3D MR neurography with gadolinium contrast to improve the visualization of pelvic nerves and the branches.

Objective: To evaluate the effectiveness of 3D NerveVIEW sequence with gadolinium contrast on the visualization of pelvic nerves and their branches compared to that without contrast. Methods: Participants were scanned twice using 3D NerveVIEW sequence with and without gadolinium contrast to acquire pelvic nerve images. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and contrast ratio of the nerves were calculated and compared to determine the quality of images. To subjectively assess, using a 3-point scale, branch nerves critical to therapeutic decision-making, including the pelvic splanchnic nerve and pelvic plexus, the superior gluteal nerve, and the pudendal nerve. Results: In the 32 eligible participants after using contrast, the CNRs of the images of nerve-to-bone and nerve-to-vessel significantly increased (p < 0.05). The CR of the images with contrast of all nerve-to-surrounding tissues (i.e., bone, muscle, blood vessels, and fat) were also found significantly higher (p < 0.05). The assessment of observers also shows higher scores for images with contrast compared to images without contrast. Conclusion: The 3D NerveVIEW sequence combined with gadolinium contrast improved vascular suppression, increased the contrast between pelvic nerves and surrounding tissue, and enhanced the visualization of nerves and their branches. This study may be helpful for the technically challenging preoperative planning of pelvic diseases surgery.

The Predictive Effect of Quantitative Analysis of Signal Intensity Heterogeneity on T2-Weighted MR Images for High-intensity Focused Ultrasound Treatment of Uterine Fibroids.

RATIONALE AND OBJECTIVES: To investigate whether the quantitative index of signal intensity (SI) heterogeneity on T2-weighted (T2W) magnetic resonance images can predict the difficulty and efficacy of high-intensity focused ultrasound (HIFU) ablation for uterine fibroids. MATERIALS AND METHODS: The standard deviation (SD) of T2W image (T2WI) SI was used to quantify SI heterogeneity. The correlation between SD and the non-perfused volume ratio (NPVR) in 575 patients undergoing HIFU treatment was retrospectively analyzed, and the efficacy of SD in predicting NPVR was discussed. Three classifications were made based on the SD, and the ablation difficulty and ablation effect of different grades were compared. A total of 65 cases from another center were used as an external validation set to verify the classification performance of SD. RESULTS: The SD of SI was negatively correlated with NPVR (r = -0.460, p < 0.001). The predictive efficiency of SD for the ablation effect was higher than that of the scaled signal intensity (0.767 vs. 0.701, p = 0.006). Univariate and multivariate logistic regression analyses showed that SD was an independent predictor of ablation effect. Based on SD, the three classifications were divided into SD I: SD < 101.0, SD II: 101.0 ≤ SD < 138.7, and SD III: SD≥ 138.7. The treatment time, sonication time, treatment intensity, and total energy of SD I were lower than those of SD II and III (p < 0.05). CONCLUSION: The heterogeneity of T2WI SI of uterine fibroids is negatively correlated with NPVR. The SD of SI can be used to predict the ablation difficulty and ablation effect of HIFU.

Different multiparametric MRI-based radiomics models for differentiating stage IA endometrial cancer from benign endometrial lesions: A multicenter study.

Purpose: The aim of this study was to evaluate the value of different multiparametric MRI-based radiomics models in differentiating stage IA endometrial cancer (EC) from benign endometrial lesions. Methods: The data of patients with endometrial lesions from two centers were collected. The radiomics features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) map, and late contrast-enhanced T1-weighted imaging (LCE-T1WI). After data dimension reduction and feature selection, nine machine learning algorithms were conducted to determine which was the optimal radiomics model for differential diagnosis. The univariate analyses and logistic regression (LR) were performed to reduce valueless clinical parameters and to develop the clinical model. A nomogram using the radscores combined with clinical parameters was developed. Two integrated models were obtained respectively by the ensemble strategy and stacking algorithm based on the clinical model and optimal radiomics model. The area under the curve (AUC), clinical decisive curve (CDC), net reclassification index (NRI), and integrated discrimination index (IDI) were used to evaluate the performance and clinical benefits of the models. Results: A total of 371 patients were incorporated. The LR model was the optimal radiomics model with the highest average AUC (0.854) and accuracy (0.802) in the internal and external validation groups (AUC = 0.910 and 0.798, respectively), and outperformed the clinical model (AUC = 0.739 and 0.592, respectively) or the radiologist (AUC = 0.768 and 0.628, respectively). The nomogram (AUC = 0.917 and 0.802, respectively) achieved better discrimination performance than the optimal radiomics model in two validation groups. The stacking model (AUC = 0.915) and ensemble model (AUC = 0.918) had a similar performance compared with the nomogram in the internal validation group, whereas the AUCs of the stacking model (AUC = 0.792) and ensemble model (AUC = 0.794) were lower than those of the nomogram and radiomics model in the external validation group. According to the CDC, NRI, and IDI, the optimal radiomics model, nomogram, stacking model, and ensemble model achieved good net benefits. Conclusions: Multiparametric MRI-based radiomics models can non-invasively differentiate stage IA EC from benign endometrial lesions, and LR is the best machine learning algorithm. The nomogram presents excellent and stable diagnostic efficiency.