{"paper_id":"34b27972-2171-4454-ad61-c7aee60c0170","body_text":"Abstract\nThis 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.\nSimilar content being viewed by others\nData Availability\nThe data that support the findings of this study are available from the corresponding author upon reasonable request.\nReferences\nChapron C, Vannuccini S, Santulli P, et al.: Diagnosing adenomyosis: an integrated clinical and imaging approach. Hum Reprod Update 26:392-411, 2020\nStanekova V, Woodman R J, Tremellen K: The rate of euploid miscarriage is increased in the setting of adenomyosis. Hum Reprod Update 3: hoy011, 2018\nSudderuddin S, Helbren E, Telesca M, et al.: MRI appearances of benign uterine disease. Clin Radiol 69:1095-1104, 2014\nDueholm M: Minimally invasive treatment of adenomyosis. Best Pract Res Clin Obstet Gynaecol 51:119-137, 2018\nBuggio L, Dridi D, Barbara G: Adenomyosis: impact on fertility and obstetric outcomes. Reprod Sci 28:3081-3084, 2021\nYounes G, Tulandi T: Conservative surgery for adenomyosis and results: a systematic review. J Minim Invasive Gynecol 25:265-276, 2018\nYao R, Hu J, Zhao W, et al.: A review of high-intensity focused ultrasound as a novel and non-invasive interventional radiology technique. J Interv Med 5:127-132, 2022\nYu J, Jiang L, Su X, et al.: Comparison efficacy of ultrasound-guided HIFU for adenomyosis-associated dysmenorrhea with different signal intensity on T2-weighted MR imaging. J Obstet Gynaecol Res 49:1189-1197, 2023\nKeserci B, Duc N M: Magnetic resonance imaging features influencing high-intensity focused ultrasound ablation of adenomyosis with a nonperfused volume ratio of \\(\\ge\\) 90% as a measure of clinical treatment success: retrospective multivariate analysis. Int J Hyperthermia 35:626-636, 2018\nMcCague C, Ramlee S, Reinius M, et al.: Introduction to radiomics for a clinical audience. Clin Radiol 78:83-98, 2023\nLi H, Gao L, Ma H, et al.: Radiomics-based features for prediction of histological subtypes in central lung cancer. Front Oncol 11:658887, 2021\nSabouri M, Hajianfar G, Hosseini Z, et al.: Myocardial Perfusion SPECT Imaging Radiomic Features and Machine Learning Algorithms for Cardiac Contractile Pattern Recognition. J Digit Imaging 36:497-509, 2023\nQi L, Lu X, Shen H, et al.: Automatic Classification of Mass Shape and Margin on Mammography with Artificial Intelligence: Deep CNN Versus Radiomics. J Digit Imaging 1-9, 2023\nZhou H, Dong D, Chen B, et al.: Diagnosis of distant metastasis of lung cancer: based on clinical and radiomic features. Transl Oncol 11:31-36, 2018\nTaleie H, Hajianfar G, Sabouri M, et al.: Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms. J Digit Imaging 1-13, 2023\nBarabino E, Rossi G, Pamparino S, et al.: Exploring response to immunotherapy in non-small cell lung cancer using delta-radiomics. Cancers 14:350, 2022\nSundar S, Sumathy S. Transfer learning approach in deep neural networks for uterine fibroid detection. Int J Computational Science and Engineering 25:52-63, 2022\nDai M, Liu Y, Hu Y, et al.: Combining multiparametric MRI features-based transfer learning and clinical parameters: application of machine learning for the differentiation of uterine sarcomas from atypical leiomyomas. Eur Radiol 32:7988-7997, 2022\nMohammad F, Al Ahmadi S.: Alzheimer’s Disease Prediction Using Deep Feature Extraction and Optimization. Mathematics 11: 3712, 2023\nDey N, Zhang Y D, Rajinikanth V, et al.: Customized VGG19 architecture for pneumonia detection in chest X-rays. Pattern Recognit Lett 143: 67-74, 2021\nGong C, Wang Y, Lv F, et al.: Evaluation of high intensity focused ultrasound treatment for different types of adenomyosis based on magnetic resonance imaging classification. Int J Hyperthermia 39:530-538, 2022\nLi J, Wang W, Liao L, et al.: Analysis of the nonperfused volume ratio of adenomyosis from MRI images based on fewshot learning. Phys Med Biol 66:045019, 2021\nKibria H B, Matin A: The severity prediction of the binary and multi-class cardiovascular disease– A machine learning-based fusion approach. Comput Biol Chem 98:107672, 2022\nHe W, Shi Z, Liu Y, et al.: Feature Fusion Classifier With Dynamic Weights for Abnormality Detection of Amniotic Fluid Cell Chromosome. IEEE Access 11:31755-31766, 2023\nFunding\nThis 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).\nAuthor information\nAuthors and Affiliations\nCorresponding authors\nEthics declarations\nCompeting Interest\nThe authors declare no competing interests.\nAdditional information\nPublisher's Note\nSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\nRights and permissions\nSpringer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.\nAbout this article\nCite this article\nYing, 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. 37, 1579–1590 (2024). https://doi.org/10.1007/s10278-024-01063-4\nReceived:\nRevised:\nAccepted:\nPublished:\nVersion of record:\nIssue date:\nDOI: https://doi.org/10.1007/s10278-024-01063-4","source_license":"CC0","license_restricted":false}