用多器官影像分析提升非增强CT对食管静脉曲张的分级诊断准确率。
Non-Contrast CT Esophageal Varices Grading through Clinical Prior-Enhanced Multi-Organ Analysis
- 融合食管、肝、脾影像特征，结合临床先验知识进行多器官联合分析。
- 在289例独立测试中，严重程度分类AUC达0.894，显著优于单器官方法。
- 适合放射科医生辅助诊断，尤其适用于无法耐受内镜检查的患者。
食管静脉曲张（EV）是门脉高压的重要并发症，约60%肝硬化患者受累，出血风险达30%。传统依赖侵入性内镜诊断，而无对比计算机断层扫描（NCCT）提供了潜在非侵入替代方案，但尚未广泛应用。本文提出多器官协同网络++（MOON++），一种新型多模态框架，通过综合分析NCCT图像中的食管、肝脏和脾脏特征，提升EV评估能力。受临床证据支持——器官体积关系反映肝病严重程度——该模型采用多模态学习整合三者影像特征。基于1,631例患者数据，其中内镜确诊的EV分为四类等级。在239例验证病例与289例独立测试中，相比传统单器官方法，MOON++表现更优：严重程度分级（G3 vs <G3）AUC达0.894（单器官为0.803），中重度区分（≥G2 vs <G2）AUC达0.921（单器官为0.793）。我们还组织了由经验丰富的放射科医生参与的阅片研究，进一步验证其性能。据我们所知，MOON++是首个结合临床先验知识的多器官NCCT分析框架，有望成为可靠的非侵入性诊断工具。
原文摘要 · Abstract (English)
Esophageal varices (EV) represent a critical complication of portal hypertension, affecting approximately 60% of cirrhosis patients with a significant bleeding risk of ~30%. While traditionally diagnosed through invasive endoscopy, non-contrast computed tomography (NCCT) presents a potential non-invasive alternative that has yet to be fully utilized in clinical practice. We present Multi-Organ-COhesion Network++ (MOON++), a novel multimodal framework that enhances EV assessment through comprehensive analysis of NCCT scans. Inspired by clinical evidence correlating organ volumetric relationships with liver disease severity, MOON++ synthesizes imaging characteristics of the esophagus, liver, and spleen through multimodal learning. We evaluated our approach using 1,631 patients, those with endoscopically confirmed EV were classified into four severity grades. Validation in 239 patient cases and independent testing in 289 cases demonstrate superior performance compared to conventional single organ methods, achieving an AUC of 0.894 versus 0.803 for the severe grade EV classification (G3 versus <G3) and 0.921 versus 0.793 for the differentiation of moderate to severe grades (>=G2 versus <G2). We conducted a reader study involving experienced radiologists to further validate the performance of MOON++. To our knowledge, MOON++ represents the first comprehensive multi-organ NCCT analysis framework incorporating clinical knowledge priors for EV assessment, potentially offering a promising non-invasive diagnostic alternative.
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