Segmentation methods applied to MRI-derived radiomic analysis for the prediction of placenta accreta spectrum in patients with placenta previa

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.

Prediction of hemorrhage in placenta previa: Radiomics analysis of pelvic MRI images

INTRODUCTION

Prediction of intraoperative massive hemorrhage is still challenging in placenta previa. Radiomics analysis has been investigated as a new evaluation method for analyzing medical images. We used radiomics analysis on placental magnetic resonance imaging (MRI) images to predict intraoperative hemorrhage in placenta previa.

METHODS

We used the sagittal MRI T2-weighted sequence in placenta previa. Using the rectangular region from the uterine os to the anterior wall, we extracted 97 radiomics features. We also collected patient demographics and blood test data as clinical variables. Combining these radiomics features and clinical variables, logistic regression models with a stepwise method were built to predict the risk of hemorrhage, defined as blood loss of > 2000 ml. We evaluated the prediction performance of the models using accuracy and area under the curve (AUC), also analyzing the important variables for the prediction by stepwise methods.

RESULTS

We enrolled a total of 63 placenta previa cases including 30 hemorrhage cases from two institutes. The model combining clinical variables and radiomics features showed the best prediction performance with an accuracy of 0.70 and an AUC of 0.69 in the internal validation data, and accuracy of 0.41 and an AUC of 0.70 in the external validation data, compared with human experts (accuracy of 0.62). Regarding variable selection, two radiomics features. 'original_glrlm_LowGrayLevelRunEmphasis,' and 'diagnostics_Image-original_Minimum,' were important predictors for hemorrhage by the stepwise method.

DISCUSSION

Radiomics features based on MRI could be used as effective predictive variables for hemorrhage in placenta previa. Radiomics analysis of placental imaging could lead to further analysis of quantitative variables related to obstetric diseases.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

A novel MRI-based diagnostic model for predicting placenta accreta spectrum

Objective To develop and evaluate a diagnostic model based on MRI signs for predicting placenta accreta spectrum. Materials and Methods A total of 155 pregnant women were included in this study, randomly divided into 104 cases in the training set and 51 cases in the validation set. There were 93 Non-PAS cases, and 62 cases in the PAS group. The training set included 62 Non-PAS cases and 42 PAS cases. Clinical factors and MRI signs were collected for univariate analysis. Then, binary logistic regression analysis was used to develop independent diagnostic models with clinical relevant risk factors or MRI signs, as well as those combining clinical risk factors and MRI signs. The ROC curve analysis was used to evaluate the diagnostic performance of each diagnostic model. Finally, the validation was performed with the validation set. Results In the training set, four clinical factors (gestity, parity, uterine surgery history, placental position) and 11 MRI features (T2-dark bands, placental bulge, T2 hypointense interface loss, myometrial thinning, bladder wall interruption, focal exophytic mass, abnormal placental bed vascularization, placental heterogeneity, asymmetric placental thickening/shape, placental ischemic infarction, abnormal intraplacental vascularity) were considered as risk factors for PAS. The AUC of the clinical diagnostic model, MRI diagnostic model, and clinical + MRI model of PAS were 0.779, 0.854, and 0.874, respectively. In the validation set, the AUC of the clinical diagnostic model, MRI diagnostic model, and clinical + MRI model of PAS were 0.655, 0.728, and 0.735, respectively. Conclusion Diagnosis model based on MRI features in this study can well predict placenta accreta spectrum.