Machine learning-based ultrasound radiomics for prediction of 6-month local thermal ablation response in benign thyroid nodules: a multicenter study
Rattachement africain : cn, us. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Objective: This study aimed to develop a machine learning (ML)-based ultrasound (US) radiomics model for prediction of 6-month local treatment response (LTR) ofthermal ablation (TA) for benign thyroid nodules (BTNs). Methods: Between January 2018 and July 2021, a total of 388 patients who underwent US-guided TA in three centers were included. US radiomics features were extracted from preoperative grayscale US images and data dimensionality reduced using principal component analysis and least absolute shrinkage and selection operator. Then, support vector machine (SVM), logistic regression, a decision tree, K-nearest neighbors, and random forest were applied to selected key US radiomics features for distinguishing a volume reduction ratio (VRR) ≥50% or <50%. Factors affecting 6-month LTR were assessed using multivariate logistic regression to construct a clinical model. Receiver operating characteristic curves were plotted to compare the predictive performance between radiomics-based ML and clinical models. Results: At 6 months post-ablation, patients with VRR ≥ 50% were 75.8% (292/372) in the training and internal test cohorts and 59.4% (19/32) in the external test cohort. Finally, 10 US radiomics features were selected for analysis. Solidity was the only independent clinical predictor associated with VRR<50%. Among the five algorithms, the SVM model achieved the optimal predictive efficacy, with an area under the curve (AUC) = 0.81 in the internal test set. The AUC of the SVM-based US radiomics model was significantly higher than that of the clinical model in both internal (0.81 vs. 0.63, P< 0.05) and external test cohorts (0.77 vs. 0.54, P< 0.05). Conclusion: The SVM-based US radiomicsmodel yielded a satisfactory performance for predicting the 6-month LTR, outperforming the clinical model, and facilitating decision-making in favor of TA.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Machine learning-based ultrasound radiomics for prediction of 6-month local thermal ablation response in benign thyroid nodules: a multicenter study
- Date Crossref
- 18/08/2026
- Éditeur
- Frontiers Media SA
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Shanghai Tenth People's Hospital pays non établi dans la noticeÉtablissement de santé
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East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing pays non établi dans la noticeUniversité ou école supérieure
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American Institute of Ultrasound in Medicine pays non établi dans la noticeInstitution
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University of Electronic Science and Technology of China Department of Ultrasound pays non établi dans la noticeUniversité ou école supérieure
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School of Medicine Department of Medical Ultrasound pays non établi dans la noticeUniversité ou école supérieure
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Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment pays non établi dans la noticeStructure de recherche
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Fudan University Department of Ultrasound pays non établi dans la noticeUniversité ou école supérieure
Shanghai Tenth People's Hospital, Shanghai Key Laboratory of Multidimensional Information Processing — East China Normal University et American Institute of Ultrasound in Medicine, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.