Prenatal planning of placenta previa: diagnostic accuracy of a novel MRI-based prediction model for placenta accreta spectrum (PAS) and clinical outcome

Predicting intraoperative blood loss during cesarean sections based on multi-modal information: a two-center study

PURPOSE

To develop and validate a nomogram model that combines radiomics features, clinical factors, and coagulation function indexes (CFI) to predict intraoperative blood loss (IBL) during cesarean sections, and to explore its application in optimizing perioperative management and reducing maternal morbidity.

METHODS

In this retrospective consecutive series study, a total of 346 patients who underwent magnetic resonance imaging (156 for training and 68 for internal test, center 1; 122 for external test, center 2) were included. IBL+ was defined as more than 1000 mL estimated blood loss during cesarean sections. The prediction models of IBL were developed based on machine-learning algorithms using CFI, radiomics features, and clinical factors. ROC analysis was performed to evaluate the performance for IBL diagnosis.

RESULTS

The support vector machine model incorporating all three modalities achieved an AUC of 0.873 (95% CI 0.769-0.941) and a sensitivity of 1.000 (95% CI 0.846-1.000) in the internal test set, with an AUC of 0.806 (95% CI 0.725-0.872) and a sensitivity of 0.873 (95% CI 0.799-0.922) in the external test set. It was also scored significantly higher than the CFI model (P = 0.035) on the internal test set, and both the CFI (P = 0.002) and radiomics-CFI models (P = 0.007) on the external test set. Additionally, the nomogram constructed based on three modalities achieved an internal testing set AUC of 0.960 (95% CI 0.806-0.999) and an external testing set AUC of 0.869 (95% CI 0.684-0.967) in the pregnant population without a pernicious placenta previa. It is noteworthy that the AUC of the proposed model did not show a statistically significant improvement compared to the Clinical-CFI model in both internal (P = 0.115) and external test sets (P = 0.533).

CONCLUSION

The proposed model demonstrated good performance in predicting intraoperative blood loss (IBL), exhibiting high sensitivity and robust generalizability, with potential applicability to other surgeries such as vaginal delivery and postpartum hysterectomy. However, the performance of the proposed model was not statistically significantly better than that of the Clinical-CFI model.

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 Model Based on Multisequence MRI Images for Assessing Adverse Pregnancy Outcome in Placenta Accreta

BACKGROUND

Preoperative assessment of adverse outcomes risk in placenta accreta spectrum (PAS) disorders is of high clinical relevance for perioperative management and prognosis.

PURPOSE

To investigate the association of preoperative MRI multisequence images and adverse pregnancy outcomes by establishing a deep learning model in patients with PAS.

STUDY TYPE

Retrospective.

POPULATION

323 pregnant women (age from 20 to 46, the median age is 33), suspected of PAS, underwent MRI to assess the PAS, divided into the training (N = 227) and validation datasets (N = 96).

FIELD STRENGTH/SEQUENCE

1.5T scanner/fast imaging employing steady-state acquisition sequence and single shot fast spin echo sequence.

ASSESSMENT

Different deep learning models (i.e., with single MRI input sequence/two sequences/multisequence) were compared to assess the risk of adverse pregnancy outcomes, which defined as intraoperative bleeding ≥1500 mL and/or hysterectomy. Net reclassification improvement (NRI) was used for quantitative comparison of assessing adverse pregnancy outcome between different models.

STATISTICAL TESTS

The AUC, sensitivity, specificity, and accuracy were used for evaluation. The Shapiro-Wilk test and t-test were used. A P value of <0.05 was considered statistically significant.

RESULTS

215 cases were invasive placenta accreta (67.44% of them with adverse outcomes) and 108 cases were non-invasive placenta accreta (9.25% of them with adverse outcomes). The model with four sequences assessed adverse pregnancy outcomes with AUC of 0.8792 (95% CI, 0.8645-0.8939), with ACC of 85.93% (95%, 84.43%-87.43%), with SEN of 86.24% (95% CI, 82.46%-90.02%), and with SPC of 85.62% (95%, 82.00%-89.23%) on the test cohort. The performance of model with four sequences improved above 0.10 comparing with that of model with two sequences and above 0.20 comparing with that of model with single sequence in terms of NRI.

DATA CONCLUSION

The proposed model showed good diagnostic performance for assessing adverse pregnancy outcomes.

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.

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

PURPOSE

To build and validate a predictive model of placental accreta spectrum (PAS) in patients with placenta previa (PP) combining clinical risk factors (CRF) with US and MRI signs.

METHOD

Our retrospective study included patients with PP from two institutions. All patients underwent US and MRI examinations for suspicion of PAS. CRF consisting of maternal age, cesarean section number, smoking and hypertension were retrieved. US and MRI signs suggestive of PAS were evaluated. Logistic regression analysis was performed to identify CRF and/or US and MRI signs associated with PAS considering histology as the reference standard. A nomogram was created using significant CRF and imaging signs at multivariate analysis, and its diagnostic accuracy was measured using the area under the binomial ROC curve (AUC), and the cut-off point was determined by Youden's J statistic.

RESULTS

A total of 171 patients were enrolled from two institutions. Independent predictors of PAS included in the nomogram were: 1) smoking and number of previous CS among CRF; 2) loss of the retroplacental clear space at US; 3) intraplacental dark bands, focal interruption of the myometrial border and placental bulging at MRI. A PAS-prediction nomogram was built including these parameters and an optimal cut-off of 14.5 points was identified, showing the highest sensitivity (91%) and specificity (88%) with an AUC value of 0.95 (AUC of 0.80 in the external validation cohort).

CONCLUSION

A nomogram-based model combining CRF with US and MRI signs might help to predict PAS in PP patients, with MRI contributing more than US as imaging evaluation.

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%.

Uterus-preserving surgical management of placenta accreta spectrum disorder: a large retrospective study

BACKGROUND

The two-child policy implemented in China resulted in a surge of high-risk pregnancies among advanced maternal aged women and presented a window of opportunity to identify a large number of placenta accreta spectrum (PAS) cases, which often invoke severe blood loss and hysterectomy. We thus had an opportunity to evaluate the surgical outcomes of a unique conservative PAS management strategy for uterus preservation, and the impacts of magnetic resonance imaging (MRI) in PAS surgical planning.

METHODS

Cross-sectional study, comparing the outcomes of a new uterine artery ligation combined with clover suturing technique (UAL + CST) with the existing conservative surgical approaches in a maternal public hospital with an annual birth of more than 20,000 neonates among all placenta previa cases suspecting of PAS between January 1, 2015 and December 31, 2018.

RESULTS

From a total of 89,397 live births, we identified 210 PAS cases from 400 singleton pregnancies with placenta previa. Aside from 2 self-requested natural births (low-lying placenta), all PAS cases had safe cesarean deliveries without any total hysterectomy. Compared with the existing approaches, the evaluated UAL + CST had a significant reduction in intraoperative blood loss (β=-312 ml, P < .001), RBC transfusion (β=-1.08 unit, P = .001), but required more surgery time (β = 16.43 min, P = .01). MRI-measured placenta thickness, when above 50 mm, can increase blood loss (β = 315 ml, P = .01), RBC transfusion (β = 1.28 unit, P = .01), surgery time (β = 48.84 min, P < .001) and hospital stay (β = 2.58 day, P < .001). A majority of percreta patients resumed normal menstrual cycle within 12 months with normal menstrual fluid volume, without abnormal urination or defecation.

CONCLUSIONS

A conservative surgical management approach of UAL + CST for PAS is safe and effective with a low complication rate. MRI might be useful for planning PAS surgery.

CLINICAL TRIAL REGISTRATION NUMBER

ChiCTR2000035202.

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.

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.

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.

Assessment of postpartum haemorrhage for placenta accreta: Is measurement of myometrium thickness and dark intraplacental bands using MRI helpful?

Background

This study aimed to investigate the predictive values of magnetic resonance imaging (MRI) myometrial thickness grading and dark intraplacental band (DIB) volumetry for blood loss in patients with placenta accreta spectrum (PAS).

Methods

Images and clinical data were acquired from patients who underwent placenta MRI examinations and were diagnosed with PAS from March 2015 to January 2021. Two radiologists jointly diagnosed, processed, and analysed the MR images of each patient. The analysis included MRI-based determination of placental attachment, as well as myometrial thickness grading and DIB volumetry. The patients included in the study were divided into three groups according to the estimated blood loss volume: in the general blood loss (GBL) group, the estimated blood loss volume was < 1000 ml; in the massive blood loss (MBL) group, the estimated blood loss volume was ≥ 1000 ml and < 2000 ml; and in the extremely massive blood loss (ex-MBL) group, the estimated blood loss volume was ≥ 2000 ml. The categorical, normally distributed, and non-normally distributed data were respectively analysed by the Chi-square, single-factor analysis of variance, and Kruskal–Wallis tests, respectively. The verification of correlation was completed by Spearman correlation analysis. The evaluation capabilities of indicators were assessed using receiver operating characteristic curves.

Results

Among 75 patients, 25 were included in the GBL group, 26 in the MBL group, and 24 in the ex-MBL group. A significant negative correlation was observed between the grade of myometrial thickness and the estimated blood loss (P < 0.001, ρ = − 0.604). There was a significant positive correlation between the volume of the DIB and the estimated blood loss (P < 0.001, ρ = 0.653). The areas under the receiver operating characteristic curve of the two MRI features for predicting blood loss ≥ 2000 ml were 0.776 and 0.897, respectively.

Conclusions

The grading and volumetric MRI features, myometrial thickness, and volume of DIB, can be used as good prediction indicators of the risk of postpartum haemorrhage in patients with PAS.

The maximum length of T2-dark intraplacental bands may help predict intraoperative haemorrhage in pregnant women with placenta accreta spectrum (PAS)

PURPOSE

To investigate the relationship between the maximum length of T2-dark intraplacental bands (MLTIB) and intraoperative haemorrhage in pregnant women with placenta accreta spectrum (PAS).

METHODS

Between February 2018 and February 2021, 86 pregnant women with PAS who delivered in Taizhou Hospital of Zhejiang Province and underwent preoperative magnetic resonance imaging (MRI) examination were retrospectively recruited. The presence of T2-dark intraplacental bands, placental/uterine bulge, loss of retroplacental T2-hypointense line, myometrial thinning, bladder wall interruption, focal exophytic mass, and abnormal vascularization of placental bed were recorded, and the MLTIB was measured. The relative risk ratios of the MRI findings and intraoperative bleeding were measured. Receiver operating characteristic (ROC) analysis was used to evaluate the ability of the MLTIB to help predict intraoperative haemorrhage in pregnant women with PAS.

RESULTS

Of the 86 pregnant women, 32 had intraoperative blood loss ≥ 1000 ml; of these, 18 had intraoperative blood loss ≥ 2000 ml. Abnormal vascularization of placental bed was associated with the highest relative risk ratio for the detection of intraoperative haemorrhage (RR = 10.66), followed by the presence of T2-dark intraplacental bands (RR = 8.02). The optimal cut-off of the MLTIB for predicting intraoperative haemorrhage (≥ 1000 ml) in pregnant women with PAS was 28.95 mm, and the AUC was 0.91 (sensitivity: 84%; specificity: 91%). The optimal cut-off of the MLTIB for predicting massive intraoperative haemorrhage (≥ 2000 ml) was 35.65 mm, and the AUC was 0.94 (sensitivity: 89%; specificity: 85%).

CONCLUSION

MLTIB was related to intraoperative haemorrhage in pregnant women with PAS. An MLTIB greater than 28.95 mm is an effective predictor of intraoperative haemorrhage. An MLTIB of 35.65 mm or greater strongly suggests the possibility of massive intraoperative haemorrhage.

A Case of a Patient with Adhesive Small Bowel Obstruction in Pregnancy after Extensive Myomectomy for Diffuse Uterine Leiomyomatosis

Background. Diffuse uterine leiomyomatosis is a rare disease in which countless, poorly defined, and small nodules are present in most parts of the uterine myometrium. It frequently occurs in fertile women and causes infertility. A deep, median, longitudinal incision of the uterine corpus with the opening of the endometrial cavity, “extensive myomectomy,” is required to restore fertility. However, myomectomy may also be a risk factor for perinatal complications. We present a rare case of adhesive small bowel obstruction after extensive myomectomy for diffuse uterine leiomyomatosis. Case. A 37-year-old primigravida presented with sharp epigastric pain and vomiting at 21-week gestation. The patient had a history of extensive myomectomy for diffuse uterine leiomyomatosis. Abdominal radiography revealed moderate air fluid levels in the small intestine, and the patient was diagnosed with adhesive small bowel obstruction. The patient was also diagnosed with placenta previa. Bowel rest with intestinal tube was continued until delivery. Cesarean section was performed at 32-week gestation due to (i) prolonged fasting and total parenteral nutrition for conservative treatment and (ii) fear of sudden massive bleeding from placenta previa. Because the ileum was strongly adherent to the uterine scar from the previous myomectomy, adhesiolysis and enterectomy were performed. The placenta was uncomplicatedly delivered and the hemorrhage was well-controlled. Conclusions. Pregnancy with a history with extensive myomectomy for diffuse uterine leiomyomatosis should be carefully monitored because of the occasional occurrence of adhesive small intestine obstruction during pregnancy.

Predicting Placenta Accreta Spectrum Disorders in a Cohort of Pregnant Patients in the North-East Region of Romania-Diagnostic Accuracy of Ultrasound and Magnetic Resonance Imaging

Background: Placenta accreta spectrum (PAS) disorders are associated with high mortality and morbidity due to postpartum hemorrhage, hysterectomy, and organ injury, and a multidisciplinary team is required for an individualized case management. In this study, we assessed the diagnostic and prognostic accuracy of the most important ultrasonographic (US) and magnetic resonance imagining (MRI) markers for PAS disorders. Material and Methods: The study included 39 adult pregnant patients with at least one previous cesarean delivery and both US and MRI investigations for placenta previa evaluated at the tertiary maternity hospital ‘Cuza Voda’, Iasi, between 2019 and 2021. The following US signs were evaluated: intra-placental lacunae, loss of the retroplacental hypoechoic zone, myometrial thinning < 1 mm, bladder wall interruption, placental bulging, bridging vessels, and the hypervascularity of the uterovesical or retroplacental space. The MRI signs that were evaluated were intra-placental dark T2 bands, placental bulging, loss of the retroplacental hypointense line on T2 images, myometrial thinning, bladder wall interruption, focal exophytic placental mass, and abnormal vascularization of the placental bed. Results: The US and MRI signs analyzed in our study presented adequate sensitivities and specificities for PAS, but no sign proved to be a useful predictor by itself. The presence of three or more US markers for accretion was associated with a sensitivity of 84.6.6% and a specificity of 92.3% (p < 0.001). The presence of three or more MRI signs supplemented these results and were associated with a sensitivity of 92.3% and a specificity of 61.5% for predicting PAS (p < 0.001). Moreover, US and MRI findings were correlated with FIGO grading and severity of PAS. Conclusions: Even though no US or MRI finding alone can predict PAS with high sensitivity and specificity, our study proves that the presence of three or more imagistic signs could significantly increase the diagnostic accuracy of this condition. Furthermore, US and MRI could be useful tools for evaluating prognostic and perinatal planning.

Acute pelvic pain: A pictorial review with magnetic resonance imaging

Acute uterine emergencies constitute both obstetric and gynecologic conditions. The superior image resolution, superior soft-tissue characterization, and lack of ionizing radiation make magnetic resonance imaging (MRI) preferable over ultrasonography (USG) and computed tomography (CT) in investigating uterine emergencies. Although USG is the first-line imaging modality and is easily accessible, it has limitations. USG is an operator dependent and limited by patient factors such as obesity and muscle atrophy. CT is limited by its risk of teratogenicity in pregnant females, poor tissue differentiation, and radiation effect. The non-specific findings on CT may lead to misinterpretation of the pathology. MRI overcomes all these limitations and is emerging as the most crucial imaging modality in the emergency room (ER). The evolving 3D MR sequences further reduce the acquisition times, expanding its ER role. Although MRI is not the first-line imaging modality, it is a problem-solving tool when the ultrasound and CT are inconclusive. This pictorial review discusses the various MRI techniques used in uterine imaging and the appearances of distinct etiologies of uterine emergencies across different MRI sequences.

Effectiveness of a fetal magnetic resonance imaging scoring system for predicting the prognosis of pernicious placenta previa: A retrospective study

Background: The value of multiple magnetic resonance imaging (MRI) signs in predicting pernicious placenta previa (PPP) with placenta accreta spectrum disorders (PAS) is still controversial. This study aimed to investigate the value of a self-made fetal magnetic resonance imaging scoring system in predicting the different types of PAS in pernicious placenta previa and its associated risk of bleeding.

Methods: This retrospective study included 193 patients diagnosed with PPP based on MRI findings before delivery. Based on pathological and intraoperative findings, we divided patients into four groups: non-PAS, placental adhesion, placental implantation, and placenta percreta. Receiver operator characteristic curves of the MRI total score and placental implantation type were drawn using pROC packages in the R Studio environment, and cutoff values of each type were calculated, as well as diagnostic evaluation indexes, such as sensitivity, specificity, and the Youden index. Hemorrhage during surgery was compared between the groups.

Results: The boundary value between the non-PAS and placental adhesion was 5.5, that between placental adhesion and placental implantation was 11.5, and that between placental implantation and placenta percreta was 15.5 points. The respective specificities were 0.700, 0.869, and 0.958, and the respective sensitivities were 0.994, 0.802, and 0.577. The Youden indices were 0.694, 0.671, and 0.535, respectively. The median (minimum, maximum) quantities of hemorrhage during the operation in the non-PAS, placental adhesion, placental implantation, and placenta percreta groups were 225 (100, 3700), 600 (200, 6000), 1500 (300, 7000), and 3000 (400, 6300) ml, respectively. Hemorrhage was significantly different between the four groups (p < 0.001).

Conclusion: These results suggest that the proposed MRI scoring system could be an effective diagnostic tool for assessing PPP types and predicting the associated bleeding risk.

Prospective comparative analysis for application and selection of FIESTA sequence and SSFSE sequence in MRI for prenatal diagnosis of placenta previa accreta

Placenta previa accreta patients were examined using fast-imaging employing steady-state acquisition (FIESTA) and single-shot fast spin echo (SSFSE) sequence. The diagnostic value of the two sequences was compared. FIESTA was better than the SSFSE sequence in displaying outline-boundary (excellent: 82 vs. 26), signal-to-noise ratio (excellent: 75 vs. 54) for placenta and uterus. The direct signs detection rate in FIESTA was higher than SSFSE (implantable: P = .028, adhesive: P = .131, penetrating type: P = .326). The indirect signs detection rate in FIESTA was lower than SSFSE (low-signal density: P = .029, uneven-signal density: P = .328, thicker and more vascular shadow: P = 398). FIESTA combining SSFSE demonstrated higher detecting rates (100% for sensitivity, specificity, and accuracy) for all types than single sequence scanning (FIESTA/SSFSE). In conclusion, FIESTA clearly showed the situation of the placenta and uterus in placenta previa accreta patients, with excellent image quality. A combination of FIESTA and SSFSE can improve the diagnostic value of placenta previa accreta.Important statementWhat is already known on this subject? Placenta previa is the most common cause of vaginal bleeding in the third trimester of pregnancy.What do the results of this study add? FIESTA was better than the SSFSE sequence in displaying images and demonstrated higher detection rates for direct signs and lower detection rate comparing the SSFSE sequence. FIESTA combining SSFSE sequence demonstrated higher detecting rates for implantable, adhesive and penetrating types than single sequence scanning.What are the implications of these findings for clinical practice and/or further research? FIESTA sequence clearly showed the situation of placenta and uterus in placenta previa accreta patients, with excellent image quality. Combination of FIESTA and SSFSE sequences can effectively improve the diagnostic value of placenta previa accreta.

The MRI estimations of placental thickness and cervical length correlate with postpartum hemorrhage (PPH) in patients with risk for placenta accreta spectrum (PAS) disorders

INTRODUCTION

This study aims to identify whether placental thickness and cervical length measured by MRI correlate with postpartum hemorrhage (PPH) in patients at high risk for placenta accreta spectrum (PAS) disorders.

METHODS

The placental thickness and cervical length of 200 patients from October 2017 to October 2021 were retrospectively measured. The mid-sagittal plane of the placentas was measured by 2 independent radiologists using MRI. Partial correlation analysis was used to characterize the correlation between placental thickness, cervical length and estimated blood loss during surgery. The correlation between clinical features, placental thickness, cervical length and PPH was evaluated with univariate and multivariate analyses. A nomogram was constructed based on the logistic regression.

RESULTS

Placental thickness was positively correlated with the estimated blood loss during delivery, while cervical length had a negative correlation with it, based on the adjustment for gestational age. Multivariate analyses revealed that prior cesarean section, placenta previa, increased placental thickness(≧4.35 cm) and short cervical length（< 3.05 cm） were independent risk factors for PPH. When the 4 risk factors were combined together, the AUC was the highest, 0.773 (95%CI 0.707-0.840).

DISCUSSION

Placental thickness and cervical length correlated with PPH. The nomogram constructed based on prior cesarean section, placenta previa, placental thickness and cervical length can be used to recognize patients with a higher risk of PPH.

Bleeding-related outcomes of low-risk total placenta previa are equivalent to those of partial/marginal placenta previa

OBJECTIVE

To clarify whether "low-risk total PP" patients bleed more than partial/marginal PP patients.

MATERIALS AND METHODS

The retrospective cohort study was performed involving patients with PP between April 2006 and December 2018. The placental position was determined by ultrasound. From medical charts, the backgrounds as well as obstetric and neonatal outcomes of PP patients were retrieved.

RESULTS

This study included 349 patients with PP, which was classified into three types according to the distance between the placenta and internal ostium: total (n = 174), partial (n = 52), and marginal (n = 123) PP. In total PP patients, three factors (prior CS, anterior placenta, and placental lacunae on ultrasound) significantly increased blood loss at CS, the need for hysterectomy, homologous transfusion (≥10 U), and ICU admission. No significant difference was observed in bleeding-related poor outcomes (rate of blood loss ≥2000 mL, amount of homologous transfusion, need for hysterectomy, and ICU admission) between total PP patients without all three factors: "low-risk total PP patients" and partial/marginal PP patients (19.8 vs. 17.1%; p = 0.604, 3.7 vs. 1.1%; p = 0.330, 1.2 vs. 1.1%; p = 1.000, and 1.2 vs. 1.1%; p = 1.000, respectively).

CONCLUSION

Prior CS, anterior placenta, and placental lacunae on ultrasound were risk factors for a bleeding-related poor outcome in total PP patients. Total PP patients without these three factors showed the same bleeding-related poor outcome as partial/marginal PP patients.

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.

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.

Comparison of Magnetic Resonance Imaging of the Lower Uterine Segment in Pregnant Women with Central Placenta Previa with and without Placenta Accreta Spectrum from a Single Center

BACKGROUND Placenta accreta spectrum (PAS) includes placenta increta, placenta percreta, and placenta accreta. PAS is due to abnormal decidualization and can lead to severe maternal hemorrhage and occurs in up to 3% of women with central placental previa (CPP). This study from a single center aimed to compare the magnetic resonance imaging (MRI) changes in the lower uterine segment in pregnant women with CPP, with and without PAS. MATERIAL AND METHODS This retrospective study includes 90 pregnant women with PAS and 66 pregnant women without PAS. All participants were confirmed to have CPP by MRI. Eight MRI parameters were assessed and compared with perinatal outcomes for mothers and newborns. RESULTS The pregnancies in the non-PAS group had less operative time (P=0.001), less intrapartum hemorrhage (P<0.001), and less blood transfusion than the PAS group (P<0.001). The 8 MRI variables with different odds ratios were placenta thickness (4.20), cervical lengths (3.27), placental dark T2 bands area (5.10), cervical marginal sinus (3.04), bladder bulge (3.55), myometrial thinning (6.41), lower uterine segment bulge (4.61), and placental signals in the cervix (9.14). The sensitivity and specificity of MRI in the diagnosis of PAS were 82.22% and 91.09%, respectively, by the combined 8 MRI features, and the area under the curve (AUC) was 0.816. CONCLUSIONS The findings from this study showed that MRI of the lower uterine segment had high sensitivity and specificity for the diagnosis of PAS in pregnant women with CPP.

Prediction for Postpartum Hemorrhage of Placenta Previa Patients through MRI Using Self-Adaptive Edge Detection Algorithm

The study aimed to explore the application value of MRI images based on the optimized self-adaptive edge detection algorithm in the diagnosis of placenta previa and in the prediction of postpartum hemorrhage. Specifically, a self-adaptive edge detection algorithm was constructed based on optimized edge operators, with the nearest scale parameters analyzed. It was then used to process the MRI images of 36 patients with placenta previa. MRI images of different types of placenta previa were analyzed. The results found that the placenta of the complete placenta previa was attached to the lower wall of the uterus and covered the internal cervix in U shape, and the placenta adhered to the anterior and lower wall of the uterus, with widespread placenta accreta noted. With the results of cesarean section as the standard, it was observed that 2 cases of complete placenta previa were diagnosed as partial placenta previa. The diagnostic accuracy rate was 94.44%, which was not notably different from the results of cesarean section (p > 0.05). The postpartum hemorrhage rate and hysterectomy rate of complete placenta previa were higher than partial placenta previa and marginal placenta previa, and the difference was notable (p < 0.05), but no notable differences were noted in placenta adhesion, placenta accreta, neonatal death, and neonatal asphyxia between the three types of placenta previa (p > 0.05). The incidence of thinned myometrium, placenta penetrating the cervix, placenta accreta, and uneven placental signal in patients with postpartum hemorrhage was higher versus those without postpartum hemorrhage, and the difference was notable (p < 0.05). In a word, MRI images based on the self-adaptive edge detection algorithm can clearly show the status of placenta previa and exhibit better diagnosis effects and a higher accuracy rate. The thinned myometrium, the placenta penetrating the cervix, placenta accreta, and uneven placental signal may be the related risk factors for postpartum hemorrhage in patients with placenta previa.

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."

YouTube as a Source of Patient Information Regarding Placenta Accreta Spectrum

OBJECTIVE

 As the awareness of the accompanying morbidity of placenta accreta spectrum (PAS) has increased over recent decades. We sought to analyze the precision and reliability of the currently available content regarding PAS on YouTube.

STUDY DESIGN

 A YouTube search was performed on June 17, 2019 by using the search terms "placenta accreta," "PAS," and "invasive placentation." Search results were sorted by relevance, and up to 200 videos per search term were systematically evaluated by four independent reviewers. A quality assessment checklist relating to aspects of PAS was developed with a Likert's scale from 0 to 12 points to quantify video content. Videos were classified as poor educational quality (grade 0 to ≤4), moderate quality (grade >4-8), and high quality (grade >8-12).

RESULTS

 Of the 318 videos identified, 99 videos met inclusion criteria. The majority of videos (61.6%) were produced by a professional source, that is, appearing to be from a hospital, university, or educational service. Of the remaining videos, 16.2% were classified as personal, that is, posted from personal YouTube accounts and depicting a personal or family member experience, and 22.2% were classified as other. The majority of the "other" category consisted of news segments and short clips from talk shows. Overall, 60.6% of videos were of poor educational quality, 32.3% were of moderate quality, and 7.1% were deemed high quality. All seven of the high-quality videos were produced by a professional source and intended for an audience of medical professionals. There were neither high-quality videos intended for the general public nor the likely affected and relevant patient population.

CONCLUSION

 This study suggests that the currently available videos on YouTube regarding PAS are poor educational sources for patients seeking information, and demonstrates a need for high-quality content videos produced by medical professionals specifically focused on meeting the needs of patient population.

KEY POINTS

· Awareness of the accompanying morbidity of placenta accreta spectrum has increased over recent decades.. · YouTube videos are poor educational sources for patients seeking information regarding PAS.. · YouTube videos and all social media warrant improvements regarding patient's information..

Deep learning and radiomics analysis for prediction of placenta invasion based on T2WI

The purpose of this study was to explore whether the Nomogram, which was constructed by combining the Deep learning and Radiomic features of T2-weighted MR images with Clinical factors (NDRC), could accurately predict placenta invasion. This retrospective study included 72 pregnant women with pathologically confirmed placenta invasion and 40 pregnant women with normal placenta. After 24 gestational weeks, all participants underwent magnetic resonance imaging. The uterus and placenta regions were segmented in magnetic resonance images on sagittal T2WI. Ninety-three radiomics features were extracted from the placenta region, and 128 deep features were extracted from the uterus region using a deep neural network. The least absolute shrinkage and selection operator (LASSO) algorithm was used to filter these 221 features and to form the combined signature. Then the combined signature (CS) and clinical factors were combined to construct a nomogram. The accuracy, sensitivity, specificity and AUC of the nomogram were compared with four machine learning methods. The model NDRC was trained on the dataset of 78 pregnant women in the training cohort. Finally, the model NDRC was compared with four machine learning methods on the independent validation cohort of 34 pregnant women. The results showed that the prediction accuracy, sensitivity, specificity and AUC of the NDRC model were 0.941, 0.952, 0.923 and 0.985 respectively, which outperforms the traditional machine learning methods which rely on radiomics features and deep learning features alone.

Accuracy and Interobserver Reliability of Magnetic Resonance Imaging for Placenta Accreta Spectrum Disorders

OBJECTIVE

This study aims to define the accuracy, predictive value, and interobserver reliability of magnetic resonance imaging (MRI) in the diagnosis of placenta accreta spectrum (PAS) disorders.

STUDY DESIGN

Two experienced radiologists independently interpreted the MRI studies of patients with possible PAS from two referral centers. Radiologists were blinded to sonographic and clinical information. We calculated diagnostic testing characteristics and kappa statistics of interobserver reliability for MRI findings of PAS.

RESULTS

Sixty-eight MRI cases were evaluated. Confirmed PAS and severe PAS were present in 44 (65%) and 20 (29%) cases. For the diagnosis of any PAS, MRI had a sensitivity 66%, specificity 71%, positive predictive value (PPV) 81%, negative predictive value (NPV) 53%, and accuracy 68%. For the diagnosis of severe PAS (percreta), MRI had a sensitivity 85%, specificity 79%, PPV 63%, NPV 93%, and accuracy 81%. The accuracy of individual signs of PAS was lower (44-65%). Interobserver agreement was almost perfect for previa; substantial for myometrial interruptions, PAS, severe PAS, and placental bulging/balling; and moderate to slight for other signs of PAS.

CONCLUSION

Although the interobserver reliability of MRI for a diagnosis of PAS is substantial, the accuracy and predictive value are modest and lower than previously reported.

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.

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

OBJECTIVE

This study aimed to quantify the magnetic resonance imaging (MRI) features of placenta accreta spectrum (PAS) and to use MRI-based scores to classify them in high-risk gravid patients.

MATERIALS AND METHODS

The clinical data and MRI features of 65 high-risk gravid patients diagnosed with PAS were retrospectively reviewed. The MRI features of PAS were analysed and compared using the chi-squared test, and the odds ratios (ORs) for significant risk factors for classification of PAS were identified via a multivariate logistic regression model. A receiver-operating characteristic (ROC) curve was used to calculate cut-off values and their corresponding sensitivity, specificity, and accuracy in classifying PAS.

RESULTS

We identified 3 significant risk features for classification of PAS, including placental heterogeneity (OR = 13.604), abnormal vascularization at the placental-maternal interface (OR = 9.528), and focal myometrial interruption (OR = 118.779). The significant risk features for classification of PAS were scored according to their OR values, as 3 points (OR ≥ 20), 2 points (10 ≤ OR < 20), or 1 point (OR < 10). Based on the scores of the 3 risk features, a cut-off score of 4.5 points achieved optimal sensitivity (94.3%), specificity (90%), and accuracy (92.3%) for classifying PAS in high-risk gravid patients.

CONCLUSION

Quantifying these MRI features including placental heterogeneity, abnormal vascularization at the placental-maternal interface, and focal myometrial interruption can make a classification of PAS in high-risk gravid patients.

Ultrasound Imaging of Abdominal Wall Endometriosis: A Pictorial Review

Endometriosis is a debilitating disease characterized by endometrial glands and stroma outside the endometrial cavity. Abdominal wall endometriosis (AWE) indicates the presence of ectopic endometrium between the peritoneum and the skin, including subcutaneous adipose tissue and muscle layers, often following obstetric and gynecological surgical procedures. AWE is a not infrequent gynecological surgical complication, due to the increasing number of cesarean sections worldwide. In this pictorial review, we discuss the importance of medical history and physical examination, including the main ultrasound features in the diagnosis of AWE.

Diagnosing uterine cervical cancer on a single T2-weighted image: Comparison between deep learning versus radiologists

PURPOSE

To compare deep learning with radiologists when diagnosing uterine cervical cancer on a single T2-weighted image.

METHODS

This study included 418 patients (age range, 21-91 years; mean, 50.2 years) who underwent magnetic resonance imaging (MRI) between June 2013 and May 2020. We included 177 patients with pathologically confirmed cervical cancer and 241 non-cancer patients. Sagittal T2-weighted images were used for analysis. A deep learning model using convolutional neural networks (DCNN), called Xception architecture, was trained with 50 epochs using 488 images from 117 cancer patients and 509 images from 181 non-cancer patients. It was tested with 60 images for 60 cancer and 60 non-cancer patients. Three blinded experienced radiologists also interpreted these 120 images independently. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were compared between the DCNN model and radiologists.

RESULTS

The DCNN model and the radiologists had a sensitivity of 0.883 and 0.783-0.867, a specificity of 0.933 and 0.917-0.950, and an accuracy of 0.908 and 0.867-0.892, respectively. The DCNN model had an equal to, or better, diagnostic performance than the radiologists (AUC = 0.932, and p for accuracy = 0.272-0.62).

CONCLUSION

Deep learning provided diagnostic performance equivalent to experienced radiologists when diagnosing cervical cancer on a single T2-weighted image.

Review of MRI imaging for placenta accreta spectrum: Pathophysiologic insights, imaging signs, and recent developments

Placenta Accreta Spectrum (PAS) refers to the range of abnormally adhesive and penetrative placental tissue at a uterine scar. PAS is divided into accreta, increta, and percreta based on degree of myometrial invasion. Its incidence has increased, and PAS is now the leading indication for emergency peripartum hysterectomy in the setting of catastrophic hemorrhage from a non-separating placenta. The recent release of the International Federation of Gynecology and Obstetrics (FIGO) guidelines in 2018 coupled with the joint consensus statement from the Society of Abdominal Radiology (SAR) and European Society of Urogenital Radiology (ESUR) in 2020 reflect decades worth of diagnostic and therapeutic advances in this field. Although the increasing role of MRI in PAS diagnosis is evident, the literature on PAS reveals several disparate but conceptually overlapping MRI signs. Identifying and differentiating between placenta increta and percreta on imaging may be quite challenging even with MRI and sometimes even on final pathology. In this review, we aim to (i) provide a clarified understanding of PAS pathophysiology, (ii) comprehensively review and classify MRI signs based on pathophysiologic underpinnings, (iii) highlight shortcomings in the current PAS literature; and (iv) highlight best practice guidelines for imaging diagnosis of PAS.

Maternal Serum VEGF Predicts Abnormally Invasive Placenta Better than NT-proBNP: a Multicenter Case-Control Study

The aim of this study was to test if maternal serum vascular endothelial growth factor (VEGF) or N-terminal pro B-type natriuretic peptide (NT-proBNP) predicts abnormally invasive placenta (AIP) better. Secondary objective was to test whether the serum levels of VEGF and NT-proBNP can predict the degree of invasion. In a multicenter case–control study design, gestational age-matched serum samples from pregnant women with AIP (n = 44) and uncomplicated pregnancies (n = 55) who had been enrolled at Charité – Universitätsmedizin Berlin, Germany and Centre Hospitalier Régional de la Citadelle in Liège, Belgium were analyzed. Maternal blood serum VEGF and NT-proBNP levels were immunoassayed from samples taken immediately before delivery (GA median: 35 weeks). Biomarker levels were compared between AIP and control group. The correlation of biomarker levels with the clinical AIP degree was assessed. The predictive biomarker ability was characterized through a multivariate regression model and receiver operating characteristic curves. Women with AIP had significantly lower maternal serum VEGF levels (AIP mean 285 pg/ml, 95% CI 248–322, vs. control: 391 pg/ml, 95% CI 356–426, p < 0.01) and higher NT-proBNP levels (AIP median 329 pg/ml, IQR 287–385, vs. control 295 pg/ml, IQR 273–356, p = 0.03). Maternal serum VEGF levels were able to predict AIP better (AUC = 0.729, 0.622–0.836, p < 0.001; VEGF + number of previous cesarean deliveries: AUC = 0.915, 0.853–0.977, p < 0.001). Maternal serum VEGF levels correlated inversely with the clinical AIP degree (r = − 0.32, p < 0.01). In short, maternal serum VEGF, more than NT-proBNP, can help in predicting AIP and hints at the degree of invasion.

The use of magnetic resonance imaging to predict placenta previa with placenta accreta spectrum

INTRODUCTION

Massive hemorrhage due to placenta previa with placenta accreta spectrum is associated with high maternal mortality and morbidity. Therefore, accurate prediction of placenta previa with placenta accreta spectrum is essential; magnetic resonance imaging (MRI) is a useful tool for this purpose. This study investigated novel predictors of anterior and posterior placenta previa with placenta accreta spectrum using MRI.

MATERIAL AND METHODS

This was a retrospective study at a tertiary obstetrics hospital in Japan. The singleton patients with placenta previa who were scanned with MRI prenatally and had a cesarean section at our institution between 2007 and 2018 were included. The prediction of anterior and posterior placenta previa with placenta accreta spectrum was evaluated using four MRI findings: heterogeneous signals in the placenta, dark T2-weighted intraplacental bands, myometrial thinning or interruption, and focal uterine bulging. The prediction of posterior placenta previa with placenta accreta spectrum was performed using the quantification of cervical varicosities, which were defined as the ratio of the distance between the minimum distance from the most dorsal cervical varicosities (a) to the deciduous and amniotic placenta (b) on a sagittal image.

RESULTS

Among 202 patients, 14 (6.9%) patients were pathologically diagnosed as having placenta accreta spectrum. Further, 38 (18.8%) patients had anterior placenta previa and 164 (81.2%) patients had posterior placenta previa. When anterior placenta previa with placenta accreta spectrum was predicted using at least one of the four MRI findings, the sensitivity and specificity of the anterior placenta previa with placenta accreta spectrum were 87.5% and 86.7%, respectively. In contrast, the sensitivity and specificity of posterior placenta previa with placenta accreta spectrum were 42.9% and 96.2%, respectively. But when the A/B ratio was set at 0.20, the sensitivity and specificity of the prediction for posterior placenta previa with placenta accreta spectrum using cervical varicosities were 100.0% and 89.2%, respectively.

CONCLUSIONS

The findings of MRI to predict the anterior placenta previa with placenta accreta spectrum were different from posterior placenta previa. The cervical varicosities may be useful in predicting posterior placenta previa with placenta accreta spectrum.

Placental magnetic resonance imaging Part II: placenta accreta spectrum

The human placenta remains an enigma to many. Its position as the point of communication between distinct maternal and fetal circulations means that it must act as both source of nourishment and gatekeeper for the developing pregnancy. In vivo assessment of the placenta is perhaps the greatest challenge, yet it is most essential for diagnostic and prognostic purposes. In particular, there is a need for improved diagnostic accuracy in recognizing the invasive forms of the placenta accreta spectrum that require surgical intervention at delivery and often cesarean hysterectomy. The costs of insufficient sensitivity and specificity are high, with well-documented cases of adverse outcomes ranging from unnecessary surgery to maternal hemorrhage and even death. In Part I of this pictorial essay series, we reviewed the appearance of the normal developing placenta across gestation by MRI. With this as a background, we here consider the varied appearances of the placenta accreta spectrum (placenta accreta, increta, percreta), which is a growing challenge given the rapidly expanding number of women worldwide with history of cesarean section delivery. Accurate prenatal imaging is crucial for recognizing cases of the placenta accreta spectrum and for planning the necessary surgery.

Placental blood flow and pregnancy outcomes in women with abnormal placental localization and absence of placental "migration"

Aim: We investigated the arcuate artery blood flow in the region of the abnormally localized placenta in women who had undergone insertion of an obstetric pessary and were receiving micronized progesterone.Materials and methods: The study included 120 pregnant women with high perinatal risks and abnormal placental localization. The patients were randomized to receive the Arabin's pessary and vaginal micronized progesterone (Group A, n = 60) or vaginal micronized progesterone only (Group B, n = 60). Randomization was carried based on the order of hospital admission: odd patient numbers were allocated to Group A and even numbers to Group B. Patients underwent a series of ultrasound scans to evaluate the placental migration and presence of abnormal placental attachment. Depending on the results of the scan, study participants were divided into the following groups: (1) patients without placental migration: A₁ (n = 23) and B₁ (n = 42); and (2) patients with placental migration: A₂ (n = 37) and B₂ (n = 18). Women in subgroups A₁ and B₁ were further divided into the subgroups based on the presence of abnormal placental attachment: A_1x (n = 5) and B_1x (n = 12) with abnormal placental attachment; and A_1O (n = 18) and B_1O (n = 30) without the abnormal placental attachment.Conclusion: In patients with abnormal placental attachment, the resistance of blood flow in the arcuate arteries was significantly higher than in those with normal placental attachment. A significant increase in the blood flow resistance occurred between 24 and 28 weeks of gestation. The combined use of the obstetric pessary and vaginal micronized progesterone in women with abnormal placental localization helped maintain the resistivity index at low levels and reduce the rate of abnormal placental attachment by 1.3-fold (OR 0.694 (95% CI: 0.21-2.29)).

Identification of suspicious invasive placentation based on clinical MRI data using textural features and automated machine learning

OBJECTIVE

The aim of this study was to investigate whether intraplacental texture features from routine placental MRI can objectively and accurately predict invasive placentation.

MATERIAL AND METHODS

This retrospective study includes 99 pregnant women with pathologically confirmed placental invasion and 56 pregnant women with simple placenta previa. All participants underwent magnetic resonance imaging after 24 gestational weeks. The placenta was segmented in sagittal images from both turbo spin echo (TSE) and balanced turbo field echo (bTFE) sequences. Textural features were extracted from the both original and Laplacian of Gaussian (LoG)-filtered MRI images. An automated machine learning algorithm was applied to the extracted feature sets to obtain the optimal preprocessing steps, classification algorithm, and corresponding hyper-parameters.

RESULTS

A gradient boosting classifier using all textual features from original and LoG-filtered TSE images and bTFE images identified by the automated machine learning algorithm achieved the optimal performance with sensitivity, specificity, accuracy, and area under ROC curve (AUC) of 100%, 88.5%, 95.2%, and 0.98 in the prediction of placental invasion. In addition, textural features that contributed to the prediction of placental invasion differ from the features significantly affected by normal placenta maturation.

CONCLUSIONS

Quantifying intraplacental heterogeneity using LoG filtration and texture analysis highlights the different heterogeneous appearance caused by abnormal placentation relative to normal maturation. The predictive model derived from automated machine learning yielded good performance, indicating the proposed radiomic analysis pipeline can accurately predict placental invasion and facilitate clinical decision-making for pregnant women with suspicious placental invasion.

KEY POINTS

• The intraplacental texture features have high efficiency in prediction of invasive placentation after 24 gestational weeks. • The features with dominated predictive power did not overlap with the features significantly affected by gestational age.