ArticleScientific reports2019
Comprehensive feature selection for classifying the treatment outcome of high-intensity ultrasound therapy in uterine fibroids.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Comparative Effectiveness and Safety of High-Intensity Focused Ultrasound for Uterine Fibroids: A Systematic Review and Meta-Analysis.Frontiers in oncology · 2021Pooled it
- Volume Alterations in Thalamic Subnuclei in Parkinson's Disease Dementia and Machine Learning-Based Prediction of Diagnosis and Severity.Brain and behavior · 2026Article
- Understanding different aspects of blood supply of uterine fibroids: an overview of ultrasound and magnetic resonance imaging techniques.Insights into imaging · 2025Article
- Ablation of symptomatic uterine fibroids with the Mirabilis system for rapid noninvasive ultrasound-guided high-intensity focused ultrasound (HIFU): a prospective observational clinical study.La Radiologia medica · 2025Observational
- Application of transabdominal ultrasound- and laparoscopy-guided percutaneous microwave ablation for treating uterine fibroids: 24-month follow-up outcomes.Archives of gynecology and obstetrics · 2024Article
- Development and validation of a radiomics model based on T2-weighted imaging for predicting the efficacy of high intensity focused ultrasound ablation in uterine fibroids.Quantitative imaging in medicine and surgery · 2024Article
- Prediction of non-perfusion volume ratio for uterine fibroids treated with ultrasound-guided high-intensity focused ultrasound based on MRI radiomics combined with clinical parameters.Biomedical engineering online · 2023Article
- No incision required for long-lasting symptom relief in a selection of women suffering from uterine fibroids.European radiology · 2023Article
- Automated Uterine Fibroids Detection in Ultrasound Images Using Deep Convolutional Neural Networks.Healthcare (Basel, Switzerland) · 2023Article
- Artificial intelligence-aided method to detect uterine fibroids in ultrasound images: a retrospective study.Scientific reports · 2023Article
- Should Ultrasound-Guided High Frequency Focused Ultrasound Be Considered as an Alternative Non-Surgical Treatment of Uterine Fibroids in Non-Asiatic Countries? An Opinion Paper.Journal of clinical medicine · 2022Article
- Correction to: A feasibility analysis of the ArcBlate MR‑guided high‑intensity focused ultrasound system for the ablation of uterine fibroids.Abdominal radiology (New York) · 2022Article
- A feasibility analysis of the ArcBlate MR-guided high-intensity focused ultrasound system for the ablation of uterine fibroids.Abdominal radiology (New York) · 2021Article
- Magnetic Resonance-Guided High-Intensity Focused Ultrasound Ablation of Uterine Fibroids-Efficiency Assessment with the Use of Dynamic Contrast-Enhanced Magnetic Resonance Imaging and the Potential Role of the Administration of Uterotonic Drugs.Diagnostics (Basel, Switzerland) · 2021Article
- Physician Experience in Technical Success of Achieving NPVR ≥ 80% of High-Intensity Focused Ultrasound Ablation for Uterine Fibroids: A Multicenter Study.Frontiers in medical technology · 2021Article
- Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches.JMIR medical informatics · 2020Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study aim was to utilise multiple feature selection methods in order to select the most important parameters from clinical patient data for high-intensity focused ultrasound (HIFU) treatment outcome classification in uterine fibroids. The study was retrospective using patient data from 66 HIFU treatments with 89 uterine fibroids. A total of 39 features were extracted from the patient data and 14 different filter-based feature selection methods were used to select the most informative features. The selected features were then used in a support vector classification (SVC) model to evaluate the performance of these parameters in predicting HIFU therapy outcome. The therapy outcome was defined as non-perfused volume (NPV) ratio in three classes: <30%, 30-80% or >80%. The ten most highly ranked features in order were: fibroid diameter, subcutaneous fat thickness, fibroid volume, fibroid distance, Funaki type I, fundus location, gravidity, Funaki type III, submucosal fibroid type and urinary symptoms. The maximum F1-micro classification score was 0.63 using the top ten features from Mutual Information Maximisation (MIM) and Joint Mutual Information (JMI) feature selection methods. Classification performance of HIFU therapy outcome prediction in uterine fibroids is highly dependent on the chosen feature set which should be determined prior using different classifiers.
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