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Artificial Intelligence-Enabled Analysis of Radiology Reports: Epidemiology and Outcomes of Incidental Thyroid Findings.

2/5 보강
The Journal of clinical endocrinology and metabolism 2026 Thyroid Cancer Diagnosis and Treatme
Retraction 확인
출처
PubMed DOI OpenAlex 마지막 보강 2026-04-29

PICO 자동 추출 (휴리스틱, conf 2/4)

유사 논문
P · Population 대상 환자/모집단
683 patients (mean age, 56.
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
Most cancers were papillary (88.5%), and larger when detected after ITFs (2 cm-SD 1.4) vs no ITF (1.3 cm-SD 0.8). [CONCLUSIONS] ITFs were common and strongly associated with cascades leading to detection of small, low-risk cancers, highlighting their role in overdiagnosis and the need for standardized reporting and more selective follow-up.
OpenAlex 토픽 · Thyroid Cancer Diagnosis and Treatment Artificial Intelligence in Healthcare and Education Thyroid Disorders and Treatments

Larios F, Borras-Osorio M, Wu Y, Claros AG, Toro-Tobon D, Cabezas E, Loor-Torres R, Chavez MM, Guevara Maldonado K, Andrango LV, Lizarazo Jimenez M, Alzamora IM, Al Zahidy M, Montero M, Proano AC, Jacome CS, Fan JW, Ponce-Ponte OJ, Branda ME, Singh Ospina N, Brito JP

📝 환자 설명용 한 줄

[CONTEXT] Incidental thyroid findings (ITFs) are increasingly detected on imaging performed for non-thyroid indications.

🔬 핵심 임상 통계 (초록에서 자동 추출 — 원문 검증 권장)
  • 95% CI 41.1-49.3
  • 연구 설계 cohort study

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BibTeX ↓ RIS ↓
APA Felipe Larios, Mariana Borras-Osorio, et al. (2026). Artificial Intelligence-Enabled Analysis of Radiology Reports: Epidemiology and Outcomes of Incidental Thyroid Findings.. The Journal of clinical endocrinology and metabolism. https://doi.org/10.1210/clinem/dgag186
MLA Felipe Larios, et al.. "Artificial Intelligence-Enabled Analysis of Radiology Reports: Epidemiology and Outcomes of Incidental Thyroid Findings.." The Journal of clinical endocrinology and metabolism, 2026.
PMID 42037237

Abstract

[CONTEXT] Incidental thyroid findings (ITFs) are increasingly detected on imaging performed for non-thyroid indications. Their prevalence, features, and 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] Retrospective cohort study.

[SETTING] Mayo Clinic sites (Rochester, Arizona, Florida, Mayo Clinic Health System).

[PARTICIPANTS] Adults without prior thyroid disease undergoing thyroid-capturing imaging 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.

[OUTCOMES] ITF prevalence, downstream thyroid ultrasound, biopsy, thyroidectomy, and cancer diagnosis. Logistic regression identified demographic and imaging-related factors.

[RESULTS] Among 115,683 patients (mean age, 56.8 [SD 17.2]; 52.9% women), 9,077 (7.8%) had an ITF (92.9% nodular). ITFs were more likely in women, older adults, higher BMI, and in imaging ordered by specialties different from Emergency 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%. Compared with patients without ITFs, those with ITFs had higher odds of thyroid nodule diagnosis (OR 45, 95%CI 41.1-49.3) biopsy (OR 46.8, 95%CI 39.0-56.2) thyroidectomy (OR 55.8, 95%CI 31.3-99.3) and thyroid cancer diagnosis (OR 61.7, 95%CI 38.6-98.5). Most cancers were papillary (88.5%), and larger when detected after ITFs (2 cm-SD 1.4) vs no ITF (1.3 cm-SD 0.8).

[CONCLUSIONS] ITFs were common and strongly associated with cascades leading to detection of small, low-risk cancers, highlighting their role in overdiagnosis and the need for standardized reporting and more selective follow-up.

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