Quantifying magnetic resonance imaging features to classify placenta accreta spectrum (PAS) in high-risk gravid patients

Grading magnetic resonance imaging signs for diagnosing invasive placenta accreta spectrum disorders

INTRODUCTION

Placenta accreta spectrum (PAS) disorders result from abnormal placental attachment, leading to varying degrees of myometrial invasion. Magnetic resonance imaging (MRI) plays a crucial role in assessing the depth and extent of placental invasion. This study aims to evaluate the correlation between quantified MRI findings and the diagnosis of PAS, as classified according to the FIGO system.

MATERIALS AND METHODS

A retrospective analysis was conducted on 556 high-risk PAS patients, defined as those with placenta previa or a history of previous cesarean sections. Ten predefined MRI signs were assessed board certified radiologists. Multivariate logistic regression was used to identify independent predictors of invasive PAS. The positive predictive value (PPV) and negative predictive value (NPV) were calculated to assess the diagnostic performance of signs.

RESULTS

Among the 556 cases, 150 (26.98 %) were classified as non-PAS, 180 (32.37 %) as placenta accreta, 158 (28.42 %) as placenta increta, and 68 (12.23 %) as placenta percreta. Four MRI signs were identified as significant predictors of invasive PAS: bladder wall interruption (odd ratio [OR] = 160.17), placental ischemic infarction (OR = 19.91), placental protrusion (OR = 14.66), and myometrial thinning (OR = 14.07). The PPV of these signs ranged from 70 % to 85 %, while the NPV ranged from 65 % to 72 %. Multivariate analysis confirmed these MRI findings as independent predictors of invasive PAS.

CONCLUSIONS

This study identified four key MRI signs as reliable predictors of invasive PAS, which can effectively inform clinical decision-making regarding surgical interventions, such as cesarean hysterectomy.

Comparative Evaluation of Clinical-MRI, Radiomics, and Integrated Nomogram Models for Preoperative Prediction of Placenta Accreta Spectrum

RATIONALE AND OBJECTIVES

The escalating incidence of placental accreta spectrum (PAS), a pregnancy complication, underscores the need for accurate prenatal diagnosis to guide optimal management strategies. This study aims to develop, validate, and compare various prenatal PAS prediction models integrating clinical data, MRI signs, and radiomics signatures.

MATERIALS AND METHODS

A cohort comprising 111 patients (72 with PAS and 39 without, denoted as N-PAS) served as the training set, while another 47 patients (33 PAS and 14 N-PAS) constituted the validation set. Clinical features and MRI signs were subjected to univariate and multivariate analyses to construct the Clinical-MRI model. Radiomic features were extracted from MRI images and refined through the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm, thereby establishing the Radiomics model. An optimal set of radiomic features was utilized to derive the Radscore, which was then integrated with clinical features and MRI signs to formulate the Nomogram model. The performance of these models was comprehensively evaluated and compared.

RESULTS

In the validation set evaluation, the Nomogram model, which integrated Radscore, a pivotal clinical indicator, and two MRI signs, demonstrated superior performance. With an area under the curve (AUC) of 0.861 (95% CI: 0.745, 0.978), this model significantly outperformed both the clinical-MRI model (AUC = 0.796, 95% CI: 0.649, 0.943) and the radiomics model (AUC = 0.704, 95% CI: 0.531, 0.877). Specifically, the Nomogram model achieved a high sensitivity of 81.8% and a specificity of 78.6% in the prenatal diagnosis of placenta accreta spectrum (PAS), thereby offering clinicians a precise and efficient diagnostic aid.

CONCLUSION

The radiomics-derived Radscore serves as an independent predictor for prenatal PAS. Combining Radscore with clinical features and MRI signs into a Nomogram model provides a non-invasive tool with high sensitivity or specificity for PAS diagnosis, enhancing prenatal assessment and management.

Diagnostic Accuracy of Magnetic Resonance Imaging in the Diagnosis of Placenta Accreta Spectrum: A Systematic Review and Meta-analysis

Objective:

To evaluate the diagnostic accuracy of magnetic resonance imaging (MRI) in diagnosing placenta accreta spectrum (PAS).

Methods:

We conducted a comprehensive literature search from database inception to November 2023 using terms such as placenta creta, increta, percreta, PAS, MRI, and their respective Medical Subject Headings terms. All prospective and retrospective cohort, case-control, and cross-sectional studies involving prenatal magnetic resonance imaging diagnosis of PAS with subsequent pathological confirmation were included.

Results:

A total of 40 studies encompassing 3664 women met the inclusion criteria, with 1894 cases confirmed pathologically as PAS. The overall sensitivity of MRI was 0.867 (95% confidence interval (CI): 0.807–0.910), and the specificity was 0.860 (95% CI: 0.799–0.905), with a correlation of 0.693 between sensitivity and specificity. The estimated odds ratio was 28.693 (95% CI: 14.463–56.924), the negative likelihood ratio was 0.178 (95% CI: 0.122–0.258), and the positive likelihood ratio was 4.316 (95% CI: 3.186–5.846). Analysis of individual MRI criteria revealed estimates of sensitivity, specificity, odds ratio, negative likelihood ratio, and positive likelihood ratio for abnormal placental bed vascularization as 0.500, 0.740, 2.788, 0.571, and 1.645 respectively; 0.384, 0.985, 6.270, 0.471, and 2.720 for bladder wall interruption; 0.766, 0.818, 13.638, 0.262, and 3.375 for the presence of dark intraplacental bands; 0.691, 0.913, 10.828, 0.352, and 3.361 for heterogeneous placenta; 0.688, 0.984, 34.886, 0.254, and 7.164 for indistinctive myometrium; 0.757, 0.864, 8.496, 0.362, and 2.778 for loss of retroplacental dark zone; 0.828, 0.593, 5.829, 0.329, and 1.766 for myometrial thinning; and 0.518, 0.916, 9.473, 0.411, and 3.526 for placental bulge, respectively.

Conclusion:

MRI demonstrates significant utility in diagnosing PAS and its severity. It is recommended for use in all cases with inconclusive ultrasonographic findings.

Registration:

Registration number CRD42021267501.

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.

Use of super resolution reconstruction MRI for surgical planning in Placenta accreta spectrum disorder: Case series

INTRODUCTION

Comprehensive imaging using ultrasound and MRI of placenta accreta spectrum (PAS) aims to prevent catastrophic haemorrhage and maternal death. Standard MRI of the placenta is limited by between-slice motion which can be mitigated by super-resolution reconstruction (SRR) MRI. We applied SRR in suspected PAS cases to determine its ability to enhance anatomical placental assessment and predict adverse maternal outcome.

METHODS

Suspected PAS patients (n = 22) underwent MRI at a gestational age (weeks + days) of (32+3±3+2, range (27+1-38+6)). SRR of the placental-myometrial-bladder interface involving rigid motion correction of acquired MRI slices combined with robust outlier detection to reconstruct an isotropic high-resolution volume, was achieved in twelve. 2D MRI or SRR images alone, and paired data were assessed by four radiologists in three review rounds. All radiologists were blinded to results of the ultrasound, original MR image reports, case outcomes, and PAS diagnosis. A Random Forest Classification model was used to highlight the most predictive pathological MRI markers for major obstetric haemorrhage (MOH), bladder adherence (BA), and placental attachment depth (PAD).

RESULTS

At delivery, four patients had placenta praevia with no abnormal attachment, two were clinically diagnosed with PAS, and six had histopathological PAS confirmation. Pathological MRI markers (T2-dark intraplacental bands, and loss of retroplacental T2-hypointense line) predicting MOH were more visible using SRR imaging (accuracy 0.73), in comparison to 2D MRI or paired imaging. Bladder wall interruption, predicting BA, was only easily detected by paired imaging (accuracy 0.72). Better detection of certain pathological markers predicting PAD was found using 2D MRI (placental bulge and myometrial thinning (accuracy 0.81)), and SRR (loss of retroplacental T2-hypointense line (accuracy 0.82)).

DISCUSSION

The addition of SRR to 2D MRI potentially improved anatomical assessment of certain pathological MRI markers of abnormal placentation that predict maternal morbidity which may benefit surgical planning.

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.

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.