Artificial Intelligence-Enabled Analysis of Radiology Reports: Epidemiology and Consequences of Incidental Thyroid Findings
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909877964636160 |
|---|---|
| author | Larios, Felipe Borras-Osorio, Mariana Wu, Yuqi Claros, Ana Gabriela Toro-Tobon, David Cabezas, Esteban Loor-Torres, Ricardo Chavez, Maria Mateo Maldonado, Kerly Guevara Andrango, Luis Vilatuna Jimenez, Maria Lizarazo Alzamora, Ivan Mateo Zahidy, Misk Al Montero, Marcelo Proano, Ana Cristina Jacome, Cristian Soto Fan, Jungwei W. Ponce-Ponte, Oscar J. Branda, Megan E. Ospina, Naykky Singh Brito, Juan P. |
| author_facet | Larios, Felipe Borras-Osorio, Mariana Wu, Yuqi Claros, Ana Gabriela Toro-Tobon, David Cabezas, Esteban Loor-Torres, Ricardo Chavez, Maria Mateo Maldonado, Kerly Guevara Andrango, Luis Vilatuna Jimenez, Maria Lizarazo Alzamora, Ivan Mateo Zahidy, Misk Al Montero, Marcelo Proano, Ana Cristina Jacome, Cristian Soto Fan, Jungwei W. Ponce-Ponte, Oscar J. Branda, Megan E. Ospina, Naykky Singh Brito, Juan P. |
| contents | 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 and imaging-related factors. Results Among 115,683 patients (mean age, 56.8 [SD 17.2] years; 52.9% women), 9,077 (7.8%) had an ITF, of which 92.9% were nodules. ITFs were more likely in women, older adults, those with higher BMI, and when imaging was ordered by oncology or internal medicine. Compared with chest CT, ITFs were more likely via neck CT, PET, and nuclear medicine scans. Nodule characteristics were poorly documented, with size reported in 44% and other features in fewer than 15% (e.g. calcifications). Compared with patients without ITFs, those with ITFs had higher odds of thyroid nodule diagnosis, biopsy, thyroidectomy and thyroid cancer diagnosis. Most cancers were papillary, and larger when detected after ITFs vs no ITF. Conclusions ITFs were common and strongly associated with cascades leading to the detection of small, low-risk cancers. These findings underscore the role of ITFs in thyroid cancer overdiagnosis and the need for standardized reporting and more selective follow-up. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26032 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence-Enabled Analysis of Radiology Reports: Epidemiology and Consequences of Incidental Thyroid Findings Larios, Felipe Borras-Osorio, Mariana Wu, Yuqi Claros, Ana Gabriela Toro-Tobon, David Cabezas, Esteban Loor-Torres, Ricardo Chavez, Maria Mateo Maldonado, Kerly Guevara Andrango, Luis Vilatuna Jimenez, Maria Lizarazo Alzamora, Ivan Mateo Zahidy, Misk Al Montero, Marcelo Proano, Ana Cristina Jacome, Cristian Soto Fan, Jungwei W. Ponce-Ponte, Oscar J. Branda, Megan E. Ospina, Naykky Singh Brito, Juan P. Computation and Language Artificial Intelligence 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 and imaging-related factors. Results Among 115,683 patients (mean age, 56.8 [SD 17.2] years; 52.9% women), 9,077 (7.8%) had an ITF, of which 92.9% were nodules. ITFs were more likely in women, older adults, those with higher BMI, and when imaging was ordered by oncology or internal medicine. Compared with chest CT, ITFs were more likely via neck CT, PET, and nuclear medicine scans. Nodule characteristics were poorly documented, with size reported in 44% and other features in fewer than 15% (e.g. calcifications). Compared with patients without ITFs, those with ITFs had higher odds of thyroid nodule diagnosis, biopsy, thyroidectomy and thyroid cancer diagnosis. Most cancers were papillary, and larger when detected after ITFs vs no ITF. Conclusions ITFs were common and strongly associated with cascades leading to the detection of small, low-risk cancers. These findings underscore the role of ITFs in thyroid cancer overdiagnosis and the need for standardized reporting and more selective follow-up. |
| title | Artificial Intelligence-Enabled Analysis of Radiology Reports: Epidemiology and Consequences of Incidental Thyroid Findings |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2510.26032 |