Ultrasound radiomics models improve preoperative diagnosis and reduce unnecessary biopsies in indeterminate thyroid nodules

作者：Lu Chen, Yan Wang, Haoyu Jing, Rui Bao, Bin Sun, Mingbo Zhang, Yukun Luo · 发表于：Frontiers in Endocrinology · 年份：2025 · DOI：10.3389/fendo.2025.1615304 · 被引用次数：5 · 研究领域：Thyroid Cancer Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、AI in cancer detection

Purpose Cytologically indeterminate thyroid nodules constitute 20–30% of fine-needle aspiration samples obtained from suspicious thyroid nodules. Over half of patients with indeterminate thyroid nodules undergo diagnostic surgery; however, 60–80% of excised nodules are benign. While some radiomics studies have built models to enhance the diagnostic efficacy of thyroid nodules, few have focused on indeterminate thyroid nodules with confirmed pathological results. We aimed to develop and evaluate ultrasound radiomics models to improve the diagnosis of indeterminate thyroid nodules and reduce unnecessary surgeries. Methods We retrospectively analyzed ultrasound images of 197 indeterminate thyroid nodules with definitive pathological results. Regions of interest were manually delineated using 3-Dimensional Slicer software, and radiomics features were extracted using Pyradiomics software. Ultrasound radiomics feature selection and dimensionality reduction were performed using univariate analysis and the least absolute shrinkage and selection operator method. Independent training (n=136) and validation (n=61) cohorts were used to develop three radiomics models. Model performance was evaluated using receiver operating characteristic analysis and compared to two existing assisted diagnostic tools and two junior radiologists. Results The Radunion model achieved the highest performance, with 90.5% sensitivity, 56.8% specificity, 75.0% positive predictive value, 80.7% negative predictiv...