Objectives: To develop and evaluate magnetic resonance imaging (MRI)‐based radiomics models for predicting residual myoma regrowth within 1 year after high‐intensity focused ultrasound (HIFU) ablation of uterine myomas.Methods: A retrospective analysis of residual myoma regrowth within 1 year was performed on 428 myomas in 339 patients who were diagnosed with uterine myoma and treated with HIFU ablation in two hospital centers. In total, 851 radiomics features were extracted from T2‐weighted images (T2WI) obtained 1 day after HIFU ablation, and the least absolute shrinkage and selection operator in the training cohort (n = 243) was employed to select radiomics features. Support vector machines were adopted to develop radiomics, clinicoradiological and combined radiomics–clinical models to predict residual myoma regrowth, defined as an increase in residual myoma volume of > 10% between that at day 1 post HIFU and that at follow‐up MRI within 1 year. These models were validated in both internal (n = 81) and external (n = 104) test cohorts. The predictive performance and clinical application of these models were assessed using receiver‐operating‐characteristics‐curve analysis, the area under the curve (AUC) and decision‐curve analysis.Results: The AUCs of the T2WI‐based radiomics prediction model in the internal and external test cohorts were 0.834 (95% CI, 0.747–0.920) and 0.801 (95% CI, 0.712–0.889), respectively, and those of the clinicoradiological model were 0.888 (95% CI, 0.816–0.960) and 0.912 (95% CI, 0.851–0.973), respectively. The combined model had better predictive performance than either the radiomics or the clinicoradiological model, with AUC values of 0.922 (95% CI, 0.857–0.987) and 0.930 (95% CI, 0.880–0.980) in the internal and external test cohorts, respectively. Decision‐curve analysis also indicated that application of the combined model has clinical value, this model achieving more net benefits than the other two models.Conclusion: T2WI‐based radiomics features can predict effectively the occurrence of residual myoma regrowth within 1 year after HIFU ablation of uterine myomas, which serves as an accurate and convenient reference for clinical decision‐making. © 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Objectives: To develop and apply magnetic resonance imaging (MRI) parameter-based machine learning (ML) models to predict non-perfused volume (NPV) reduction and residual regrowth of uterine fibroids after high-intensity focused ultrasound (HIFU) ablation. Methods: MRI data of 573 uterine fibroids in 410 women who underwent HIFU ablation from the Chongqing Haifu Hospital (training set, N = 405) and the First Affiliated Hospital of Chongqing Medical University (testing set, N = 168) were retrospectively analyzed. Fourteen MRI parameters were screened for important predictors using the Boruta algorithm. Multiple ML models were constructed to predict NPV reduction and residual fibroid regrowth in a median of 203.0 (interquartile range: 122.5–367.5) days. Furthermore, optimal models were used to plot prognostic prediction curves. Results: Fourteen features, including postoperative NPV, indicated predictive ability for NPV reduction. Based on the 10-fold cross-validation, the best average performance of multilayer perceptron achieved with R² was 0.907. In the testing set, the best model was linear regression (R² =0.851). Ten features, including the maximum thickness of residual fibroids, revealed predictive power for residual fibroid regrowth. Random forest model achieved the best performance with an average area under the curve (AUC) of 0.904 (95% confidence interval (CI), 0.869–0.939), which was maintained in the testing set with an AUC of 0.891 (95% CI, 0.850–0.929). Conclusions: ML models based on MRI parameters can be used for prognostic prediction of uterine fibroids after HIFU ablation. They can potentially serve as a new method for learning more about ablated fibroids.

Type 2 diabetes mellitus (T2DM) poses a significant global health challenge. While pharmacological interventions exist, increasing attention has been directed toward novel non-invasive therapeutic strategies. The pancreas plays a crucial role in T2DM pathogenesis, and its dysfunction is closely linked to mitochondrial abnormalities and oxidative stress. This study investigated the therapeutic potential of low-intensity pulsed ultrasound (LIPUS) on pancreatic protection in db/db mice, a model of T2DM. Mice were divided into sham-irradiated (db/db sham) and LIPUS-treated (db/db LIPUS) groups, with db/m mice as controls. The pancreatic region received daily LIPUS (0.37 MHz, 200 mW/cm²) for 45 days. Metabolic parameters, pancreatic histopathology, and ultrastructure were assessed alongside molecular analyses including proteomics and Western blot. LIPUS treatment significantly improved diabetic phenotypes, reducing random blood glucose (P < 0.05) and body weight (P < 0.05) while ameliorating islet hyperplasia and mitochondrial abnormalities. Importantly, LIPUS enhanced insulin sensitivity (homeostatic model assessment of insulin resistance reduction, P < 0.05) and reduced pancreatic apoptosis (BCL-2-associated X/B-cell lymphoma-2, P < 0.05) and oxidative stress (4-hydroxynonenal reduction, P < 0.05). Proteomic analysis revealed LIPUS-mediated regulation of energy metabolism pathways, particularly downregulation of oxidative phosphorylation components including cytochrome c oxidase subunit 7A2, ubiquinol-cytochrome c reductase core III subunit Q, and ATP synthase subunit O (all P < 0.05), confirmed by Western blot. These findings demonstrate that LIPUS alleviates pancreatic injury in diabetic mice primarily by modulating mitochondrial oxidative phosphorylation activity and subsequent oxidative damage. The study establishes LIPUS as a promising non-invasive therapeutic strategy for T2DM through its protective effects on pancreatic tissue, warranting further clinical investigation.