Abstract
This study aimed to develop a model based on radiomics and deep learning features to predict the ablation rate in patients with adenomyosis undergoing high-intensity focused ultrasound (HIFU) therapy. A total of 119 patients with adenomyosis who received HIFU therapy were retrospectively analyzed. Participants were included in the training and testing queues in a 7:3 ratio. Radiomics features were extracted from T2-weighted imaging (T2WI) images, and VGG-19 was used to extract advanced deep features. An ensemble model based on multi-model fusion for predicting the efficacy of HIFU in adenomyosis was proposed, which consists of four base classifiers and was evaluated using accuracy, precision, recall, F-score, and area under the receiver operating characteristic curve (AUC). The predictive performance of the combined model combining radiomics and deep learning features outperformed the radiomics and deep learning feature models alone, with accuracy of 0.848 and 0.814 in training and test sets, and AUC of 0.916 and 0.861, respectively. Compared with the base classifiers that make up the multi-model fusion model, the fusion model also exhibited better prediction performance. The fusion model incorporating both radiomics and deep learning features had certain predictive value for the ablation rate of adenomyosis under HIFU therapy and could help select patients with adenomyosis who would benefit from HIFU therapy.
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Data Availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Funding
This work was supported by the grants from the Shanghai Science and Technology Innovation Action Plan (No. 22S31903700) and grants from Shanghai Hospital Development Center-United Imaging Joint Research & Development Plan (No. 2022SKLY-12).
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Ying, J., Jing, X., Gao, F. et al. Prediction of Ablation Rate for High-Intensity Focused Ultrasound Therapy of Adenomyosis in MR Images Based on Multi-model Fusion. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01063-4
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DOI: https://doi.org/10.1007/s10278-024-01063-4