{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T05:00:13Z","timestamp":1790312413174,"version":"4.1.0"},"reference-count":59,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"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>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Indeterminate thyroid nodules (Bethesda III\/IV) are difficult to manage: ultrasound and fine-needle aspiration (FNA) often yield non-definitive results, so many patients undergo diagnostic lobectomy for lesions that prove benign. The combination of AI-enhanced ultrasound and molecular biomarker platforms provides complementary diagnostic capabilities. However, evidence on their combined application remains limited, derives from a small number of early studies, and has not yet been validated in large or prospective cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A systematic search of PubMed (2012\u20132025) identified studies evaluating the diagnostic performance of conventional ultrasound + FNA, AI-assisted ultrasound, molecular biomarker platforms (Afirma, ThyroSeq, ThyGeNEXT, ThyraMIR), and integrated multimodal approaches combining AI-enhanced ultrasound radiomics with molecular biomarker testing in indeterminate thyroid nodules. The search was limited to English-language publications. After applying inclusion and exclusion criteria, 49 studies were included. Extracted outcomes included sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). Study quality was appraised using QUADAS-2, and model reporting transparency was benchmarked against TRIPOD and STARD-AI guidelines. Because of methodological heterogeneity, no meta-analysis or statistical pooling was performed; summary values are unweighted descriptive means presented with observed ranges.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Conventional ultrasound combined with FNA cytology demonstrated high sensitivity (71\u201399.6%) but variable specificity (14.1\u201392%), reflecting persistent diagnostic uncertainty in Bethesda III\/IV nodules. AI-enhanced ultrasound tended to improve specificity and reduce false-positive findings compared with conventional methods, though performance varied considerably across studies. Molecular biomarker platforms showed generally high diagnostic performance, with a descriptive (unweighted) mean sensitivity of 86.8%, specificity 78.5%, and NPV 91.8%, enabling many patients to avoid diagnostic lobectomy. Only a few studies have evaluated integrated AI + biomarker models; these preliminary results suggest potential additive value but require further prospective validation before conclusions can be drawn.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Molecular biomarker testing and AI-assisted ultrasound each show promise for enhancing diagnostic accuracy in indeterminate thyroid nodules, and early evidence suggests that integrating these modalities could further refine malignancy-risk assessment. However, the current evidence is preliminary and heterogeneous, and the benefit of combined approaches has not yet been confirmed. Large, multicenter prospective trials with standardized protocols and transparent reporting are needed to confirm these findings and to assess real-world cost-effectiveness before routine clinical adoption.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1764495","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T04:28:04Z","timestamp":1790310484000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Integration of AI-driven ultrasound and molecular testing to improve diagnosis of indeterminate thyroid nodules"],"prefix":"10.3389","volume":"17","author":[{"given":"Samantha","family":"Cortes","sequence":"first","affiliation":[{"name":"Florida International University Herbert Wertheim College of Medicine","place":["Miami, FL, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cameron","family":"Hrabak","sequence":"additional","affiliation":[{"name":"Midwestern University College of Health Sciences","place":["Glendale, AZ, United States"]},{"name":"Arizona College of Osteopathic Medicine, Midwestern University","place":["Glendale, AZ, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arman","family":"Danesh","sequence":"additional","affiliation":[{"name":"University of California, Irvine","place":["Irvine, CA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karen","family":"Jiang","sequence":"additional","affiliation":[{"name":"Rocky Vista University","place":["Parker, CO, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adarsh","family":"Mahendrakumar","sequence":"additional","affiliation":[{"name":"Michigan State University College of Osteopathic Medicine","place":["East Lansing, MI, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Precious Ochuwa","family":"Imokhai","sequence":"additional","affiliation":[{"name":"Rocky Vista University Montana College of Osteopathic Medicine","place":["Billings, MT, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hafiah","family":"Eltahir","sequence":"additional","affiliation":[{"name":"North Atlanta Ear, Nose, and Throat and Allergy","place":["Atlanta, GA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.76615","article-title":"Rates and predictors of Malignancy in Bethesda III and IV thyroid nodules: A prospective study","volume":"16","author":"Azzam","year":"2024","journal-title":"Cureus"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1002\/edm2.70076","article-title":"Clinical outcomes of patients with Bethesda III or IV cytology on fine needle aspiration of thyroid nodules\u2014a retrospective study","volume":"8","author":"Khan","year":"2025","journal-title":"Endocrinol Diabetes Metab"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1002\/lio2.847","article-title":"Association of patient characteristics, ultrasound features, and molecular testing with Malignancy risk in Bethesda III\u2013V thyroid nodules","volume":"7","author":"Talmor","year":"2022","journal-title":"Laryngoscope Investig Otolaryngol"},{"key":"B4","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.surg.2018.04.094","article-title":"The association between the ultrasonography TIRADS classification system and surgical pathology among indeterminate thyroid nodules","volume":"165","author":"Sahli","year":"2019","journal-title":"Surgery"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1097\/md.0000000000018320","article-title":"Thyroid imaging reporting and data system combined with Bethesda classification in qualitative thyroid nodule diagnosis","volume":"98","author":"Tan","year":"2019","journal-title":"Med (Baltimore)"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1210\/clinem\/dgad181","article-title":"Bethesda III and IV thyroid nodules managed nonoperatively after molecular testing with Afirma GSC or Thyroseq v3","volume":"108","author":"Kim","year":"2023","journal-title":"J Clin Endocrinol Metab"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1001\/jamasurg.2018.1153","article-title":"Performance of a genomic sequencing classifier for the preoperative diagnosis of cytologically indeterminate thyroid nodules","volume":"153","author":"Patel","year":"2018","journal-title":"JAMA Surg"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1002\/cncy.21624","article-title":"Has Afirma gene expression classifier testing refined the indeterminate thyroid category in cytology","volume":"124","author":"Yang","year":"2016","journal-title":"Cancer Cytopathol"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2022.104137","article-title":"Clinical decision support analysis of a microRNA-based thyroid molecular classifier: a real-world, prospective and multicentre validation study","volume":"82","author":"Santos","year":"2022","journal-title":"eBioMedicine"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.21037\/qims-23-1597","article-title":"Deep-learning radiomics based on ultrasound can objectively evaluate thyroid nodules and assist in improving the diagnostic level of ultrasound physicians","volume":"14","author":"Du","year":"2024","journal-title":"Quant Imaging Med Surg"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-022-09378-y","article-title":"Clinical value of artificial intelligence in thyroid ultrasound: a prospective study from the real world","volume":"33","author":"Li","year":"2023","journal-title":"Eur Radiol"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2023.108114","article-title":"AI diagnosis of Bethesda category IV thyroid nodules","volume":"26","author":"Yao","year":"2023","journal-title":"iScience"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1089\/thy.2024.0410","article-title":"Assessment of the diagnostic performance of a commercially available artificial intelligence algorithm for risk stratification of thyroid nodules on ultrasound","volume":"34","author":"Ashton","year":"2024","journal-title":"Thyroid\u00ae"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-023-10132-1","article-title":"US of thyroid nodules: can AI-assisted diagnostic system compete with fine needle aspiration","volume":"34","author":"Zhou","year":"2023","journal-title":"Eur Radiol"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1089\/thy.2023.0603","article-title":"Clinical evaluation of an artificial intelligence-based decision support system for the diagnosis and American College of Radiology thyroid imaging reporting and data system classification of thyroid nodules","volume":"34","author":"Fern\u00e1ndez Velasco","year":"2024","journal-title":"Thyroid\u00ae"},{"key":"B16","unstructured":"Combining radiomics and molecular biomarkers: a novel economic tool to improve diagnostic ability in papillary thyroid cancer\n          \n          \n            \n              Wang\n              Q\n            \n            \n              Dai\n              L\n            \n            \n              Lin\n              S\n            \n            \n              Zhang\n              S\n            \n            \n              Wen\n              J\n            \n            \n              Chen\n              E\n            \n          \n          Front Endocrinol\n          \n          2024"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1210\/clinem\/dgaf399","article-title":"Integrating artificial intelligence in thyroid nodule management: clinical outcomes and cost-effectiveness analysis","author":"Bodoque-Cubas","year":"2025","journal-title":"J Clin Endocrinol Metab"},{"key":"B18","doi-asserted-by":"publisher","first-page":"6906","DOI":"10.1038\/s41598-017-07353-y","article-title":"Cytologically indeterminate thyroid nodules: increased diagnostic performance with combination of US TI-RADS and a new scoring system","volume":"7","author":"He","year":"2017","journal-title":"Sci Rep"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1002\/jum.16195","article-title":"Clinical value of artificial intelligence\u2010based computer\u2010aided diagnosis system versus contrast\u2010enhanced ultrasound for differentiation of benign from Malignant thyroid nodules in different backgrounds","volume":"42","author":"Liu","year":"2023","journal-title":"J Ultrasound Med"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2025.1546983","article-title":"Intelligent diagnosis of thyroid nodules with AI ultrasound assistance and cytology classification","volume":"16","author":"Cai","year":"2025","journal-title":"Front Endocrinol"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1530\/ec-23-0135","article-title":"Molecular testing raises thyroid nodule fine needle aspiration diagnostic value","volume":"12","author":"Han","year":"2023","journal-title":"Endocr Connect"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1210\/clinem\/dgae112","article-title":"Outcomes of cytologically indeterminate thyroid nodules managed with genomic sequencing classifier","volume":"109","author":"Ahmadi","year":"2024","journal-title":"J Clin Endocrinol Metab"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoncol.2018.4616","article-title":"Performance of a multigene genomic classifier in thyroid nodules with indeterminate cytology: a prospective blinded multicenter study","volume":"5","author":"Steward","year":"2019","journal-title":"JAMA Oncol"},{"key":"B24","doi-asserted-by":"publisher","first-page":"1954964","DOI":"10.21037\/gs-24-310","article-title":"Combined model integrating clinical, radiomics, BRAFV600E and ultrasound for differentiating between benign and Malignant indeterminate cytology (Bethesda III) thyroid nodules: a bi-center retrospective study","volume":"13","author":"Zhong","year":"2024","journal-title":"Gland Surg"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1002\/1878-0261.13205","article-title":"Risk stratification of indeterminate thyroid nodules by novel multigene testing: a study of Asians with a high risk of Malignancy","volume":"16","author":"Hu","year":"2022","journal-title":"Mol Oncol"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1002\/cncy.22112","article-title":"Clinical validation of the ThyroSeq v3 genomic classifier in thyroid nodules with indeterminate FNA cytology","volume":"127","author":"Nikiforov","year":"2019","journal-title":"Cancer Cytopathol"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1002\/dc.25311","article-title":"ThyroSeq overview on indeterminate thyroid nodules: an institutional experience","volume":"52","author":"Sirotnikov","year":"2024","journal-title":"Diagn Cytopathol"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1002\/hed.27429","article-title":"European experience with the Afirma gene expression classifier for indeterminate thyroid nodules: a clinical utility study in the Netherlands","volume":"45","author":"Lon\u010dar","year":"2023","journal-title":"Head Neck"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1210\/clinem\/dgab304","article-title":"Afirma genomic sequencing classifier and Xpression Atlas molecular findings in consecutive Bethesda III-VI thyroid nodules","volume":"106","author":"Hu","year":"2021","journal-title":"J Clin Endocrinol Metab"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1002\/cncy.22542","article-title":"Combining molecular testing and the Bethesda category III:VI ratio for thyroid fine\u2010needle aspirates: a quality\u2010assurance metric for evaluating diagnostic performance in a cytopathology laboratory","volume":"130","author":"Gokozan","year":"2022","journal-title":"Cancer Cytopathol"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1002\/cncy.21993","article-title":"Molecular testing for indeterminate thyroid nodules: performance of the Afirma gene expression classifier and ThyroSeq panel","volume":"126","author":"Jug","year":"2018","journal-title":"Cancer Cytopathol"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.3390\/jcm13061759","article-title":"Current state of molecular cytology in thyroid nodules: platforms and their diagnostic and theranostic utility","volume":"13","author":"Hannoush","year":"2024","journal-title":"J Clin Med"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1002\/cncy.21749","article-title":"Impact of Afirma gene expression classifier on cytopathology diagnosis and rate of thyroidectomy","volume":"124","author":"Sacks","year":"2016","journal-title":"Cancer Cytopathol"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1200\/jco.22.00232","article-title":"High diagnostic accuracy of epigenetic imprinting biomarkers in thyroid nodules","volume":"41","author":"Xu","year":"2023","journal-title":"J Clin Oncol"},{"key":"B35","doi-asserted-by":"publisher","first-page":"108829","DOI":"10.1016\/j.surg.2024.07.074","article-title":"Avoidable biopsies? Validating artificial intelligence\u2013based decision support software in indeterminate thyroid nodules","volume":"177","author":"Carnabatu","year":"2025","journal-title":"Surgery"},{"key":"B36","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1001\/jamaoto.2019.3073","article-title":"Machine learning by ultrasonography for genetic risk stratification of thyroid nodules","volume":"146","author":"Daniels","year":"2020","journal-title":"JAMA Otolaryngol Neck Surg"},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.1111\/cen.14612","article-title":"Risk stratification of indeterminate thyroid nodules using ultrasound and machine learning algorithms","volume":"96","author":"Gild","year":"2022","journal-title":"Clin Endocrinol (Oxf)"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.1530\/etj-21-0023","article-title":"Reliability of a computer-aided system in the evaluation of indeterminate ultrasound images of thyroid nodules","volume":"11","author":"Reverter","year":"2022","journal-title":"Eur Thyroid J"},{"key":"B39","doi-asserted-by":"publisher","first-page":"8763","DOI":"10.1038\/s41598-025-93226-8","article-title":"ELTIRADS framework for thyroid nodule classification integrating elastography, TIRADS, and radiomics with interpretable machine learning","volume":"15","author":"Barzegar-Golmoghani","year":"2025","journal-title":"Sci Rep"},{"key":"B40","doi-asserted-by":"publisher","first-page":"1615304","DOI":"10.1016\/j.ultrasmedbio.2025.11.366","article-title":"Ultrasound radiomics models improve preoperative diagnosis and reduce unnecessary biopsies in indeterminate thyroid nodules","volume":"16","author":"Chen","year":"2025","journal-title":"Front Endocrinol"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-07670-3","article-title":"Implications of US radiomics signature for predicting Malignancy in thyroid nodules with indeterminate cytology","volume":"31","author":"Yoon","year":"2021","journal-title":"Eur Radiol"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.611436","article-title":"A computer-aided diagnosis system and thyroid imaging reporting and data system for dual validation of ultrasound-guided fine-needle aspiration of indeterminate thyroid nodules","volume":"11","author":"Liang","year":"2021","journal-title":"Front Oncol"},{"key":"B43","doi-asserted-by":"publisher","DOI":"10.1093\/ejendo\/lvad024","article-title":"Application of machine learning methods to guide patient management by predicting the risk of Malignancy of Bethesda III-V thyroid nodules","volume":"188","author":"D\u2019Andr\u00e9a","year":"2023","journal-title":"Eur J Endocrinol"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.1007\/s11604-025-01809-2","article-title":"Evaluation of AI diagnostic systems for breast ultrasound: comparative analysis with radiologists and the effect of AI assistance","volume":"43","author":"Tsuyuzaki","year":"2025","journal-title":"Jpn J Radiol"},{"key":"B45","doi-asserted-by":"publisher","DOI":"10.1148\/rycan.2020209034","article-title":"Comparative performance of artificial intelligence algorithms for screening mammography","volume":"2","author":"Taya","year":"2020","journal-title":"Radiol Imaging Cancer"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-024-10661-3","article-title":"Diagnostic capabilities of artificial intelligence as an additional reader in a breast cancer screening program","volume":"34","author":"Seker","year":"2024","journal-title":"Eur Radiol"},{"key":"B47","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.lungcan.2022.12.002","article-title":"Diagnostic test accuracy of artificial intelligence-based imaging for lung cancer screening: a systematic review and meta-analysis","volume":"176","author":"Thong","year":"2023","journal-title":"Lung Cancer"},{"key":"B48","doi-asserted-by":"publisher","DOI":"10.1097\/rti.0000000000000613","article-title":"Diagnostic accuracy and performance of artificial intelligence in detecting lung nodules in patients with complex lung disease: a noninferiority study","volume":"37","author":"Abadia","year":"2022","journal-title":"J Thorac Imaging"},{"key":"B49","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-023-10181-6","article-title":"Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review","volume":"34","author":"Lokaj","year":"2023","journal-title":"Eur Radiol"},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.1089\/thy.2023.0132","article-title":"Artificial intelligence in thyroidology: a narrative review of the current applications, associated challenges, and future directions","volume":"33","author":"Toro-Tobon","year":"2023","journal-title":"Thyroid\u00ae"},{"key":"B51","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.22.27430","article-title":"Artificial intelligence (AI) tools for thyroid nodules on ultrasound, from the AJR special series on AI applications","volume":"219","author":"Wildman-Tobriner","year":"2022","journal-title":"Am J Roentgenol"},{"key":"B52","doi-asserted-by":"publisher","DOI":"10.1002\/cam4.3935","article-title":"Artificial intelligence in oncology: Path to implementation","volume":"10","author":"Chua","year":"2021","journal-title":"Cancer Med"},{"key":"B53","doi-asserted-by":"publisher","DOI":"10.1093\/jbi\/wbac065","article-title":"Artificial intelligence in clinical practice: Implementation considerations and barriers","volume":"4","author":"Bahl","year":"2022","journal-title":"J Breast Imaging"},{"key":"B54","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2025.1536039","article-title":"Research progress on artificial intelligence technology-assisted diagnosis of thyroid diseases","volume":"15","author":"Yang","year":"2025","journal-title":"Front Oncol"},{"key":"B55","doi-asserted-by":"publisher","DOI":"10.3390\/biomedicines13040951","article-title":"Artificial intelligence advancements in oncology: A review of current trends and future directions","volume":"13","author":"Huhulea","year":"2025","journal-title":"Biomedicines"},{"key":"B56","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecl.2021.12.002","article-title":"The future of thyroid nodule risk stratification","volume":"51","author":"Burgos","year":"2022","journal-title":"Endocrinol Metab Clin North Am"},{"key":"B57","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-07640-9","article-title":"Deep learning in breast radiology: Current progress and future directions","volume":"31","author":"Ou","year":"2021","journal-title":"Eur Radiol"},{"key":"B58","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1186\/s12943-025-02369-9","article-title":"Current AI technologies in cancer diagnostics and treatment","volume":"24","author":"Tiwari","year":"2025","journal-title":"Mol Cancer"},{"key":"B59","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1186\/s13045-023-01514-5","article-title":"Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment","volume":"16","author":"Zhang","year":"2023","journal-title":"J Hematol OncolJ Hematol Oncol"}],"container-title":["Frontiers in Endocrinology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2026.1764495\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T04:28:06Z","timestamp":1790310486000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2026.1764495\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,25]]},"references-count":59,"alternative-id":["10.3389\/fendo.2026.1764495"],"URL":"https:\/\/doi.org\/10.3389\/fendo.2026.1764495","relation":{},"ISSN":["1664-2392"],"issn-type":[{"value":"1664-2392","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,25]]},"article-number":"1764495"}}