating invasive placentas in patients with high risks.

METHODS

A total of 181 pregnant women suspected of placenta accreta spectrum (PAS) disorders and who underwent MRI for placenta evaluation were retrospectively enrolled. The data set was randomly divided into the training (n = 125; invasive = 63, noninvasive = 62) and test (n = 56; invasive = 28, noninvasive = 28) groups. Radiomics features were extracted from half-Fourier acquisition single-shot turbo spin echo (HASTE) and sagittal true fast imaging in steady-state precession (TRUFISP) sequences independently and mainly selected based on their correlations with invasive placentas to construct two radiomics signatures including HASTE-Radscore and TRUFISP-Radscore. Then, the predictive performance of radiomic signatures, clinical features, radiographic features, and their combination were evaluated. The model with the best predictive performance was validated with its discrimination ability, calibration, and clinical usefulness.

RESULTS

Five radiomics features from HASTE and three radiomics features from TRUFISP were retained, respectively, for predicting invasive placentas. The combination of radiomics signatures and clinical features including prior cesarean delivery, placenta previa, and radiographic feature, the placental thickness resulted in the best discrimination ability, with area under the curve of 0.898 (95% confidence interval [CI] 0.844-0.9522) and 0.858 (95% confidence interval 0.7514-0.9655) in the training and test cohort, respectively. The combined model showed a significantly better area under the curve performance and clinical usefulness than independent clinical or radiographic model according to DeLong test (p < .05), net reclassification improvement and integrated discrimination improvement analysis (positive improvement) and decision curve analysis (higher net benefit).

CONCLUSIONS

The T₂ -weighted imaging MRI radiomics model could serve as a potential prenatal diagnosis tool for identifying invasive placentas in patients with high risks.

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).

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.

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.

Prenatal prediction and typing of placental invasion using MRI deep and radiomic features

BACKGROUND

To predict placental invasion (PI) and determine the subtype according to the degree of implantation, and to help physicians develop appropriate therapeutic measures, a prenatal prediction and typing of placental invasion method using MRI deep and radiomic features were proposed.

METHODS

The placental tissue of abdominal magnetic resonance (MR) image was segmented to form the regions of interest (ROI) using U-net. The radiomic features were subsequently extracted from ROI. Simultaneously, a deep dynamic convolution neural network (DDCNN) with codec structure was established, which was trained by an autoencoder model to extract the deep features from ROI. Finally, combining the radiomic features and deep features, a classifier based on the multi-layer perceptron model was designed. The classifier was trained to predict prenatal placental invasion as well as determine the invasion subtype.

RESULTS

The experimental results show that the average accuracy, sensitivity, and specificity of the proposed method are 0.877, 0.857, and 0.954 respectively, and the area under the ROC curve (AUC) is 0.904, which outperforms the traditional radiomic based auxiliary diagnostic methods.

CONCLUSIONS

This work not only labeled the placental tissue of MR image in pregnant women automatically but also realized the objective evaluation of placental invasion, thus providing a new approach for the prenatal diagnosis of placental invasion.

Prognosticators of intravoxel incoherent motion (IVIM) MRI for adverse maternal and neonatal clinical outcomes in patients with placenta accreta spectrum disorders

BACKGROUND

The incidence of placenta accreta spectrum (PAS) disorders has increased rapidly in recent years and is associated with several maternal and neonatal complications. Intravoxel incoherent motion (IVIM) imaging is a method which can assess placental perfusion quantitatively. Therefore, the first aim of this study was to investigate whether patients with adverse maternal and neonatal outcomes of PAS disorders differed in the parameters from IVIM. A second aim was to identify these parameters for adverse peripartum outcome in gravid patients at risk for PAS.

METHODS

The subject group consisted of patients with placenta previa, in which 75 patients had PAS disorders and 24 patients did not have PAS disorders between 28+0 and 39+6 weeks, respectively. All women underwent magnetic resonance imaging (MRI) examination including an IVIM sequence with 8 b values on a 1.5T scanner. The perfusion fraction (f), pseudodiffusion coefficient (D*), and standard diffusion coefficient (D) were calculated. All medical records were received postpartum. The final degree of placental invasion was established either by placental villi alterations from a placental sample or from maternity records of the women's general practitioners.

RESULTS

Women with PAS disorders had a higher perfusion fraction (34.12%) than women without the disease (29.39%) (P<0.05). The perfusion fraction was 36.86% in women with massive blood loss and was 35.15% in women requiring transfusion, which was higher than women without massive blood loss and not requiring transfusion (P<0.05). The D value was 1.65×10^-3 mm²/s in women with low birth weight, which was lower than that in women with appropriate birth weight (1.70×10^-3 mm²/s) (P<0.05).

CONCLUSIONS

Patients with PAS disorders differed in placental perfusion fraction from women without PAS disorders. The f and D value may be used to recognize patients with certain adverse clinical outcomes.

Can introvoxel incoherent motion MRI be used to differentiate patients with placenta accreta spectrum disorders?

BACKGROUND

The incidence of PAS disorders increased rapidly in recent years, and introvoxel incoherent motion (IVIM) MRI has been applied in the assessment of placenta. The study aims to investigate whether the parameters from IVIM can be used to differentiate patients with PAS disorders complicating placenta previa and secondly to differentiate different categories of PAS disorders.

METHODS

The study participants were comprised of 99 patients with placenta previa, including 16 patients with placenta accreta, 51 patients with increta, 8 patients with percreta and 24 patients without PAS disorders between 28 + 0 and 39 + 6 weeks. IVIM MRI was performed on a 1.5 T scanner. Perfusion fraction (f), pseudodiffusion coefficient (D*) and diffusion coefficient (D) were calculated.

RESULTS

Women with PAS disorders had a higher perfusion fraction (p = 0.019) than women without the disease. Multiple comparisons showed perfusion fraction in patients without PAS disorders was significantly lower than in patients with placenta accreta and percreta(P = 0.018 and 0.033 respectively), but was not lower than in patients with increta(p = 1).

CONCLUSION

Patients with placenta accreta and percreta differed in placental perfusion fraction from women with increta and without PAS disorders.

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.