Ultrasound radiomics models improve preoperative diagnosis and reduce unnecessary biopsies in indeterminate thyroid nodules
PurposeCytologically 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 st...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2025.1615304/full |
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| author | Lu Chen Yan Wang Yan Wang Haoyu Jing Haoyu Jing Rui Bao Bin Sun Mingbo Zhang Yukun Luo |
| author_facet | Lu Chen Yan Wang Yan Wang Haoyu Jing Haoyu Jing Rui Bao Bin Sun Mingbo Zhang Yukun Luo |
| author_sort | Lu Chen |
| collection | DOAJ |
| description | PurposeCytologically 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.MethodsWe 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.ResultsThe Radunion model achieved the highest performance, with 90.5% sensitivity, 56.8% specificity, 75.0% positive predictive value, 80.7% negative predictive value, and 76.6% accuracy. The Radsize model minimized biopsies by 21.1%, reducing the rate from 48.9% to 13.8%. These models outperformed the ITS 100 system, Thynet deep learning-based tools (p < 0.05), and junior radiologists.ConclusionUltrasound radiomics models are promising, convenient, and accurate adjunct tools for predicting malignancy, improving junior radiologists’ diagnostic performance, reducing unnecessary biopsies, and enhancing diagnostic precision in clinical practice. |
| format | Article |
| id | doaj-art-66d23dd9fede41fa8e74913a90dc97a0 |
| institution | Matheson Library |
| issn | 1664-2392 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj-art-66d23dd9fede41fa8e74913a90dc97a02025-07-10T04:11:56ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922025-07-011610.3389/fendo.2025.16153041615304Ultrasound radiomics models improve preoperative diagnosis and reduce unnecessary biopsies in indeterminate thyroid nodulesLu Chen0Yan Wang1Yan Wang2Haoyu Jing3Haoyu Jing4Rui Bao5Bin Sun6Mingbo Zhang7Yukun Luo8Department of Ultrasound, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, ChinaDepartment of Ultrasound, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, ChinaGraduate School Medical School of Chinese People's Liberation Army (PLA), Beijing, ChinaDepartment of Ultrasound, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, ChinaGraduate School Medical School of Chinese People's Liberation Army (PLA), Beijing, ChinaDepartment of Ultrasound, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, ChinaDepartment of Ultrasound, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, ChinaDepartment of Ultrasound, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, ChinaDepartment of Ultrasound, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, ChinaPurposeCytologically 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.MethodsWe 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.ResultsThe Radunion model achieved the highest performance, with 90.5% sensitivity, 56.8% specificity, 75.0% positive predictive value, 80.7% negative predictive value, and 76.6% accuracy. The Radsize model minimized biopsies by 21.1%, reducing the rate from 48.9% to 13.8%. These models outperformed the ITS 100 system, Thynet deep learning-based tools (p < 0.05), and junior radiologists.ConclusionUltrasound radiomics models are promising, convenient, and accurate adjunct tools for predicting malignancy, improving junior radiologists’ diagnostic performance, reducing unnecessary biopsies, and enhancing diagnostic precision in clinical practice.https://www.frontiersin.org/articles/10.3389/fendo.2025.1615304/fullindeterminate thyroid nodulesmachine learningradiomics modelultrasound diagnosisfine needle biopsy |
| spellingShingle | Lu Chen Yan Wang Yan Wang Haoyu Jing Haoyu Jing Rui Bao Bin Sun Mingbo Zhang Yukun Luo Ultrasound radiomics models improve preoperative diagnosis and reduce unnecessary biopsies in indeterminate thyroid nodules Frontiers in Endocrinology indeterminate thyroid nodules machine learning radiomics model ultrasound diagnosis fine needle biopsy |
| title | Ultrasound radiomics models improve preoperative diagnosis and reduce unnecessary biopsies in indeterminate thyroid nodules |
| title_full | Ultrasound radiomics models improve preoperative diagnosis and reduce unnecessary biopsies in indeterminate thyroid nodules |
| title_fullStr | Ultrasound radiomics models improve preoperative diagnosis and reduce unnecessary biopsies in indeterminate thyroid nodules |
| title_full_unstemmed | Ultrasound radiomics models improve preoperative diagnosis and reduce unnecessary biopsies in indeterminate thyroid nodules |
| title_short | Ultrasound radiomics models improve preoperative diagnosis and reduce unnecessary biopsies in indeterminate thyroid nodules |
| title_sort | ultrasound radiomics models improve preoperative diagnosis and reduce unnecessary biopsies in indeterminate thyroid nodules |
| topic | indeterminate thyroid nodules machine learning radiomics model ultrasound diagnosis fine needle biopsy |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2025.1615304/full |
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