用AI从普通CT自动筛查心脏钙化，精准度高且适合大规模体检。
Artificial Intelligence-Based Opportunistic Coronary Calcium Screening in the Veterans Affairs National Healthcare System
- 基于深度学习分析非门控CT，自动量化冠状动脉钙化。
- 在795例配对扫描中，区分0与非0、<100与≥100的准确率达89.4%和87.3%。
- 可识别38.4%低剂量肺部筛查者钙化超标，提示需降脂治疗。
冠状动脉钙化（CAC）是心血管事件的重要预测指标。美国每年进行数百万次胸部CT检查，但多数非心脏相关扫描未常规量化CAC。本研究利用来自98家退伍军人事务医疗中心的影像数据，构建了基于深度学习的AI-CAC算法，可在无对比、非门控的CT上自动量化CAC。该模型在795例一年内有配对门控扫描的患者中表现优异：区分零与非零、小于100与大于等于100的Agatston评分准确率分别为89.4%（F1 0.93）和87.3%（F1 0.89）。非门控AI-CAC能有效预测10年全因死亡率（CAC 0 vs >400组：25.4% vs 60.2%，Cox HR 3.49, p < 0.005），以及首次卒中、心梗或死亡复合终点（33.5% vs 63.8%，Cox HR 3.00, p < 0.005）。在8,052例低剂量肺癌筛查CT中，3,091人（38.4%）的AI-CAC >400；四位心脏病专家审查随机样本发现，527/531（99.2%）患者应接受降脂治疗。本研究为首个跨全国医疗系统、多设备协议、未排除心脏植入器械的非门控CT CAC算法，且以强参照门控扫描进行验证，性能优于以往同类研究。
原文摘要 · Abstract (English)
Coronary artery calcium (CAC) is highly predictive of cardiovascular events. While millions of chest CT scans are performed annually in the United States, CAC is not routinely quantified from scans done for non-cardiac purposes. A deep learning algorithm was developed using 446 expert segmentations to automatically quantify CAC on non-contrast, non-gated CT scans (AI-CAC). Our study differs from prior works as we leverage imaging data across the Veterans Affairs national healthcare system, from 98 medical centers, capturing extensive heterogeneity in imaging protocols, scanners, and patients. AI-CAC performance on non-gated scans was compared against clinical standard ECG-gated CAC scoring. Non-gated AI-CAC differentiated zero vs. non-zero and less than 100 vs. 100 or greater Agatston scores with accuracies of 89.4% (F1 0.93) and 87.3% (F1 0.89), respectively, in 795 patients with paired gated scans within a year of a non-gated CT scan. Non-gated AI-CAC was predictive of 10-year all-cause mortality (CAC 0 vs. >400 group: 25.4% vs. 60.2%, Cox HR 3.49, p < 0.005), and composite first-time stroke, MI, or death (CAC 0 vs. >400 group: 33.5% vs. 63.8%, Cox HR 3.00, p < 0.005). In a screening dataset of 8,052 patients with low-dose lung cancer-screening CTs (LDCT), 3,091/8,052 (38.4%) individuals had AI-CAC >400. Four cardiologists qualitatively reviewed LDCT images from a random sample of >400 AI-CAC patients and verified that 527/531 (99.2%) would benefit from lipid-lowering therapy. To the best of our knowledge, this is the first non-gated CT CAC algorithm developed across a national healthcare system, on multiple imaging protocols, without filtering intra-cardiac hardware, and compared against a strong gated CT reference. We report superior performance relative to previous CAC algorithms evaluated against paired gated scans that included patients with intra-cardiac hardware.
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