Predicting high-intensity focused ultrasound efficacy in adenomyosis treatment based on magnetic resonance (MR) radiomics and clinical-imaging features

Z Liu, X Wan, Y Wang, X Huang
Clinical radiology · 2025 · vol. 81 , pp. 106778 · doi:10.1016/j.crad.2024.106778 · PMID:39798274
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A combined model using radiomics from enhanced T1WI/T2WI-FS sequences and clinical-imaging features effectively predicts high-intensity focused ultrasound efficacy in adenomyosis treatment.

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The paper studied how MR radiomics features together with clinical and imaging variables can predict efficacy of high-intensity focused ultrasound (HIFU) treatment for adenomyosis, using pre-treatment magnetic resonance information to build a predictive model. The authors report that combinations of radiomics and selected clinical-imaging factors are associated with treatment response, aiming to improve prediction of high-intensity focused ultrasound outcomes. A key limitation is that the work is based on the study’s available dataset and its retrospective/observational design constraints as implied by the framing of predictive modeling in a single investigation, which can limit generalizability. This paper is centrally about endometriosis and/or adenomyosis—specifically adenomyosis—predicting HIFU efficacy using MR radiomics and clinical-imaging features.

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Abstract

AIMS: To develop a model predicting high-intensity focused ultrasound (HIFU) efficacy in adenomyosis treatment using enhanced T1WI and T2WI-FS radiomics combined with clinical imaging features. MATERIALS AND METHODS: The study included 137 adenomyosis patients treated with HIFU from September 2021 to December 2023. Based on nonperfused volume ratio (NPVR), participants were divided into two groups: NPVR < 50% (n=77) and NPVR ≥ 50% (n=60). Patients were randomly split into training and test sets (7:3 ratio). Radiomics features were extracted from enhanced T1WI and T2WI-FS sequences, while clinical imaging features were selected using univariate analysis and binary logistic regression. Logistic regression models were built for radiomics, clinical imaging, and combined data. Model performance was assessed using ROC curves, Delong's test, and calibration curves. RESULTS: AUCs for the radiomics, clinical-imaging, and combined models in the training set were 0.831, 0.664, and 0.845, respectively, and 0.829, 0.597, and 0.831 in the test set. The combined model outperformed the clinical-imaging model (training p=0.001, test p=0.01) and the radiomics model (training p=0.012, test p=0.032). However, no significant difference was found between the combined and radiomics models (p>0.05). Calibration curves and decision curve analysis confirmed the combined model's accuracy and clinical applicability. CONCLUSION: A model incorporating clinical-imaging features with T1WI and T2WI-FS radiomics effectively predicts HIFU success in adenomyosis treatment, offering valuable guidance for clinical decision-making.
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Predicting high-intensity focused ultrasound efficacy in adenomyosis treatment based on magnetic resonance (MR) radiomics and clinical-imaging features Affiliations & Notes Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China Article Info Publication History: Received August 22, 2024; Revised November 9, 2024; Accepted December 9, 2024; Published online December 14, 2024 DOI: 10.1016/j.crad.2024.106778 External LinkAlso available on ScienceDirect External Link Copyright: © 2024 The Royal College of Radiologists. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Linked Articles Download started OkReferences 1. Li, Z. ∙ Zhang, J. ∙ Song, Y. ... Utilization of radiomics to predict long-term outcome of magnetic resonance-guided focused ultrasound ablation therapy in adenomyosis Eur Radiol. 2021; 31:392-402 2. Upson, K. ∙ Missmer, S.A. 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adenomyosis

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Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis

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