Academic Journal
Machine learning-driven ultrasound echo feature analysis for accurate classification and area prediction of HIFU-induced lesions: ex vivo study.
| Τίτλος: | Machine learning-driven ultrasound echo feature analysis for accurate classification and area prediction of HIFU-induced lesions: ex vivo study. |
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| Συγγραφείς: | Gong G; State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing 400016, China., Liu T; State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing 400016, China., Xing B; State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing 400016, China., Ma X; State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing 400016, China., Wu M; State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing 400016, China., Li Y; State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing 400016, China., Mpehle RCM; Department of Obstetrics and Gynaecology, Chris Hani Baragwanath Academic Hospital, Johannesburg, South Africa; Department of Obstetrics and Gynaecology, Faculty of Health Science, School of Clinical Medicine, University of the Witwatersrand, Johannesburg, South Africa., Zhou Y; State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, Chongqing 400016, China; Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing 400016, China; National Medical Products Administration (NMPA) Key Laboratory for Quality Evaluation of Ultrasonic Surgical Equipment, 507 Gaoxin Ave., Donghu New Technology Development Zone, Wuhan, Hubei 430075, China; National Engineering Research Center of Ultrasound Medicine, Chongqing 401120, China. Electronic address: yufeng.zhou@cqmu.edu.cn. |
| Πηγή: | Ultrasonics [Ultrasonics] 2026 Oct; Vol. 166, pp. 108112. Date of Electronic Publication: 2026 May 12. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Science Country of Publication: Netherlands NLM ID: 0050452 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1874-9968 (Electronic) Linking ISSN: 0041624X NLM ISO Abbreviation: Ultrasonics Subsets: MEDLINE |
| Imprint Name(s): | Publication: 1995- : Amsterdam : Elsevier Science Original Publication: London. Butterworth Scientific Ltd. |
| Ιατρικοί όροι (MeSH): | High-Intensity Focused Ultrasound Ablation*/methods , Liver*/diagnostic imaging , Liver*/surgery , Liver*/pathology , Boosting Machine Learning Algorithms*, Ultrasonography/methods ; Animals ; Cattle ; Classification Algorithms ; Prediction Algorithms ; Predictive Learning Models ; Random Forest ; Support Vector Machine |
| Περίληψη: | In high-intensity focused ultrasound (HIFU) treatment, precise and reliable prediction of lesion phenotype and area yet remains challenging due to the intricate structures and heterogeneous responses of biological tissues. Conventional monitoring technologies such as B-mode sonography cannot distinguish among the various HIFU-induced lesion patterns or quantify their extent. To address this limitation, we systematically varied acoustic parameters-duty cycle, pulse duration, sonication time, and acoustic power-to create five distinct lesion phenotypes in ex vivo bovine livers. Ultrasonic echo signals from the HIFU focal region were acquired before and after HIFU treatment, from which 21 features were extracted and fed to three machine learning (ML) models: random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM). On the augmented dataset, XGBoost outperformed the other models, achieving an average classification accuracy of 82.6% and an area under the receiver-operating-characteristic curve (AUC) exceeding 0.93 for every lesion category. The same model also demonstrated excellent predictive performance for lesion areas, with R2 > 0.84 for three of the five phenotypes. Subsequent feature-importance analysis revealed that each lesion phenotype exhibits a unique time-frequency signature, providing discriminative information for robust lesion classification. This ex vivo study demonstrated ultrasound echo-feature analysis coupled with ML can simultaneously identify HIFU lesion type and estimate lesion area, thereby providing a methodological basis for future intra-procedural real-time HIFU monitoring and underscoring the potential of echo features for feedback-controlled treatment. (Copyright © 2026 Elsevier B.V. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Yufeng Zhou reports financial support was provided by Chongqing Medical University. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: High-intensity focused ultrasound (HIFU); Lesion area prediction; Lesion type classification; Machine learning (ML); Ultrasonic echo features |
| Entry Date(s): | Date Created: 20260515 Date Completed: 20260612 Latest Revision: 20260623 |
| Update Code: | 20260623 |
| DOI: | 10.1016/j.ultras.2026.108112 |
| PMID: | 42139911 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1874-9968 |
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| DOI: | 10.1016/j.ultras.2026.108112 |