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

Ultrasound versus magnetic resonance imaging features in diagnosing placenta accreta: A systematic review and meta-analysis

OBJECTIVE

The purpose of this study is to conduct a complete analysis of the accuracy of ultrasound and MRI in detecting placenta accreta spectrum (PAS) disorders, as well as to investigate the accuracy of independent imaging findings in these diseases.

METHODS

Pubmed, Web of Science, Embase, The Cochrane Library, and Google Scholar databases were searched from their establishment to January 1, 2025. Included were all studies that used both ultrasonography and MRI to diagnose pregnant women with PAS disorder. The ability of ultrasonography, MRI, and their independent features to diagnose PAS was evaluated using pooled sensitivity, specificity, diagnostic odds ratio (DOR), and receiver operating curves (ROC). Heterogeneity was calculated using Cochran Q and I2 statistics, and the sources of heterogeneity were investigated using meta-regression and subgroup analysis.

RESULTS

Following a series of rigorous assessments, the meta-analysis comprised 1989 pregnant women from 30 studies. The sensitivity and specificity of ultrasonography for the diagnosis of PAS were 0.87 (95 % CI, 0.82-0.90) and 0.83 (95 % CI, 0.77-0.88), respectively, whereas the sensitivity and specificity of MRI for the same diagnostic were 0.87 (95 % CI, 0.82-0.90) and 0.84 (95 % CI, 0.80-0.88). Intraplacental lacunae has the best diagnostic accuracy of ultrasound, while placental bulge has the highest diagnostic accuracy of MRI, with their area under the curve (AUC) of the ROC being 0.76 (95 % CI, 0.72-0.79) and 0.89 (95 % CI, 0.85-0.91), respectively.

CONCLUSION

The diagnostic accuracy of ultrasound and MRI for PAS was similar. However, radiographic findings should not be utilized to make an independent diagnosis of PAS disorders.

Predicting placenta accreta spectrum and high postpartum hemorrhage risk using radiomics from T2-weighted MRI

BACKGROUND

Antenatal diagnosis of placenta accreta spectrum (PAS) is of critical importance, considering that women have much better outcomes when delivery occurs at a level III or IV maternal care facility before labor initiation or bleeding, thus avoiding placental disruption. Herein, we aimed to investigate the performance of magnetic resonance imaging (MRI) in antenatal prediction of PAS and postpartum hemorrhage (PPH).

METHODS

This retrospective study included 433 women with singleton pregnancies (PAS group, n = 208; non-PAS group, n = 225; PPH-positive (PPH (+)) group, n = 80; PPH-negative (PPH (-)) group, n = 353), who were randomly divided into a training set and a test set in a 7:3 ratio. Radiomic features were extracted from T2WI (T2-weighted imaging). Features strongly correlated with PAS and PPH (p < 0.05) were selected using Pearson correlation, followed by LASSO regression for dimensionality reduction. Subsequently, radiomics models were constructed for PAS and PPH risk prediction, respectively. Regression analyses were conducted using radiomics score (R-score) and clinical factors to identify independent clinical risk factors for PAS and PPH, leading to the development of corresponding clinical models. Next, clinical-radiomics models were built by combining R-score and clinical risk factors. The predictive performance of the models was evaluated using nomograms, calibration curves, and decision curves.

RESULTS

The clinical-radiomics models and radiomics models for predicting PAS and PPH risk both outperformed their clinical models in the training and testing sets. For PAS, the AUC (Area Under the Receiver Operating Characteristic Curve) of the clinical-radiomics model, radiomics model, and clinical model in the training set are 0.918, 0.908, and 0.755, respectively, and in the testing set, the AUCs are 0.885, 0.866, and 0.771, respectively. For PPH, the AUCs of the clinical-radiomics model, radiomics model, and clinical model in the training set are 0.918, 0.884, and 0.723, respectively, and in the testing set, the AUCs are 0.905, 0.860, and 0.688, respectively. The DeLong test p-values between the clinical-radiomics models and radiomics models for predicting PAS and PPH are both less than 0.05. Additionally, in the testing set, the clinical-radiomics models perform best in predicting PAS and PPH risk, with accuracies of 82.31% and 84.61%, respectively.

CONCLUSION

This novel clinical-radiomics model has a robust performance in predicting PAS antepartum and predicting massive PPH in pregnancies.

Radiomics analysis of placental MRI for prenatal prediction of placenta accreta spectrum in pregnant women in the third trimester: A retrospective study of 594 patients

OBJECTIVE

To develop and validate a model based on placental MRI for the prenatal prediction of placenta accreta spectrum (PAS) in pregnant women in the third trimester.

MATERIALS AND METHODS

A total of 594 pregnant women who were suspected of having PAS and underwent placental MRI antenatally were included and were allocated into the training cohort and testing cohort at a 2:1 ratio. MRI diagnosis was determined by three experienced radiologists. Radiomic features were extracted from images of T2 weighted imaging for each patient. After a feature selection strategy, a radiomics signature and a clinical-radiomics nomogram combining radiomics score and clinical risk factors were constructed to predict PAS. The performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and clinical utility.

RESULTS

MRI diagnosis yielded AUCs of 0.77 and 0.79 for predicting PAS in the training and testing cohorts, respectively. The AUCs of the radiomics signature used to predict PAS in both cohorts were 0.80 and 0.83, respectively. The nomogram accurately predicted PAS in both cohorts (AUC = 0.84 and 0.89), with better results than those of MRI diagnosis and radiomics signature in the training (p = 0.009 and 0.003, respectively) and testing cohorts (p = 0.010 and 0.008, respectively), decision curve analysis confirmed its best clinical utility compared to the other models.

CONCLUSION

Radiomics analysis based on placental MRI may serve as an effective tool to predict PAS in patients with possible PAS in the third trimester.

Comparison of MRI and CT based deep learning radiomics analyses and their combination for diagnosing intrahepatic cholangiocarcinoma

Intrahepatic cholangiocarcinoma (iCCA) and other subtypes of primary liver cancer (PLC) have overlapping clinical manifestations and radiological characteristics. The objective of this study was to evaluate the efficacy of deep learning (DL) radiomics analysis, performed using computed tomography (CT) and magnetic resonance imaging (MRI), in diagnosing iCCA within PLC. 178 pathologically confirmed PLC patients (training cohort: test cohort = 124: 54) who underwent both CT and MRI examinations was enrolled. Univariate and multivariate analysis was used to identify the significant factors of radiological findings for diagnosing iCCA. DL radiomics analysis was applied to CT and MRI images, respectively. We constructed and evaluated six distinct models: CT DL radiomics (DLRSCT), CT radiological (RCT), CT DL radiomics-radiological (DLRRCT), MRI DL radiomics (DLRSMRI), MRI radiological (RMRI) and MRI DL radiomics-radiological (DLRRMRI). To further explore the diagnostic and predictive value of a cross-modal approach, we developed a fused model that combined DLRRCT and DLRRMRI. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were employed to compare the performance of different models. MRI-based models demonstrated a superior predictive performance than CT-based models in test cohort (AUCs of MRI vs. CT: DLRR, 0.923 vs. 0.880, P = 0.521; DLRS, 0.875 vs. 0.867, P = 0.922; R, 0.859 vs. 0.840, P = 0.808). The CT-MRI cross-modal model yielded the highest AUC of 0.994 and 0.937 in training and test cohorts, respectively. CT- and MRI-based DL radiomics analyses exhibited good performance in diagnosing iCCA, and the CT-MRI cross-modal model may have significant clinical implications on detection of liver malignancies.

Association between deep learning radiomics based on placental MRI and preeclampsia with fetal growth restriction: A multicenter study

PURPOSE

Preeclampsia (PE) is associated with placental insufficiency and could lead to adverse pregnancy outcomes. The study aimed to develop a placental T2-weighted image-based automatic quantitative model for the identification of PE pregnancies and disease severity.

METHODS

Between July 2013 and September 2022, the retrospective multicenter study featured 420 pregnant women, including 140 cases of PE and 280 cases of normotensive pregnancies. The semi-supervised approach was used to gain an automatic segmentation for placental MRI. The radiomics, deep learning, and deep learning radiomics (DLR) models were built.

RESULTS

In PE pregnancies, 65 (46.4 %) fetuses developed PE with fetal growth restriction (FGR), and 75 (53.6 %) cases were PE without FGR. The Dice of semi-supervised placental segmentation was 0.917. The AUCs of the DLR signature for discriminating PE pregnancies from normotensive pregnancies were 0.839 (95 % CI: 0.793-0.886), 0.858 (95 % CI: 0.742-0.974), 0.888 (95 % CI: 0.783-0.992), and 0.843 (95 % CI: 0.731-1.000) in the training, test, internal validation, and external validation sets, respectively. This DLR analysis model performed well in discriminating between PE with FGR and normotensive pregnancies (AUC = 0.918, 95 % CI: 0.879-0.957) and PE without FGR (AUC = 0.742, 95 % CI: 0659-0.824).

CONCLUSION

The automatic radiomics analysis has been developed to identify PE pregnancies by determining DLR features on placental T2-weighted images, and to predict FGR exposed to PE.

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.

MR Imaging of Placenta Accreta Spectrum: A Comprehensive Literature Review of the Most Recent Advancements

This article delves into the latest MR imaging developments dedicated to diagnosing placenta accreta spectrum (PAS). PAS, characterized by abnormal placental adherence to the uterine wall, is of paramount concern owing to its association with maternal morbidity and mortality, particularly in high-risk pregnancies featuring placenta previa and prior cesarean sections. Although ultrasound (US) remains the primary screening modality, limitations have prompted heightened emphasis on MR imaging. This review underscores the utility of quantitative MR imaging, especially where US findings prove inconclusive or when maternal body habitus poses challenges, acknowledging, however, that interpreting placenta MR imaging demands specialized training for radiologists.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Prenatal Diagnosis of Placenta Accreta Spectrum Disorders: Deep Learning Radiomics of Pelvic MRI

BACKGROUND

Diagnostic performance of placenta accreta spectrum (PAS) by prenatal MRI is unsatisfactory. Deep learning radiomics (DLR) has the potential to quantify the MRI features of PAS.

PURPOSE

To explore whether DLR from MRI can be used to identify pregnancies with PAS.

STUDY TYPE

Retrospective.

POPULATION

324 pregnant women (mean age, 33.3 years) suspected PAS (170 training and 72 validation from institution 1, 82 external validation from institution 2) with clinicopathologically proved PAS (206 PAS, 118 non-PAS).

FIELD STRENGTH/SEQUENCE

3-T, turbo spin-echo T2-weighted images.

ASSESSMENT

The DLR features were extracted using the MedicalNet. An MRI-based DLR model incorporating DLR signature, clinical model (different clinical characteristics between PAS and non-PAS groups), and MRI morphologic model (radiologists' binary assessment for the PAS diagnosis) was developed. These models were constructed in the training dataset and then validated in the validation datasets.

STATISTICAL TESTS

The Student t-test or Mann-Whitney U, χ2 or Fisher exact test, Kappa, dice similarity coefficient, intraclass correlation coefficients, least absolute shrinkage and selection operator logistic regression, multivariate logistic regression, receiver operating characteristic (ROC) curve, DeLong test, net reclassification improvement (NRI) and integrated discrimination improvement (IDI), calibration curve with Hosmer-Lemeshow test, decision curve analysis (DCA). P < 0.05 indicated a significant difference.

RESULTS

The MRI-based DLR model had a higher area under the curve than the clinical model in three datasets (0.880 vs. 0.741, 0.861 vs. 0.772, 0.852 vs. 0.675, respectively) or MRI morphologic model in training and independent validation datasets (0.880 vs. 0.760, 0.861, vs. 0.781, respectively). The NRI and IDI were 0.123 and 0.104, respectively. The Hosmer-Lemeshow test had nonsignificant statistics (P = 0.296 to 0.590). The DCA offered a net benefit at any threshold probability.

DATA CONCLUSION

An MRI-based DLR model may show better performance in diagnosing PAS than a clinical or MRI morphologic model.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

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

BACKGROUND

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

PURPOSE

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

STUDY TYPE

Retrospective.

SUBJECTS

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

FIELD STRENGTH/SEQUENCE

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

ASSESSMENT

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

STATISTICAL TESTS

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

RESULTS

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

DATA CONCLUSION

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

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

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.

A CT-Based Radiomics Nomogram Combined with Clinic-Radiological Characteristics for Preoperative Prediction of the Novel IASLC Grading of Invasive Pulmonary Adenocarcinoma

RATIONALE AND OBJECTIVES

The novel International Association for the Study of Lung Cancer (IASLC) grading system of invasive lung adenocarcinoma (ADC) demonstrated a remarkable prognostic effect and enabled numerous patients to benefit from adjuvant chemotherapy. We sought to build a CT-based nomogram for preoperative prediction of the IASLC grading.

MATERIALS AND METHODS

This work retrospectively analyzed the CT images and clinical data of 303 patients with pathologically confirmed invasive ADC. The histological subtypes and radiological characteristics of the patients were re-evaluated. Radiomics features were extracted, and the optimal subset of features was established by ANOVA, spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses identified the independent clinical and radiological variables. Finally, multivariate logistic regression analysis incorporated clinical, radiological, and optimal radiomics features into the nomogram. Receiver operating characteristic (ROC) curve, and accuracy were applied to assess the model's performance. Decision curve analysis (DCA), and calibration curve were applied to assess the clinical usefulness.

RESULTS

Nine selected CT image features were used to develop the radiomics model. The accuracy, precision, sensitivity, and specificity of the radiomics model outperformed the clinic-radiological model in the training and testing sets. Integrating Radscore with independent radiological characteristics showed higher prediction performance than clinic-radiological characteristics alone in the training (AUC, 0.915 vs. 0.882; DeLong, p < 0.05) and testing (AUC, 0.838 vs. 0.782; DeLong, p < 0.05) sets. Good calibration and decision curve analysis demonstrated the clinical usefulness of the nomogram.

CONCLUSION

Radiomics features effectively predict high-grade ADC. The combined nomogram may facilitate selecting patients who benefit from adjuvant treatment.

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.