Artificial Intelligence-Enabled Analysis of Radiology Reports: Epidemiology and Consequences of Incidental Thyroid Findings
Felipe Larios, Mariana Borras-Osorio, Yuqi Wu, Ana Gabriela Claros, David Toro-Tobon, Esteban Cabezas, Ricardo Loor-Torres, Maria Mateo Chavez, Kerly Guevara Maldonado, Luis Vilatuna Andrango, Maria Lizarazo Jimenez, Ivan Mateo Alzamora, Misk Al Zahidy, Marcelo Montero

TL;DR
This study uses AI to analyze radiology reports, revealing the prevalence, features, and clinical outcomes of incidental thyroid findings, highlighting their role in overdiagnosis and emphasizing the need for standardized reporting and follow-up.
Contribution
Developed and validated a transformer-based NLP pipeline to identify and analyze incidental thyroid findings in radiology reports across multiple modalities.
Findings
7.8% of patients had incidental thyroid findings
Higher odds of thyroid cancer diagnosis with ITFs
Most detected cancers were papillary and small
Abstract
Importance Incidental thyroid findings (ITFs) are increasingly detected on imaging performed for non-thyroid indications. Their prevalence, features, and clinical consequences remain undefined. Objective To develop, validate, and deploy a natural language processing (NLP) pipeline to identify ITFs in radiology reports and assess their prevalence, features, and clinical outcomes. Design, Setting, and Participants Retrospective cohort of adults without prior thyroid disease undergoing thyroid-capturing imaging at Mayo Clinic sites from July 1, 2017, to September 30, 2023. A transformer-based NLP pipeline identified ITFs and extracted nodule characteristics from image reports from multiple modalities and body regions. Main Outcomes and Measures Prevalence of ITFs, downstream thyroid ultrasound, biopsy, thyroidectomy, and thyroid cancer diagnosis. Logistic regression identified demographic…
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