---
title: "EAGLE: AI detection of esophageal cancer on noncontrast CT for large‑scale screening"
id: "nature-1-large-scale-esophageal-cancer-screening-through-noncontrast-computed-tomography"
canonical_url: "https://medichelpline.com/clinical-feed/nature-1-large-scale-esophageal-cancer-screening-through-noncontrast-computed-tomography"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "Nature Medicine"
source_url: "https://www.nature.com/articles/s41591-026-04656-4"
published_at: "2026-09-22T12:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# EAGLE: AI detection of esophageal cancer on noncontrast CT for large‑scale screening
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/nature-1-large-scale-esophageal-cancer-screening-through-noncontrast-computed-tomography
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** Nature Medicine
- **Source URL:** [Original Journal Publication](https://www.nature.com/articles/s41591-026-04656-4)
- **Published At:** 2026-09-22T12:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The study developed the Esophageal AI‑Guided malignant Lesion Evaluation (**EAGLE**) model to detect **esophageal cancer (EC)** and precancerous lesions from chest **noncontrast computed tomography (NC CT)**, addressing a need for accurate, noninvasive, scalable screening tools. - Training used 6,813 patients from two centres; external validation covered 12 centres across three countries with a total of 80,612 patients in opportunistic and population‑based screening contexts. - In opportunistic screening on existing CT scans (eight external centres, n = 11,466), EAGLE achieved **98.5% specificity**, **90.0% sensitivity for cancer**, and **52.5% sensitivity for precancerous lesions**. - Low‑dose CT (LDCT) validation (two centres, n = 1,607) showed comparable performance, indicating potential integration with lung‑cancer LDCT screening programmes. - Calibration in a real‑world cohort (three centres, n = 35,402) reduced false positives by **72.7%** while preserving sensitivity. - Prospective hospital validation (n = 17,446) produced a **positive predictive value (PPV) of 42.2%**, and a real‑world low‑dose screening cohort (n = 10,959) reached **99.94% specificity**. - In paired CT–endoscopy cohorts (two centres, n = 702), at a higher‑sensitivity operating point, sensitivities were **65.0% for precancerous lesions** and **78.4% for stage I EC**. - Exploratory prospective analyses suggest that selectively referring high‑risk individuals identified by EAGLE for endoscopy could improve screening efficiency. - The authors conclude that EAGLE has potential as a scalable early‑detection tool for EC. Trial registration: Chictr.org.cn ChiCTR2300074806. - Source limitations: full methodological details, model architecture, and patient‑level demographic breakdowns were not reported in the provided source excerpt.
## Clinical Analysis & Structured Key Points
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[articles](https://www.nature.com/nm/articles?type=article) 4. article Large-scale esophageal cancer screening through noncontrast computed tomography and artificial intelligence [ Download PDF ](https://www.nature.com/articles/s41591-026-04656-4.pdf) [ Download PDF ](https://www.nature.com/articles/s41591-026-04656-4.pdf) * Article * [Open access](https://www.springernature.com/gp/open-science/about/the-fundamentals-of-open-access-and-open-research) * Published: 22 September 2026 # Large-scale esophageal cancer screening through noncontrast computed tomography and artificial intelligence * [Jian Zhou](https://www.nature.com/articles/s41591-026-04656-4#auth-Jian-Zhou-Aff1) [ORCID: orcid.org/0000-0002-6868-9866](https://orcid.org/0000-0002-6868-9866)[1](https://www.nature.com/articles/s41591-026-04656-4#Aff1) [na1](https://www.nature.com/articles/s41591-026-04656-4#na1), * [Guangyu Guo](https://www.nature.com/articles/s41591-026-04656-4#auth-Guangyu-Guo-Aff2-Aff3) [ORCID: 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[ORCID: orcid.org/0000-0001-8371-5252](https://orcid.org/0000-0001-8371-5252)[3](https://www.nature.com/articles/s41591-026-04656-4#Aff3),[11](https://www.nature.com/articles/s41591-026-04656-4#Aff11) & * … * [Qifeng Wang](https://www.nature.com/articles/s41591-026-04656-4#auth-Qifeng-Wang-Aff14) [ORCID: orcid.org/0000-0001-8544-9007](https://orcid.org/0000-0001-8544-9007)[14](https://www.nature.com/articles/s41591-026-04656-4#Aff14) Show authors [_Nature Medicine_](https://www.nature.com/nm) (2026) [Cite this article](https://www.nature.com/articles/s41591-026-04656-4#citeas) [ Save article ](https://www.nature.com/articles/s41591-026-04656-4/save-research?_csrf=dN2Zah82Hhm8XK6E-_2-dUlEg-BAfvO-) [ View saved research ](https://www.nature.com/saved-research) ## Abstract The absence of accurate, noninvasive, scalable screening tools keeps early esophageal cancer (EC) detection a global health challenge. Although noncontrast computed tomography (NC CT) is widely accessible, the esophagus is a hollow tubular structure prone to collapse and motion artifacts, making small early malignant lesions difficult to distinguish from normal tissue. Here we developed the Esophageal AI-Guided malignant Lesion Evaluation (EAGLE) model to detect precancerous lesions and cancer from chest NC CT, a task historically considered impossible. EAGLE was trained on 6,813 patients from two centers and validated across 12 centers in three countries involving 80,612 patients in opportunistic and population-based screening settings. For opportunistic screening on existing CT scans, multicenter external test cohorts (eight centers, _n_ = 11,466) achieved 98.5% specificity, with 90.0% sensitivity for cancer and 52.5% for precancerous lesions; low-dose CT (LDCT) validation (two centers, _n_ = 1,607) showed comparable performance, supporting EC screening through lung-cancer screening programs. Calibration in a real-world cohort (three centers, _n_ = 35,402) reduced false positives by 72.7% while preserving sensitivity; prospective hospital validation (_n_ = 17,446) achieved a 42.2% PPV, and real-world low-dose screening (_n_ = 10,959) reached 99.94% specificity. EAGLE also detected precancerous lesions—in paired CT–endoscopy cohorts (two centers, _n_ = 702), sensitivities were 65.0% for precancerous lesions and 78.4% for stage I EC at a higher-sensitivity operating point. Exploratory analyses of a prospectively enrolled cohort suggest that referring high-risk individuals for endoscopy could improve screening efficiency. In conclusion, EAGLE has the potential to serve as a scalable tool for early EC screening. Chictr.org.cn identifier: [ChiCTR2300074806](https://www.chictr.org.cn/showprojEN.html?proj=202090). ### Explore related subjects Discover the latest articles and news in related subjects. * [Cancer screening](https://www.nature.com/subjects/cancer-screening) * [Computational biology and bioinformatics](https://www.nature.com/subjects/computational-biology-and-bioinformatics) * [Computed tomography](https://www.nature.com/subjects/computed-tomography) * [Oesophageal cancer](https://www.nature.com/subjects/oesophageal-cancer) ## Main Esophageal cancer (EC) is one of the most common digestive tract cancers worldwide, with estimated 511,000 new cases and 445,000 deaths in 2022 (ref. [1](https://www.nature.com/articles/s41591-026-04656-4#ref-CR1 "Bray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 74, 229–263 \(2024\).")). EC comprises two major histological subtypes, esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC), which differ substantially in their risk factors, anatomical locations and geographic distributions. It remains a critical global health issue, characterized by high mortality rates due to its frequent diagnosis at advanced stages and the absence of formalized screening programs[2](https://www.nature.com/articles/s41591-026-04656-4#ref-CR2 "Lao-Sirieix, P. & Fitzgerald, R. C. Screening for oesophageal cancer. Nat. Rev. Clin. Oncol. 9, 278–287 \(2012\)."),[3](https://www.nature.com/articles/s41591-026-04656-4#ref-CR3 "Yang, H., Wang, F., Hallemeier, C. L., Lerut, T. & Fu, J. Oesophageal cancer. Lancet 404, 1991–2005 \(2024\)."). Although the prognosis of EC has improved in the past decades, survival remains unsatisfactory, as the 5-year overall survival rates are 36.9% in China and 18.5% in the United States[4](https://www.nature.com/articles/s41591-026-04656-4#ref-CR4 "He, S. et al. Cancer profiles in China and comparisons with the USA: a comprehensive analysis in the incidence, mortality, survival, staging, and attribution to risk factors. Sci. China Life Sci. 67, 122–131 \(2024\)."),[5](https://www.nature.com/articles/s41591-026-04656-4#ref-CR5 "An, L. et al. The survival of esophageal cancer by subtype in China with comparison to the United States. Int. J. Cancer 152, 151–161 \(2023\)."). While high-risk population-based endoscopic screening has made a substantial contribution to the early detection and intervention of EC in China[6](https://www.nature.com/articles/s41591-026-04656-4#ref-CR6 "Wei, W.-Q. et al. Long-term follow-up of a community assignment, one-time endoscopic screening study of esophageal cancer in China. J. Clin. Oncol. 33, 1951–1957 \(2015\)."),[7](https://www.nature.com/articles/s41591-026-04656-4#ref-CR7 "Chen, R. et al. Effectiveness of one-time endoscopic screening programme in prevention of upper gastrointestinal cancer in China: a multicentre population-based cohort study. Gut 70, 251–260 \(2021\)."),[8](https://www.nature.com/artic
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