Combining Clinical Characteristics and Specific Magnetic Resonance Imaging Features to Predict Placenta Accreta

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.

Development and validation of MRI-based scoring models for predicting placental invasiveness in high-risk women for placenta accreta spectrum

OBJECTIVES

To develop and validate MRI-based scoring models for predicting placenta accreta spectrum (PAS) invasiveness.

MATERIALS AND METHODS

This retrospective study comprised a derivation cohort and a validation cohort. The derivation cohort came from a systematic review of published studies evaluating the diagnostic performance of MRI signs for PAS and/or placenta percreta in high-risk women. The significant signs were identified and used to develop prediction models for PAS and placenta percreta. Between 2016 and 2021, consecutive high-risk pregnant women for PAS who underwent placental MRI constituted the validation cohort. Two radiologists independently evaluated the MRI signs. The reference standard was intraoperative and pathologic findings. The predictive ability of MRI-based models was evaluated using the area under the curve (AUC).

RESULTS

The derivation cohort included 26 studies involving 2568 women and the validation cohort consisted of 294 women with PAS diagnosed in 258 women (88%). Quantitative meta-analysis revealed that T2-dark bands, placental/uterine bulge, loss of T2 hypointense interface, bladder wall interruption, placental heterogeneity, and abnormal intraplacental vascularity were associated with both PAS and placenta percreta, and myometrial thinning and focal exophytic mass were exclusively associated with PAS. The PAS model was validated with an AUC of 0.90 (95% CI: 0.86, 0.93) for predicting PAS and 0.85 (95% CI: 0.79, 0.90) for adverse peripartum outcome; the placenta percreta model showed an AUC of 0.92 (95% CI: 0.86, 0.98) for predicting placenta percreta.

CONCLUSION

MRI-based scoring models established based on quantitative meta-analysis can accurately predict PAS, placenta percreta, and adverse peripartum outcome.

CLINICAL RELEVANCE STATEMENT

These proposed MRI-based scoring models could help accurately predict PAS invasiveness and provide evidence-based risk stratification in the management of high-risk pregnant women for PAS.

KEY POINTS

• Accurately identifying placenta accreta spectrum (PAS) and assessing its invasiveness depending solely on individual MRI signs remained challenging. • MRI-based scoring models, established through quantitative meta-analysis of multiple MRI signs, offered the potential to predict PAS invasiveness in high-risk pregnant women. • These MRI-based models allowed for evidence-based risk stratification in the management of pregnancies suspected of having PAS.

Nomogram model for predicting invasive placenta in patients with placenta previa: integrating MRI findings and clinical characteristics

This study aims to validate a nomogram model that predicts invasive placenta in patients with placenta previa, utilizing MRI findings and clinical characteristics. A retrospective analysis was conducted on a training cohort of 269 patients from the Second Affiliated Hospital of Fujian Medical University and a validation cohort of 41 patients from Quanzhou Children's Hospital. Patients were classified into noninvasive and invasive placenta groups based on pathological reports and intraoperative findings. Three clinical characteristics and eight MRI signs were collected and analyzed to identify risk factors and develop the nomogram model. The mode's performance was evaluated in terms of its discrimination, calibration, and clinical utility. Independent risk factors incorporated into the nomogram included the number of previous cesarean sections ≥ 2 (odds ratio [OR] 3.32; 95% confidence interval [CI] 1.28-8.59), type-II placental bulge (OR 17.54; 95% CI 3.53-87.17), placenta covering the scar (OR 2.92; CI 1.23-6.96), and placental protrusion sign (OR 4.01; CI 1.06-15.18). The area under the curve (AUC) was 0.908 for the training cohort and 0.803 for external validation. The study successfully developed a highly accurate nomogram model for predicting invasive placenta in placenta previa cases, based on MRI signs and clinical characteristics.

Association of MRI Features and Adverse Maternal Outcome in Patients With Placenta Accreta Spectrum Disorders After Abdominal Aortic Balloon Occlusion

BACKGROUND

MRI features may be associated with adverse maternal outcome in patients with placenta accreta spectrum (PAS) disorders even with abdominal aortic balloon occlusion (AABO).

PURPOSE

This study aimed to identify risk factors of MRI for association with adverse maternal outcome in patients with PAS disorders after AABO.

STUDY TYPE

Retrospective.

POPULATION

Clinical and MRI features of 80 patients were retrospectively reviewed from October 2016 to August 2021. A total of 40 patients had adverse maternal outcomes including intrapartum/peripartum bleeding >1000 mL and/or emergency hysterectomy after AABO.

SEQUENCE

Half-Fourier acquisition single-shot turbo spin echo and gradient echo imaging True fast imaging with steady-state precession (True-FISP) at 1.5T MR scanner.

ASSESSMENT

MRI features were evaluated by three radiologists and were tested for any association with adverse maternal outcome.

STATISTICAL TESTS

Interobserver agreement was calculated with kappa (k) statistics. Association between MRI features and adverse maternal outcomes were evaluated by univariate and multivariate analyses. A nomogram was constructed based on the logistic regression.

RESULTS

The interobserver agreement ranged from fair to substantial (k = 0.379-0.783). Multivariate analyses revealed that short cervical length (OR: 4.344), abnormal intraplacental vascularity (OR: 6.005), placental bulge (OR: 9.085), and myometrial interruption (OR: 9.550) were independent risk factors for adverse maternal outcomes. The combination of four risk factors together demonstrated the highest AUC of 0.851 (95% CI 0.769-0.933) with a sensitivity and specificity of 77.5% and 72.5%, respectively and then a nomogram composed of the above four risk factors was constructed to represent the probability of adverse maternal outcome.

DATA CONCLUSION

The nomogram demonstrated the association between MRI features and patient's poor outcome after undergoing AABO and C-section delivery for PAS.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 3.

External Validation of a Multivariable Prediction Model for Placenta Accreta Spectrum

BACKGROUND

Placenta accreta spectrum (PAS) is a disorder of abnormal placentation associated with severe postpartum hemorrhage, maternal morbidity, and mortality. Predelivery prediction of this condition is important to determine appropriate delivery location and multidisciplinary planning for operative management. This study aimed to validate a prediction model for PAS developed by Weiniger et al in 2 cohorts who delivered at 2 different United States tertiary centers.

METHODS

Cohort A (Brigham and Women's Hospital; N = 253) included patients with risk factors (prior cesarean delivery and placenta previa) and/or ultrasound features of PAS presenting to a tertiary-care hospital. Cohort B (Columbia University Irving Medical Center; N = 99) consisted of patients referred to a tertiary-care hospital specifically because of ultrasound features of PAS. Using the outcome variable of surgical and/or pathological diagnosis of PAS, discrimination (via c-statistic), calibration (via intercept, slope, and flexible calibration curve), and clinical usefulness (via decision curve analysis) were determined.

RESULTS

The model c-statistics in cohorts A and B were 0.728 (95% confidence interval [CI], 0.662-0.794) and 0.866 (95% CI, 0.754-0.977) signifying acceptable and excellent discrimination, respectively. The calibration intercept (0.537 [95% CI, 0.154-0.980] for cohort A and 3.001 [95% CI, 1.899- 4.335] for B), slopes (0.342 [95% CI, 0.170-0.532] for cohort A and 0.604 [95% CI, -0.166 to 1.221] for B), and flexible calibration curves in each cohort indicated that the model underestimated true PAS risks on average and that there was evidence of overfitting in both validation cohorts. The use of the model compared to a treat-all strategy by decision curve analysis showed a greater net benefit of the model at a threshold probability of >0.25 in cohort A. However, no net benefit of the model over the treat-all strategy was seen in cohort B at any threshold probability.

CONCLUSIONS

The performance of the Weiniger model is variable based on the case-mix of the population with regard to PAS clinical risk factors and ultrasound features, highlighting the importance of spectrum bias when applying this PAS prediction model to distinct populations. The model showed benefit for predicting PAS in populations with substantial case-mix heterogeneity at threshold probability of >25%.

Imaging the Uterus in Placenta Accreta Spectrum Disorder

Antenatal diagnosis of placenta accreta spectrum (PAS) improves maternal and neonatal outcomes by allowing for multidisciplinary planning and preparedness. Ultrasound is the primary imaging tool. Simplification and standardization of placental evaluation and reporting terminology allows improved communication and understanding between teams. Prior to 10 weeks of gestation, gestational sac position and least myometrial thickness surrounding the gestational sac help PAS diagnosis very early in pregnancy. Late first-, second-, and third-trimester evaluation includes comprehensive evaluation of the placenta, transabdominal and transvaginal with partially full maternal urinary bladder, and by color Doppler. Subsequently, the sonologist should indicate whether the evaluation was optimal or suboptimal; the level of suspicion as low, moderate, or high; and the extent as focal, global, or extending beyond the uterus. Other complementary imaging modalities such as 3D-power Doppler ultrasound, magnetic resonance imaging (MRI), and vascular topography mapping strive to improve antenatal placental evaluation but remain investigational at present. KEY POINTS: · Antenatal imaging, primarily using ultrasound with partially full maternal urinary bladder, is an essential means of evaluation of those at risk for PAS.. · Simplification and standardization of placental evaluation and reporting will allow improved communication between the multidisciplinary teams.. · Gestational sac location prior to 10 weeks of gestation and four markers after that (placental lacunae and echostructure, myometrial thinning, hypoechoic zone with or without bulging between placenta and myometrium, and increased flow on color Doppler)..

The prenatal diagnostic indicators of placenta accreta spectrum disorders

Placenta accreta spectrum (PAS) disorders refers to a heterogeneous group of anomalies distinguished by abnormal adhesion or invasion of chorionic villi through the myometrium and uterine serosa. PAS frequently results in life-threatening complications, including postpartum hemorrhage and hysterotomy. The incidence of PAS has increased recently as a result of rising cesarean section rates. Consequently, prenatal screening for PAS is essential. Despite the need to increase specificity, ultrasound is still considered a primary adjunct. Given the dangers and adverse effects of PAS, it is necessary to identify pertinent markers and validate indicators to improve prenatal diagnosis. This article summarizes the predictors regarding biomarkers, ultrasound indicators, and magnetic resonance imaging (MRI) features. In addition, we discuss the effectiveness of joint diagnosis and the most recent research on PAS. In particular, we focus on (a) posterior placental implantation and (b) accreta after in vitro fertilization-embryo transfer, both of which have low diagnostic rates. At last, we graphically display the prenatal diagnostic indicators and each diagnostic performance.

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).

Development and validation of a magnetic resonance imaging-based nomogram for predicting invasive forms of placental accreta spectrum disorders

AIM

The aim of the study was to develop and validate a magnetic resonance imaging (MRI)-based nomogram for predicting invasive forms of placental accreta spectrum (PAS) disorders (placenta increta and percreta) with "uncertain ultrasound diagnosis."

METHODS

This was a retrospective cohort study of a primary cohort of 118 patients and a validation cohort of 65 patients with "uncertain ultrasound diagnosis," who were further evaluated by MRI. MRI signs associated with PAS disorders were analyzed between invasive and noninvasive groups by both univariate and logistic regression to construct the nomogram. The accuracy and discriminative ability of the nomogram were measured by concordance index (C-index) and calibration curve internally and externally.

RESULTS

The history of previous cesarean deliveries (odds ratio [OR], 3.27; 95% confidence interval [CI], 1.16-9.27), loss of double-line sign (OR, 9.49; 95% CI, 3.06-29.48), abnormal uterine bulging (OR, 4.05; 95% CI, 1.53-10.69), and disorganized abnormal placenta vascularity (OR, 3.38; 95% CI, 1.09-10.50) were imputed for the nomogram. The C-index of the nomogram was 0.85 for internal validation and 0.84 for external validation. Calibration curve showed good agreement with predicted risk and actual observation for both primary and validation cohort.

CONCLUSIONS

MRI can be a useful adjunct for clinical staging of patients with "uncertain ultrasound diagnosis."

Clinical factors associated with a placenta accreta spectrum

INTRODUCTION

Placenta accreta spectrum (PAS) is a life-threating obstetric complication, and prenatal prediction of PAS can decrease maternal morbidity and mortality. The aim of this prospective cohort study was to determine the clinical factors associated with PAS.

METHODS

Pregnant women who delivered at a university hospital were enrolled. Clinical data were collected from medical records, and logistic regression analyses were performed to determine which clinical factors were associated with PAS.

RESULTS

Eighty-seven (2.1%) of the 4146 pregnant women experienced PAS. Multivariable analyses revealed that a prior history of cesarean section (CS) (OR 3.3; 95% CI 1.9-5.7; p < 0.01), dilation and curettage (D&C) (OR 2.8; 95% CI 1.7-4.6; p < 0.01), hysteroscopic surgery (OR 5.7; 95% CI 2.3-14.4; p < 0.01), uterine artery embolization (UAE) (OR 44.1; 95% CI 13.8-141.0; p < 0.01), current pregnancy via assisted reproductive technology (ART) (OR 4.1; 95% CI 2.4-7.1; p < 0.01), and the presence of placenta previa in the current pregnancy (OR 13.1; 95% CI 7.9-21.8; p < 0.01) were independently associated with the occurrence of PAS.

CONCLUSION

Pregnant women who have a prior history of CS, D&C, hysteroscopic surgery, UAE, current pregnancy via ART, and the presence of placenta previa in the current pregnancy are high risk for PAS.

Prediction of placenta accreta spectrum in patients with placenta previa using clinical risk factors, ultrasound and magnetic resonance imaging findings

OBJECTIVES

To predict placental accreta spectrum (PAS) in patients with placenta previa (PP) evaluating clinical risk factors (CRF), ultrasound (US) and magnetic resonance imaging (MRI) findings.

METHODS

Seventy patients with PP were retrospectively selected. CRF were retrieved from medical records. US and MRI images were evaluated to detect imaging signs suggestive of PAS. Univariable analysis was performed to identify CRF, US and MRI signs associated with PAS considering histology as standard of reference. Receiver operating characteristic curve (ROC) analysis was performed, and the area under the curve (AUC) was calculated. Multivariable analysis was also performed.

RESULTS

At univariable analysis, the number of previous cesarean section, smoking, loss of the retroplacental clear space, myometrial thinning < 1 mm, placental lacunae, intraplacental dark bands (IDB), focal interruption of myometrial border (FIMB) and abnormal vascularity were statistically significant. The AUC in predicting PAS progressively increased using CRF, US and MRI signs (0.69, 0.79 and 0.94, respectively; p < 0.05); the accuracy of MRI alone was similar to that obtained combining CRF, US and MRI variables (AUC = 0.97) and was significantly higher (p < 0.05) than that combining CRF and US (AUC = 0.83). Multivariable analysis showed that only IDB (p = 0.012) and FIMB (p = 0.029) were independently associated with PAS.

CONCLUSIONS

MRI is the best modality to predict PAS in patients with PP independently from CRF and/or US finding. It is reasonable to propose the combined assessment of CRF and US as the first diagnostic level to predict PAS, sparing MRI for selected cases in which US findings are uncertain for PAS.