ArticleInsights into imaging2023
An interpretable MRI-based radiomics model predicting the prognosis of high-intensity focused ultrasound ablation of uterine fibroids.
Article in Insights into imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 25 citations in OpenAlex.
- An interpretable radiomics model based on contrast‑enhanced pancreatic computed tomography for predicting the prognosis of post-acute pancreatitis diabetes mellitus.BMC medical imaging · 2026Article
- Article
- An interpretable CT-based deep learning model for predicting overall survival in patients with bladder cancer: a multicenter study.NPJ precision oncology · 2025Article
- Robust Radiomics Models for Predicting HIFU Prognosis in Uterine Fibroids Using SHAP Explanations: A Multicenter Cohort Study.Journal of imaging informatics in medicine · 2025Article
- The interpretable CT-based vision transformer model for preoperative prediction of clear cell renal cell carcinoma SSIGN score and outcome.Insights into imaging · 2025Article
- ChatGPT as an effective tool for quality evaluation of radiomics research.European radiology · 2025Article
- Ultrasound-based radiomics to predict the volume reduction rate of benign thyroid nodules after microwave ablation.Endocrine · 2025Article
- Value of a combined magnetic resonance-enhanced and diffusion-weighted imaging dual-sequence radiomics model in predicting the efficacy of high-intensity focused ultrasound ablation for uterine fibroids.BMC medical imaging · 2025Article
- Intratumoral and peritumoral radiomics for preoperative prediction of the efficacy of HIFU ablation for uterine fibroids based on multiparametric MRI: a multicenter study.Frontiers in oncology · 2025Article
- A Dual-Sequence MRI-Based Radiomics Model for Predicting High-Intensity Focused Ultrasound Ablation Efficacy in Adenomyosis Treatment.International journal of women's health · 2025Article
- Interpretable multiphasic CT-based radiomic analysis for preoperatively differentiating benign and malignant solid renal tumors: a multicenter study.Abdominal radiology (New York) · 2024Article
- Radiomics and quantitative multi-parametric MRI for predicting uterine fibroid growth.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Multimodal MRI radiomics-based stacking ensemble learning model with automatic segmentation for prognostic prediction of HIFU ablation of uterine fibroids: a multicenter study.Frontiers in physiology · 2024Article
- The value of multi-parameter radiomics combined with imaging features in predicting the therapeutic efficacy of HIFU treatment for uterine fibroids.Frontiers in oncology · 2024Article
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate 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).
methodsThis 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.
resultsThe 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.
conclusionMultiparametric 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. CLINICAL RELEVANCE STATEMENT: 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.
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