{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T15:30:11Z","timestamp":1788449411687,"version":"build-2803163510"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T00:00:00Z","timestamp":1753056000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>The aim of this study was to establish and validate a radiomics model combining the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS) and clinical features and to build a nomogram that could be utilized to enhance the diagnostic performance of malignant thyroid nodules.<\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p>From January 2019 to September 2022, 329 thyroid nodules from 323 patients who had been referred for surgery and had pathological evidence of them were gathered retrospectively and randomly allocated to training and test cohorts (8:2 ratio). A total of 107 radiomics features were extracted from the US images, and the radiomics score (Rad-score) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Different models were created using logistic regression, including the clinic-ACR score (Clin+ACR), clinic-Rad score (Clin+Rad), ACR score-Rad score (ACR+Rad), and combined clinic-ACR score-Rad score (Clin+ACR+Rad). The diagnostic performance of different models was calculated and compared using the area under the receiver operating curve (AUC) and the corresponding sensitivity and specificity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Eight radiomics features were independent signatures for predicting malignant TNs, with malignant TNs having higher Rad-scores in both cohorts (<jats:italic>P<\/jats:italic> &amp;lt; 0.05). The Clin+ACR+Rad model showed excellent diagnostic prediction ability in both the training (AUC = 0.958) and test datasets (AUC = 0.937), significantly outperforming other models including Rad-score (AUC = 0.890, 0.856), Clin+Rad (AUC = 0.895, 0.859), ACR+Rad (AUC = 0.943, 0.934), and Clin+ACR (AUC = 0.784, 0.785) (all <jats:italic>P<\/jats:italic> &amp;lt; 0.05). The calibration curve demonstrated that the mean absolute error in the training group was just 0.020 and in the test cohort was 0.033. To evaluate the clinical utility of the nomogram in reducing unnecessary biopsies, we further analyzed the performance of our integrated model (Clin+ACR+Rad) compared to the traditional ACR TI-RADS system at different probability thresholds. At the statistically optimal threshold of 0.386, the unnecessary biopsy rate decreased from 46.97% to 22.05% in the training cohort and from 45.83% to 21.05% in the test cohort.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The current study offers preliminary support that the model of combined clinic-ACR score-radiomics score can be helpful for predicting malignancy in thyroid nodules by looking at a retrospective cohort of surgically treated thyroid nodules. The Clin-ACR-Rad nomogram may be a more practical instrument and more accurate prediction model for malignant thyroid nodules.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fendo.2025.1486920","type":"journal-article","created":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T04:10:30Z","timestamp":1753071030000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Building radiomics models based on ACR TI-RADS combining clinical features for discriminating benign and malignant thyroid nodules"],"prefix":"10.3389","volume":"16","author":[{"given":"Xingxing","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lili","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajia","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,7,21]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMcp031436","article-title":"Clinical practice. The thyroid nodule","volume":"351","author":"Heged\u00fcs","year":"2004","journal-title":"N Engl J Med"},{"key":"B2","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1111\/j.1365-2362.2009.02162.x","article-title":"Very high prevalence of thyroid nodules detected by high frequency (13 MHz) ultrasound examination","volume":"39","author":"Guth","year":"2009","journal-title":"Eur J Clin Invest"},{"key":"B3","doi-asserted-by":"publisher","first-page":"l6670","DOI":"10.1136\/bmj.l6670","article-title":"Thyroid nodules: diagnostic evaluation based on thyroid cancer risk assessment","volume":"368","author":"Singh Ospina","year":"2020","journal-title":"BMJ"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.14366\/usg.17066","article-title":"Reliability of core needle biopsy as a second-line procedure in thyroid nodules with an indeterminate fine-needle aspiration report: a systematic review and meta-analysis","volume":"37","author":"Trimboli","year":"2018","journal-title":"Ultrasonography"},{"key":"B5","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1148\/radiol.2017171240","article-title":"Thyroid imaging reporting and data system (TI-RADS): A user\u2019s guide","volume":"287","author":"Tessler","year":"2018","journal-title":"Radiology"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2017.01.046","article-title":"ACR thyroid imaging, reporting and data system (TI-RADS): white paper of the ACR TI-RADS committee","volume":"14","author":"Tessler","year":"2017","journal-title":"J Am Coll Radiol"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018172572","article-title":"Reduction in thyroid nodule biopsies and improved accuracy with american college of radiology thyroid imaging reporting and data system","volume":"287","author":"Hoang","year":"2018","journal-title":"Radiology"},{"key":"B8","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1148\/radiol.2018171074","article-title":"US fine-needle aspiration biopsy for thyroid Malignancy: diagnostic performance of seven society guidelines applied to 2000 thyroid nodules","volume":"287","author":"Ha","year":"2018","journal-title":"Radiology"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2019182128","article-title":"Using artificial intelligence to revise ACR TI-RADS risk stratification of thyroid nodules: diagnostic accuracy and utility","volume":"292","author":"Wildman-Tobriner","year":"2019","journal-title":"Radiology"},{"key":"B10","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1186\/s13104-019-4235-y","article-title":"Diagnostic accuracy of ultrasound characteristics in the identification of Malignant thyroid nodules","volume":"12","author":"Wettasinghe","year":"2019","journal-title":"BMC Res Notes"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: images are more than pictures, they are data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: extracting more information from medical images using advanced feature analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur J Cancer"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2019.12.015","article-title":"Prediction of benign and Malignant solid renal masses: machine learning-based CT texture analysis","volume":"27","author":"Erdim","year":"2020","journal-title":"Acad Radiol"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.21037\/tlcr.2019.12.19","article-title":"Differentiation of non-small cell lung cancer and histoplasmosis pulmonary nodules: insights from radiomics model performance compared with clinician observers","volume":"8","author":"Uthoff","year":"2019","journal-title":"Transl Lung Cancer Res"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1089\/thy.2020.0305","article-title":"A comparative analysis of two machine learning-based diagnostic patterns with thyroid imaging reporting and data system for thyroid nodules: diagnostic performance and unnecessary biopsy rate","volume":"31","author":"Zhao","year":"2021","journal-title":"Thyroid"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2024.07.053","article-title":"Radiomics combined with ACR TI-RADS for thyroid nodules: diagnostic performance, unnecessary biopsy rate, and nomogram construction","volume":"31","author":"Zhang","year":"2024","journal-title":"Acad Radiol"},{"key":"B17","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1186\/s13244-023-01415-8","article-title":"CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII","volume":"14","author":"Kocak","year":"2023","journal-title":"Insights into Imaging"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2015.07.011","article-title":"Thyroid ultrasound reporting lexicon: white paper of the ACR thyroid imaging, reporting and data system (TIRADS) committee","volume":"12","author":"Grant","year":"2015","journal-title":"J Am Coll Radiol"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1055\/s-0034-1366780","article-title":"Konventioneller Ultraschall, Sonoelastografie und Acoustic Radiation Force Impulse Imaging zur Pr\u00e4diktion von Malignit\u00e4t in Schilddr\u00fcsenknoten [Conventional US, US elasticity imaging and acoustic radiation force impulse imaging for prediction of Malignancy in thyroid nodules","volume":"52","author":"G\u00f6tzberger","year":"2014","journal-title":"Z Gastroenterol"},{"key":"B20","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/1758-3284-3-17","article-title":"Role of ultrasound, clinical and scintigraphyc parameters to predict Malignancy in thyroid nodule","volume":"3","author":"Maia","year":"2011","journal-title":"Head Neck Oncol"},{"key":"B21","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.cmpb.2017.06.005","article-title":"Ultrasound-based differentiation of Malignant and benign thyroid Nodules: An extreme learning machine approach","volume":"147","author":"Xia","year":"2017","journal-title":"Comput Methods Programs Biomed"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1089\/thy.2017.0525","article-title":"Predicting Malignancy in thyroid nodules: radiomics Score versus 2017 american college of radiology thyroid imaging, reporting and data system","volume":"28","author":"Liang","year":"2018","journal-title":"Thyroid"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-07365-9","article-title":"Combining radiomics with ultrasound-based risk stratification systems for thyroid nodules: an approach for improving performance","volume":"31","author":"Park","year":"2021","journal-title":"Eur Radiol"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2024.02.007","article-title":"A comparative study of two radiomics-based blood flow modes with thyroid imaging reporting and data system in predicting Malignancy of thyroid nodules and reducing unnecessary fine-needle aspiration rate","volume":"31","author":"Ren","year":"2024","journal-title":"Acad Radiol"},{"key":"B25","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s40644-024-00661-3","article-title":"Dual-modal radiomics nomogram based on contrast-enhanced ultrasound to improve differential diagnostic accuracy and reduce unnecessary biopsy rate in ACR TI-RADS 4\u20135 thyroid nodules","volume":"24","author":"Ren","year":"2024","journal-title":"Cancer Imaging"}],"container-title":["Frontiers in Endocrinology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2025.1486920\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T04:10:31Z","timestamp":1753071031000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2025.1486920\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,21]]},"references-count":25,"alternative-id":["10.3389\/fendo.2025.1486920"],"URL":"https:\/\/doi.org\/10.3389\/fendo.2025.1486920","relation":{},"ISSN":["1664-2392"],"issn-type":[{"value":"1664-2392","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,21]]},"article-number":"1486920"}}