基于非增强 CT 与人工智能的大规模食管癌筛查
Large-scale esophageal cancer screening through noncontrast computed tomography and artificial intelligence
食管癌早期发现困难，因为缺乏准确、无创、可大规模推广的筛查工具。平扫 CT（NC CT，不打造影剂的胸部 CT）普及度高，但食管是易塌陷、易受运动伪影影响的空腔管状结构，早期小病灶难以与正常组织区分。研究者开发了 EAGLE 人工智能模型，用来自 2 个中心 6,813 名患者的数据训练，并在 3 个国家 12 个中心、共 80,612 名患者中验证。机会性筛查（利用已有 CT 图像）的多中心外部测试（8 个中心，11,466 人）特异性 98.5%，对癌症灵敏度 90.0%，对癌前病变 52.5%；低剂量 CT 验证（2 个中心，1,607 人）表现相当；真实世界校准队列（3 个中心，35,402 人）将假阳性降低 72.7%；前瞻性医院验证（17,446 人）阳性预测值 42.2%；真实世界低剂量筛查（10,959 人）特异性 99.94%。作者认为 EAGLE 有潜力成为可扩展的食管癌早期筛查工具。
为什么推荐给您：用常规平扫 CT 加 AI 完成大规模多中心食管癌筛查验证，属新平台技术的重要进展。
不需要生物学背景，多打比方
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摘要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 .