Large-scale esophageal cancer screening through noncontrast computed tomography and artificial intelligence.
Authors
Affiliations (32)
Affiliations (32)
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
- DAMO Academy, Alibaba Group, Hangzhou, China.
- Hupan Lab, Hangzhou, China.
- Department of Surgical Oncology, Shantou Central Hospital, Shantou, China.
- Department of Radiology, Shanghai Institution of Pancreatic Disease, Shanghai, China.
- Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, Department of Radiology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Central Hospital, Lishui, China.
- Department of Radiotherapy, Cancer Hospital Affiliated to Xiangya Medical College, Central South University, Hunan Cancer Hospital, Changsha, China.
- Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic.
- Department of radio-oncology, People's Hospital of Chenghai Shantou, Shantou, China.
- DAMO Academy, Alibaba Group, Washington, DC, USA.
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, China.
- Department of Endoscopy Center, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, China.
- Department of Radiation Oncology, Precision Radiation in Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, China.
- Department of Radiological Imaging, Suining Central Hospital, Suining, China.
- Department of Radiotherapy, The Third Affiliated Teaching Hospital of Xinjiang Medical University (Affiliated Cancer Hospital), Urumqi, China.
- Medical imaging center, The Third Affiliated Teaching Hospital of Xinjiang Medical University (Affiliated Cancer Hospital), Urumqi, China.
- Department of Radiation Oncology, The Teaching Hospital of Fujian Medical University, Fujian Provincial Cancer Hospital, Fuzhou, China.
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou, China.
- International Health Management Center, Shanghai Institution of Pancreatic Disease, Shanghai, China.
- School of Artificial Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China.
- National Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
- Department of Cancer Prevention, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China. [email protected].
- Zhejiang Key Laboratory of Imaging and Interventional Medicine, Department of Radiology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Central Hospital, Lishui, China. [email protected].
- Department of oncology, Suining Central Hospital, Suining, China. [email protected].
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China. [email protected].
- Department of Radiology, Shanghai Institution of Pancreatic Disease, Shanghai, China. [email protected].
- Sichuan Provincial Engineering Research Center of Tumor Organoids and Clinical Transformation, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, China. [email protected].
- Hupan Lab, Hangzhou, China. [email protected].
- DAMO Academy, Alibaba Group, Washington, DC, USA. [email protected].
- Department of Radiation Oncology, Precision Radiation in Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, China. [email protected].
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 .