BACKGROUND: We aimed to evaluate changes in ovarian reserve and quality of life in women treated with ultrasound-guided high-intensity focused ultrasound (USgHIFU) for uterine fibroids.
METHODS: In this single-center prospective study, a total of 69 patients with uterine fibroids treated with USgHIFU from October 2018 to November 2021 were enrolled. Fibroid volume, anti-Müllerian hormone (AMH) levels, uterine fibroid symptom scores, and uterine fibroid symptoms and quality of life (UFS-QOL) questionnaire scores before and 1, 3, and 6 months after USgHIFU treatment were analyzed. Correlations between AMH levels and age, fibroid type, and fibroid location were assessed.
RESULTS: Data from 54 of the 69 patients included in the present study were analyzed. The UFS-QOL scores at baseline and at 1 month and 6 months after USgHIFU treatment were 70 (50.75-87.50), 57 (44.75-80.00), and 52 (40.75-69.00) points, respectively (p < 0.001). The rate of fibroid volume reduction increased significantly at the 3-month follow-up compared with the 1-month follow-up (p < 0.001), and no significant change was observed between the 3-month and 6-month follow-ups (p > 0.99). The median AMH levels before and at 1, 3 and 6 months after treatment were 1.22 (0.16-3.28) ng/ml, 1.12 (0.18-2.52) ng/ml, 1.15 (0.19-2.08) ng/ml and 1.18 (0.36-2.43) ng/ml, respectively (p = 0.2). Multivariate linear regression analyses revealed that age was independently associated with AMH levels.
CONCLUSIONS: USgHIFU treatment for uterine fibroids can significantly improve quality of life with minimal adverse effects on ovarian function.

OBJECTIVE: Uterine fibroids (UF) are the most frequent tumors in ladies and can pose an enormous threat to complications, such as miscarriage. The accuracy of prognosis may also be affected by way of doctor inexperience and fatigue, underscoring the want for automatic classification fashions that can analyze UF from a giant wide variety of images.
METHODS: A hybrid model has been proposed that combines the MobileNetV2 community and deep convolutional generative adversarial networks (DCGAN) into useful resources for medical practitioners in figuring out UF and evaluating its characteristics. Real-time automated classification of UF can aid in diagnosing the circumstance and minimizing subjective errors. The DCGAN science is utilized for superior statistics augmentation to create first-rate UF images, which are labeled into UF and non-uterine-fibroid (NUF) classes. The MobileNetV2 model then precisely classifies the photos based totally on this data.
RESULTS: The overall performance of the hybrid model contrasts with different models. The hybrid model achieves a real-time classification velocity of 40 frames per second (FPS), an accuracy of 97.45%, and an F1 rating of 0.9741.
CONCLUSIONS: By using this deep learning hybrid approach, we address the shortcomings of the current classification methods of uterine fibroid.

UNASSIGNED: To quantify the reintervention rate and analyze the risk factors for reintervention after high-intensity focused ultrasound (HIFU) ablation of uterine fibroids.
UNASSIGNED: Eighteen studies were selected from the seven databases. A meta-analysis was applied to synthesize the reintervention rates for fibroids across various follow-up durations. Subgroup-analysis was conducted based on the year of surgery, sample size, guide methods, and non-perfusion volume ratio (NPVR). Signal intensity of T2-weighted imaging (T2WI) was independently evaluated for reintervention risk.
UNASSIGNED: The study enrolled 5216 patients with fibroids treated with HIFU. There were 3247, 1239, 1762, and 2535 women reaching reintervention rates of 1% (95% confidence interval (CI): 1-1), 7% (95% CI: 4-11), 19% (95% CI: 11-27), and 29% (95% CI: 14-44) at 12, 24, 36, and 60-month after HIFU. The reintervention rates of patients treated with US-guided HIFU (USgHIFU) were significantly lower than those of patients treated with MR-guided focused ultrasound surgery (MRgFUS). When the NPVR of fibroids was over 50%, the reintervention rates at 12, 36 and 60-month after HIFU were 1% (95% CI: 0.3-2), 5% (95% CI: 3-8), and 15% (95% CI: 9-20). The reintervention risk for hyper-intensity fibroids on T2WI was 3.45 times higher (95% CI: 2.7-4.39) for hypo-/iso-intensity fibroids.
UNASSIGNED: This meta-analysis showed that the overall reintervention rates after HIFU were acceptable and provided consultative suggestions regarding treatment alternatives for patients with fibroids. Subgroup-analysis revealed that USgHIFU, NPVR ≥ 50%, and hypo-/iso-intensity of fibroids on T2WI were significant factors in reducing reintervention.
UNASSIGNED: PROSPERO, CRD42023456094.

UNASSIGNED: To investigate the factors which may cause thermal injury of abdominal skin in patients with uterine fibroids (UFs) who underwent ultrasound-guided focused ultrasound ablation surgery (FUAS).
UNASSIGNED: A total of 123 patients were enrolled in the injury group. In contrast, 246 patients without thermal injury were assigned to the non-injury group. The relationship between patient and treatment parameters and injury were explored using univariate analysis and multiple logistic regression analyses. In addition, the factors influencing the degree of thermal injury were analyzed using Kruskal-Wallis H.
UNASSIGNED: (1) Abdominal scars (p = .007, OR = 2.187, 95% CI: 1.242-3.849), abdominal wall thickness (p < .001, OR = 1.042, 95% CI: 1.019-1.067), fundus fibroids (p = .038, OR = 1.790, 95% CI: 1.033-3.100), UFs with hyperintense/mixed T2-weighted imaging (T2WI) signals (p = .022, OR = 1.843, 95% CI: 1.091-3.115), average sonication power (AP) (p = .025, OR = 1.021, 95% CI: 1.003-1.039), and treatment time (TT) (p < .001, OR = 1.017, 95% CI: 1.011-1.023) were independent risk factors for thermal injury, while treatment volume (TV) (p = .002, OR = 0.775, 95% CI: 0.661-0.909) was a protective factor for injury. (2) Four groups were subdivided according to the degree of thermal injury(Group A: without skin injury. Group B: with changed T2WI signal in the abdominal wall, Group C: mild skin injury, Group D: severe skin injury), comparison of each with every other showed that the abdominal wall in Groups A and D was thinner than Groups B and C, with statistically significant differences (PAB<0.05, PAC<0.01, PDC<0.05, PDB<0.05); Group A was slightly thicker than D, however, without statistical difference. The ratio of sonication time (ST) to TV in Group A was the lowest of all (PAB, PAC, PAD all < 0.05). And as the level of thermal injury rose, the ratio gradually increased, however, without statistical difference.
UNASSIGNED: Based on our limited results, the following conclusion was made. (1) Abdominal scars, abdominal wall thickness, fundus fibroids, UFs with T2WI hyperintense/mixed signals, AP and TT were independent risk factor. (2) Neither too thick nor too thin abdominal walls would be recommended, as both might increase the risk of skin injury. (3) Noticeably, the risk of skin injury might increase considerably when the ST was longer and the sonication area was more fixed.

UNASSIGNED: Uterine fibroids (UFs) are the most common benign tumors in women of reproductive age. The most effective treatment is myomectomy, but there is no long-term or low-invasive treatment option exists. Acupuncture can be used to treat UFs in a variety of ways. However, there is no meta-analytic synthesis including valid data that explored the efficacy of acupuncture for UFs.
UNASSIGNED: To assess the efficacy and safety of acupuncture for treating UFs.
UNASSIGNED: The PRISMA 2020 checklist was used. We identified and extracted the trials through may 2023 from six databases. The quality of the trials was assessed using the risk of bias (2.0). Meta-analysis was performed using RevMan 5.4 software, and it was synthesized using the random-effects model if the included studies were in high heterogeneity. Subgroup and sensitivity analysis were used if necessary.
UNASSIGNED: A total of 1,035 trials were identified, of which 11 were included in the review and meta-analysis. In terms of acupuncture scheme design and fibroid-related symptoms, the trials are highly heterogeneous. All 11 trials have reported acupuncture types, with traditional acupuncture and electroacupuncture being the more representative subgroups. A qualitative review of existing evidence shows that acupuncture has no serious adverse reaction on UFs. Meta-analysis shows that acupuncture can effectively reduce the volume of UFs (MD - 3.89, 95% CI - 5.23 to - 2.56, P < 0.00001) or uterine volume (MD - 16.22, 95% CI - 19.89 To - 12.55, p < 0.00001), reduce the score of fibroid symptoms (MD - 3.03, 95% CI - 3.45 to - 2.60, p < 0.00001), improve the treatment efficiency (RR: 0.19, 95% CI: 0.13 to 0.25, p < 0.00001), and likely do not affect the estrogen level.

BACKGROUND: Prediction of non-perfusion volume ratio (NPVR) is critical in selecting patients with uterine fibroids who will potentially benefit from ultrasound-guided high-intensity focused ultrasound (HIFU) treatment, as it reduces the risk of treatment failure. The purpose of this study is to construct an optimal model for predicting NPVR based on T2-weighted magnetic resonance imaging (T2MRI) radiomics features combined with clinical parameters by machine learning.
METHODS: This retrospective study was conducted among 223 patients diagnosed with uterine fibroids from two centers. The patients from one center were allocated to a training cohort (n = 122) and an internal test cohort (n = 46), and the data from the other center (n = 55) was used as an external test cohort. The least absolute shrinkage and selection operator (LASSO) algorithm was employed for feature selection in the training cohort. The support vector machine (SVM) was adopted to construct a radiomics model, a clinical model, and a radiomics-clinical model for NPVR prediction, respectively. The area under the curve (AUC) and the decision curve analysis (DCA) were performed to evaluate the predictive validity and the clinical usefulness of the model, respectively.
RESULTS: A total of 851 radiomic features were extracted from T2MRI, of which seven radiomics features were screened for NPVR prediction-related radiomics features. The radiomics-clinical model combining radiomics features and clinical parameters showed the best predictive performance in both the internal (AUC = 0.824, 95% CI 0.693-0.954) and external (AUC = 0.773, 95% CI 0.647-0.902) test cohorts, and the DCA also suggested the radiomics-clinical model had the highest net benefit.
CONCLUSIONS: The radiomics-clinical model could be applied to the NPVR prediction of patients with uterine fibroids treated by HIFU to provide an objective and effective method for selecting potential patients who would benefit from the treatment mostly.

Long non-coding RNAs (lncRNAs) play crucial regulatory roles in various cellular processes, including gene expression, chromatin remodeling, and protein localization. Dysregulation of lncRNAs has been linked to several diseases, making it essential to understand their functions in disease mechanisms and therapeutic strategies. However, traditional experimental methods for studying lncRNA function are time-consuming, expensive, and offer limited insights. In recent years, computational methods have emerged as valuable tools for predicting lncRNA functions and their associations with diseases. However, many existing methods focus on constructing separate networks for lncRNA and disease similarity, resulting in information loss and insufficient processing capacity for isolated nodes. To address this, we developed \'RGLD\' by combining Random Walk with restarting (RWR), Graph Neural Network (GNN), and Graph Attention Networks (GAT) to predict lncRNA-disease associations in a heterogeneous network. RGLD achieved an impressive AUC of 0.88, outperforming other methods. It can also predict novel associations between lncRNAs and diseases. RGLD identified HOTAIR, MEG3, and PVT1 as lncRNAs associated with uterine fibroids. Biological experiments directly or indirectly verified the involvement of these three lncRNAs in uterine fibroids, validating the accuracy of RGLD\'s predictions. Furthermore, we extensively discussed the functions of the target genes regulated by these lncRNAs in uterine fibroids, providing evidence for their role in the development and progression of the disease.

BACKGROUND: Accurate preoperative assessment of the efficacy of high-intensity focused ultrasound (HIFU) ablation for uterine fibroids is essential for good treatment results. The aim of this study was to develop robust radiomics models for predicting the prognosis of HIFU-treated uterine fibroids and to explain the internal predictive process of the model using Shapley additive explanations (SHAP).
METHODS: This retrospective study included 300 patients with uterine fibroids who received HIFU and were classified as having a favorable or unfavorable prognosis based on the postoperative nonperfusion volume ratio. Patients were divided into a training set (N = 240) and a test set (N = 60). The 1295 radiomics features were extracted from T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) scans. After data preprocessing and feature filtering, radiomics models were constructed by extreme gradient boosting and light gradient boosting machine (LightGBM), and the optimal performance was obtained by Bayesian optimization. Finally, the SHAP approach was used to explain the internal prediction process.
RESULTS: The models constructed using LightGBM had the best performance, and the AUCs of the T2WI and CE-T1WI models were 87.2 (95% CI = 87.1-87.5) and 84.8 (95% CI = 84.6-85.7), respectively. The use of SHAP technology can help physicians understand the impact of radiomic features on the predicted outcomes of the model from a global and individual perspective.
CONCLUSIONS: Multiparametric radiomic models have shown their robustness in predicting HIFU prognosis. Radiomic features can be a potential source of biomarkers to support preoperative assessment of HIFU treatment and improve the understanding of uterine fibroid heterogeneity.
CONCLUSIONS: An interpretable radiomics model can help clinicians to effectively predict the prognosis of HIFU treatment for uterine fibroids. The heterogeneity of fibroids can be characterized by various radiomics features and the application of SHAP can be used to visually explain the prediction process of radiomics models.

UNASSIGNED: To evaluate the significance of magnetic resonance (MR) intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) quantitative parameters in predicting early efficacy of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids before treatment.
UNASSIGNED: 64 patients with 89 uterine fibroids undergoing HIFU ablation (51 sufficient ablations and 38 insufficient ablations) were enrolled in the study and completed MR imaging and IVIM-DWI before treatment. The IVIM-DWI parameters, including D (diffusion coefficient), D* (pseudo-diffusion coefficient), f (perfusion fraction) and relative blood flow (rBF) were calculated. The logistic regression (LR) model was constructed to analyze the predictors of efficacy. The receiver operating characteristic (ROC) curve was drawn to assess the model\'s performance. A nomograph was constructed to visualize the model.
UNASSIGNED: The D value of the sufficient ablation group (931.0(851.5-987.4) × 10-6 mm2/s) was significantly lower than that of the insufficient ablation group (1052.7(1019.6-1158.7) × 10-6 mm2/s) (p<0.001). However, differences in D*, f, and rBF values between the groups were not significant (p>0.05). The LR model was constructed with D value, fibroid position, ventral skin distance, T2WI signal intensity, and contrast enhanced degree. The area under the ROC curve, specificity, and sensitivity of the model were 0.858 (95% confidence interval: 0.781, 0.935), 0.686, and 0.947. The nomogram and calibration curves confirmed that the model had excellent performance.
UNASSIGNED: The IVIM-DWI quantitative parameters can be used to predict early effects of HIFU ablation on uterine fibroids. A high D value before treatment may indicate that the treatment will be less effective in the early stages.

To predict the risk of postoperative reintervention for uterine fibroids using clinical-imaging features and T2WI radiomics before high-intensity focused ultrasound (HIFU) ablation.
Among patients with uterine fibroids treated with HIFU from 2019 to 2021, 180 were selected per the inclusion and exclusion criteria (42 reintervention and 138 non-reintervention). All patients were randomly assigned to either the training (n = 125) or validation (n = 55) cohorts. Multivariate analysis was used to determine independent clinical-imaging features of reintervention risk. The Relief and LASSO algorithm were used to select optimal radiomics features. Random forest was used to construct the clinical-imaging model based on independent clinical-imaging features, the radiomics model based on optimal radiomics features, and the combined model incorporating the above features. An independent test cohort of 45 patients with uterine fibroids tested these models. The integrated discrimination index (IDI) was used to compare the discrimination performance of these models.
Age (p < .001), fibroid volume (p = .001) and fibroid enhancement degree (p = .001) were identified as independent clinical-imaging features. The combined model had AUCs of 0.821 (95% CI: 0.712-0.931) and 0.818 (95% CI: 0.694-0.943) in the validation and independent test cohorts, respectively. The predictive performance of the combined model was 27.8% (independent test cohort, p < .001) and 29.5% (independent test cohort, p = .001) better than the clinical-imaging and radiomics models, respectively.
The combined model can effectively predict the risk of postoperative reintervention for uterine fibroids before HIFU ablation. It is expected to help clinicians to develop accurate, personalized treatment and management plans. Future studies will need to be prospectively validated.