ArticleEuropean thyroid journal2020
Machine Learning Prediction of Radiofrequency Thermal Ablation Efficacy: A New Option to Optimize Thyroid Nodule Selection.
Article in European thyroid journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Early volume reduction slope as a novel predictor of long-term outcomes following microwave ablation of benign thyroid nodules: a machine learning-enhanced prediction model.Journal of ultrasound · 2026Article
- Machine learning-based ultrasound radiomics for prediction of 6-month local thermal ablation response in benign thyroid nodules: a multicenter study.Frontiers in endocrinology · 2026Article
- Predictors of Suboptimal Response After Radiofrequency Ablation of Benign Thyroid Nodules.Journal of clinical medicine · 2025Article
- Role of Computational Modelling in Enhancing Thermal Safety During Cardiac Ablation.Interdisciplinary cardiovascular and thoracic surgery · 2025Review
- Predicting the efficacy of microwave ablation of benign thyroid nodules from ultrasound images using deep convolutional neural networks.BMC medical informatics and decision making · 2025Article
- Ultrasound-based radiomics to predict the volume reduction rate of benign thyroid nodules after microwave ablation.Endocrine · 2025Article
- AI-Powered Clinical Decision Support Systems in Disease Diagnosis, Treatment Planning, and Prognosis: A Systematic Review.Medical journal of the Islamic Republic of Iran · 2025Review
- Optical coherence tomography for the detection of electrothermal ureteral injury in anBiophotonics discovery · 2024Article
- A prognostic model for thermal ablation of benign thyroid nodules based on interpretable machine learning.Frontiers in endocrinology · 2024Article
- Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions.Thyroid : official journal of the American Thyroid Association · 2023Review
- Analysis and prediction of regrowth in benign thyroid nodules undergoing radiofrequency ablation: a retrospective study with a 5-year follow-up.European radiology · 2023Article
- Image-guided thermal ablation in autonomously functioning thyroid nodules. A retrospective multicenter three-year follow-up study from the Italian Minimally Invasive Treatment of the Thyroid (MITT) Group.European radiology · 2022Article
- Computer-Analyzed Ultrasound Predictors of the Treatment Efficacy of Radiofrequency Ablation for Benign Thyroid Nodules.World journal of surgery · 2022Article
- Successful Applications of Food-Assisted and -Simulated Training Model of Thyroid Radiofrequency Ablation.Frontiers in endocrinology · 2022Article
- Determining an energy threshold for optimal volume reduction of benign thyroid nodules treated by radiofrequency ablation.European radiology · 2021Article
- How to Monitor and Manage Nodule Regrowth after Thermal Ablation of Benign Thyroid Nodules.Korean journal of radiology · 2021Article
- A Nomogram to Predict Regrowth After Ultrasound-Guided Radiofrequency Ablation for Benign Thyroid Nodules.Frontiers in endocrinology · 2021Article
- Treating thyroid nodules by radiofrequency: is the delivered energy correlated with the volume reduction rate? A pilot study.Endocrine · 2020Article
- Placing Thermal Ablation for Benign Thyroid Nodules into Context.European thyroid journal · 2020Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRadiofrequency (RF) is a therapeutic modality for reducing the volume of large benign thyroid nodules. If thermal therapies are interpreted as an alternative strategy to surgery, critical issues in their use are represented by the extent of nodule reduction and by the durability of nodule reduction over a long period of time.
objectiveTo assess the ability of machine learning to discriminate nodules with volume reduction rate (VRR) < or ≥50% at 12 months following RF treatment.
methodsA machine learning model was trained with a dataset of 402 cytologically benign thyroid nodules subjected to RF at six Italian Institutions. The model was trained with the following variables: baseline nodule volume, echostructure, macrocalcalcifications, vascularity, and 12-month VRR.
resultsAfter training, the model could distinguish between nodules having VRR <50% from those having VRR ≥50% in 85% of cases (accuracy: 0.85; 95% confidence interval [CI]: 0.80-0.90; sensitivity: 0.70; 95% CI: 0.62-0.75; specificity: 0.99; 95% CI: 0.98-1.0; positive predictive value: 0.95; 95% CI: 0.92-0.98; negative predictive value: 0.95; 95% CI: 0.92-0.98).
conclusionsThis study demonstrates that a machine learning model can reliably identify those nodules that will have VRR < or ≥50% at 12 months after one RF treatment session. Predicting which nodules will be poor or good responders represents valuable data that may help physicians and patients decide on the best treatment option between thermal ablation and surgery or in predicting if more than one session might be necessary to obtain a significant volume reduction.
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