{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T17:16:57Z","timestamp":1789579017790,"version":"build-2803163510"},"reference-count":42,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T00:00:00Z","timestamp":1741651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Thyroid cancer ranks among the most prevalent endocrine neoplasms, with a significant rise in incidence observed in recent decades, particularly in papillary thyroid carcinoma (PTC). This increase is largely attributed to the enhanced detection of subclinical cancers through advanced imaging techniques and fine-needle aspiration biopsies. The present study aims to externally validate a predictive model previously developed by our group, designed to assess the risk of a thyroid nodule being malignant. Methods: By utilizing clinical, analytical, ultrasound, and histological data from patients treated at the Puerto Real University Hospital, this study seeks to evaluate the performance of the predictive model in a distinct dataset and perform a decision curve analysis to ascertain its clinical utility. Results: A total of 455 patients with thyroid nodular pathology were studied. Benign nodular pathology was diagnosed in 357 patients (78.46%), while 98 patients (21.54%) presented with a malignant tumor. The most frequent histological type of malignant tumor was papillary cancer (71.4%), followed by follicular cancer (6.1%). Malignant nodules were predominantly solid (95.9%), hypoechogenic (72.4%), with irregular or microlobed borders (36.7%), and associated with suspicious lymph nodes (24.5%). The decision curve analysis confirmed the model\u2019s accuracy and its potential impact on clinical decision-making. Conclusions: The external validation of our predictive model demonstrates its robustness and generalizability across different populations and clinical settings. The integration of advanced diagnostic tools, such as AI and ML models, improves the accuracy in distinguishing between benign and malignant nodules, thereby optimizing treatment strategies and minimizing invasive procedures. This approach not only facilitates the early detection of cancer but also helps to avoid unnecessary surgeries and biopsies, ultimately reducing patient morbidity and healthcare costs.<\/jats:p>","DOI":"10.3390\/diagnostics15060686","type":"journal-article","created":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T08:59:52Z","timestamp":1741683592000},"page":"686","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["External Validation of a Predictive Model for Thyroid Cancer Risk with Decision Curve Analysis"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7297-6876","authenticated-orcid":false,"given":"Juan Jes\u00fas","family":"Fern\u00e1ndez Alba","sequence":"first","affiliation":[{"name":"Department of Obstetrics and Gynaecology, University Hospital of Puerto Real, 11-510 Cadiz, Spain"},{"name":"Institute of Research and Innovation in Biomedical Sciences of the Province of Cadiz, University Hospital \u2018Puerta del Mar\u2019, University of Cadiz, 11-009 Cadiz, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florentino","family":"Carral","sequence":"additional","affiliation":[{"name":"Department of Endocrinology and Nutrition, University Hospital of Puerto Real, 11-510 Cadiz, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carmen","family":"Ayala Ortega","sequence":"additional","affiliation":[{"name":"Department of Endocrinology and Nutrition, University Hospital of Puerto Real, 11-510 Cadiz, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose Diego","family":"Santotoribio","sequence":"additional","affiliation":[{"name":"Institute of Research and Innovation in Biomedical Sciences of the Province of Cadiz, University Hospital \u2018Puerta del Mar\u2019, University of Cadiz, 11-009 Cadiz, Spain"},{"name":"Laboratory Medicine Department, University Hospital of Puerto Real, 11-510 Cadiz, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4380-5621","authenticated-orcid":false,"given":"Mar\u00eda Castillo","family":"Lara","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynaecology, University Hospital of Puerto Real, 11-510 Cadiz, Spain"},{"name":"Institute of Research and Innovation in Biomedical Sciences of the Province of Cadiz, University Hospital \u2018Puerta del Mar\u2019, University of Cadiz, 11-009 Cadiz, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carmen","family":"Gonz\u00e1lez Mac\u00edas","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynaecology, University Hospital of Puerto Real, 11-510 Cadiz, Spain"},{"name":"Institute of Research and Innovation in Biomedical Sciences of the Province of Cadiz, University Hospital \u2018Puerta del Mar\u2019, University of Cadiz, 11-009 Cadiz, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1038\/nrendo.2016.110","article-title":"The changing incidence of thyroid cancer","volume":"12","author":"Kitahara","year":"2016","journal-title":"Nat. Rev. Endocrinol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1089\/thy.2021.0662","article-title":"Thyroid Cancer Incidence and Mortality Trends in the United States: 2000\u20132018","volume":"32","author":"Megwalu","year":"2022","journal-title":"Thyroid"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"686","DOI":"10.4158\/EP14466.DSCR","article-title":"American Association of Clinical Endocrinologists and American College of Endocrinology Disease State Clinical Review: The Increasing Incidence of Thyroid Cancer","volume":"21","author":"Davies","year":"2015","journal-title":"Endocr. Pract."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1089\/thy.2016.0100","article-title":"Systematic Review of Trends in the Incidence Rates of Thyroid Cancer","volume":"26","author":"Wiltshire","year":"2016","journal-title":"Thyroid"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1089\/thy.2015.0116","article-title":"The Impact of Diagnostic Changes on the Rise in Thyroid Cancer Incidence: A Population-Based Study in Selected High-Resource Countries","volume":"25","author":"Vaccarella","year":"2015","journal-title":"Thyroid"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/S2213-8587(21)00027-9","article-title":"Thyroid cancer incidence trends by histology in 25 countries: A population-based study","volume":"9","author":"Bray","year":"2021","journal-title":"Lancet Diabetes Endocrinol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ruiz, G., Carral, F., Tinoco, R., and Ayala, C. (2016). El Aumento de la incidencia del c\u00e1ncer diferenciado de tiroides no se relaciona con un incremento en la detecci\u00f3n de microcarcinomas incidentales. Rev. Clin. Esp., 216.","DOI":"10.1016\/j.rce.2016.02.002"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gharib, M.H., Afghani, R., Rajaei, S., Roshandel, G., Alijani, A., Karamollahi, Z., Tatari, M., Mohajernoei, S., Hosseini, S.S., and Rezazadeh, S.A. (2024). Clinical Implication of the New AI-TIRADS Classification of Thyroid Nodules; Our Real Clinical Experience. Shiraz E Med. J., 25.","DOI":"10.5812\/semj-147642"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sumayh, S.S., and Aljameel, A. (2022). Proactive Explainable Artificial Neural Network Model for the Early Diagnosis of Thyroid Cancer. De Computis, 10.","DOI":"10.3390\/computation10100183"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, W., Hong, T., Fang, J., Liu, W., Liu, Y., He, C., Li, X., Xu, C., Wang, B., and Chen, Y. (2022). Incorporation of a machine learning pathological diagnosis algorithm into the thyroid ultrasound imaging data improves the diagnosis risk of malignant thyroid nodules. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.968784"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wang, Q., Zhao, J., Yu, H., Wang, F., and Zhang, J. (2023). Automatic ultrasound diagnosis of thyroid nodules: A combination of deep learning and KWAK TI-RADS. Phys. Med. Biol., 68.","DOI":"10.1088\/1361-6560\/acfdf0"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mao, Z., Ding, Y., Wen, L., Zhang, Y., Wu, G., You, Q., Wu, J., Luo, D., Teng, L., and Wang, W. (2023). Combined fine-needle aspiration and selective intraoperative frozen section to optimize prediction of malignant thyroid nodules: A retrospective cohort study of more than 3000 patients. Front. Endocrinol. (Lausanne), 14.","DOI":"10.3389\/fendo.2023.1091200"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.4158\/EP-2020-0004","article-title":"Development and Internal Validation of a Predictive Model for Individual Cancer Risk Assessment for Thyroid Nodules","volume":"26","year":"2020","journal-title":"Endocr. Pract."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1089\/thy.2015.0020","article-title":"2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer","volume":"26","author":"Haugen","year":"2016","journal-title":"Thyroid"},{"key":"#cr-split#-ref_15.1","doi-asserted-by":"crossref","unstructured":"American Thyroid Association (ATA) Guidelines Taskforce on Thyroid Nodules and Differentiated Thyroid Cancer, Cooper, D.S., Doherty, G.M., Haugen, B.R., Kloos, R.T., Lee, S.L., Mandel, S.J., Mazzaferri, E.L., McIver, B., and Pacini, F. (2009). Revised American Thyroid Association management guidelines for patients with thyroid nodules and differentiated thyroid cancer. Thyroid, 19, 1167-1214. Erratum in: Thyroid 2010, 20, 942","DOI":"10.1089\/thy.2009.0110"},{"key":"#cr-split#-ref_15.2","doi-asserted-by":"crossref","unstructured":"Erratum in: Thyroid 2010, 20, 674-675.","DOI":"10.1089\/thy.2009.0110.cxn"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.endonu.2015.10.008","article-title":"Care and economic impact of thyroid ultrasound examination at single visits to endocrinology clinics (the ETIEN 1 Study)","volume":"63","author":"Carral","year":"2016","journal-title":"Endocrinol. Nutr."},{"key":"ref_17","first-page":"130","article-title":"Diagnostic performance of the American Thyroid Association Ultrasound Risk Assessment of Thyroid Nodules in Endocrinology (the ETIEN 3 study)","volume":"67","author":"Carral","year":"2020","journal-title":"Endocrinol. Nutr."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Riley, R.D., Archer, L., Snell, K.I.E., Ensor, J., Dhiman, P., Martin, G.P., Bonnett, L.J., and Collins, G.S. (2024). Evaluation of clinical prediction models (Part 2): How to undertake an external validation study. BMJ, 384.","DOI":"10.1136\/bmj-2023-074820"},{"key":"ref_19","unstructured":"R Core Team (2024). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_20","unstructured":"Mangiafico, S.S. (2025, February 07). rcompanion: Functions to Support Extension Education Program Evaluation, Available online: https:\/\/CRAN.R-project.org\/package=rcompanion."},{"key":"ref_21","unstructured":"De Cock Campo, B. (2023). Towards reliable predictive analytics: A generalized calibration framework. arXiv."},{"key":"ref_22","unstructured":"De Cock, B., Nieboer, D., Van Calster, B., Steyerberg, E.W., and Vergouwe, Y. (2025, February 07). The Calibrationcurves Package: Assessing the Agreement Between Observed Outcomes and Predictions. R Package Version 2.0.3. Available online: https:\/\/cran.r-project.org\/package=CalibrationCurves."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.jclinepi.2015.12.005","article-title":"A Calibration hierarchy for risk models was defined: From utopia to empirical Data","volume":"74","author":"Nieboer","year":"2016","journal-title":"J. Clin. Epidemiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1177\/0272989X06295361","article-title":"Decision curve analysis: A novel method for evaluating prediction models","volume":"26","author":"Vickers","year":"2006","journal-title":"Med. Decis. Making"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1002\/cncy.22643","article-title":"Development and validation of an RNA sequencing-based classifier for medullary thyroid carcinoma on thyroid FNA","volume":"131","author":"Lubin","year":"2022","journal-title":"Cancer Cytopathol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Vrinceanu, D., Dumitru, M., Marinescu, A., Serboiu, C., Musat, G., Radulescu, M., Popa-Cherecheanu, M., Ciornei, C., and Manole, F. (2024). Management of Giant Thyroid Tumors in Patients with Multiple Comorbidities in a Tertiary Head and Neck Surgery Center. Biomedicines, 12.","DOI":"10.3390\/biomedicines12102204"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, F., Han, F., Lu, L., Chen, Y., Guo, Z., and Yao, J. (2024). Meta-analysis of prediction models for predicting lymph node metastasis in thyroid cancer. World J. Surg. Oncol., 22.","DOI":"10.1186\/s12957-024-03566-4"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1002\/hed.23075","article-title":"Nomogram for selecting thyroid nodules for ultrasound-guided fine-needle aspiration biopsy based on a quantification of risk of malignancy","volume":"35","author":"Nixon","year":"2013","journal-title":"Head Neck"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1007\/s12020-015-0785-5","article-title":"A Meta-analysis-derived proposal for a clinical, ultrasonographic, and cytological scoring system to evaluate thyroid nodules: The \u201cCUT\u201d score","volume":"52","author":"Ianni","year":"2016","journal-title":"Endocrine"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"28463","DOI":"10.18632\/oncotarget.4691","article-title":"A New computational model for human thyroid cancer enhances the preoperative diagnostic efficacy","volume":"6","author":"Li","year":"2015","journal-title":"Oncotarget"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1055\/a-0630-5239","article-title":"A Risk Score Model for Evaluation and Management of Patients with Thyroid Nodules","volume":"50","author":"Zhang","year":"2018","journal-title":"Horm. Metab. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1111\/cen.13878","article-title":"Diagnostic accuracy of the McGill thyroid nodule score in paediatric patients","volume":"90","author":"Creo","year":"2019","journal-title":"Clin. Endocrinol. Oxf."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Witczak, J., Taylor, P., Chai, J., Amphlett, B., Soukias, J.M., Das, G., Tennant, B.P., Geen, J., and Okosieme, O.E. (2016). Predicting malignancy in thyroid nodules: Feasibility of a predictive model integrating clinical, biochemical, and ultrasound characteristics. Thyroid Res., 9.","DOI":"10.1186\/s13044-016-0033-y"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1089\/thy.2018.0380","article-title":"Machine Learning-Assisted System for Thyroid Nodule Diagnosis","volume":"29","author":"Zhang","year":"2019","journal-title":"Thyroid"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"440","DOI":"10.19127\/mbsjohs.1282265","article-title":"Machine Learning Approach for Thyroid Cancer Diagnosis Using Clinical Data","volume":"9","year":"2023","journal-title":"Middle Black Sea J. Health Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Gu, J., Xie, R., Zhao, Y., Zhao, Z., Xu, D., Ding, M., Lin, T., Xu, W., Nie, Z., and Miao, E. (2022). A Machine learning-based approach to predicting the malignant and metastasis of thyroid cancer. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.938292"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e250","DOI":"10.1016\/S2589-7500(21)00041-8","article-title":"Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: A multicentre diagnostic study","volume":"3","author":"Peng","year":"2021","journal-title":"Lancet Digit. Health"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Cao, Y., Yang, Y., Chen, Y., Luan, M., Hu, Y., Zhang, L., Zhan, W., and Zhou, W. (2024). Optimizing thyroid AUS nodules malignancy prediction: A comprehensive study of logistic regression and machine learning models. Front. Endocrinol. (Lausanne), 15.","DOI":"10.3389\/fendo.2024.1366687"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, X., Ze, Y., Sang, J., Shi, X., Bi, Y., Shen, S., Zhang, X., and Zhu, D. (2022). Risk factors and diagnostic prediction models for papillary thyroid carcinoma. Front. Endocrinol. (Lausanne), 13.","DOI":"10.3389\/fendo.2022.938008"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Vickers, A.J., van Calster, B., and Steyerberg, E.W. (2019). A simple, step-by-step guide to interpreting decision curve analysis. Diagn. Progn. Res., 3.","DOI":"10.1186\/s41512-019-0064-7"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.ultrasmedbio.2018.09.026","article-title":"Inter-observer Variability in the American College of Radiology Thyroid Imaging Reporting and Data System: In-Depth Analysis and Areas for Improvement","volume":"45","author":"Itani","year":"2019","journal-title":"Ultrasound Med. Biol."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/6\/686\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:50:29Z","timestamp":1760028629000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/6\/686"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,11]]},"references-count":42,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["diagnostics15060686"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15060686","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,11]]}}}