用影像组学方法在无标注数据下精准识别冠状动脉钙化，准确率达84%。
3D CT-Based Coronary Calcium Assessment: A Feature-Driven Machine Learning Framework
- 基于影像组学与伪标签生成训练数据，无需专家标注分割
- 在182例患者中实现84%分类准确率，优于深度学习特征
- 适合临床早期冠心病风险筛查，尤其适用于标注资源匮乏场景
冠状动脉钙化（CAC）评分在冠心病早期检测与风险分层中至关重要。本研究聚焦于临床常用的非增强冠状动脉计算机断层血管造影（CCTA）扫描，针对标注数据有限的挑战，提出一种基于影像组学的机器学习框架，利用伪标签生成训练标签，避免依赖专家分割。同时，探索使用预训练基础模型（CT-FM与RadImageNet）提取图像特征，并与传统分类器结合。比较深度学习特征与影像组学特征的性能表现。评估基于包含182名患者的临床CCTA数据集，将个体分为钙化得分零与非零两类。进一步考察仅在非增强数据上训练与联合增强/非增强数据训练的效果，测试始终仅在非增强扫描上进行。结果表明，尽管缺乏专家标注，影像组学模型显著优于基础模型提取的CNN嵌入特征（准确率达84%，p<0.05）。
原文摘要 · Abstract (English)
Coronary artery calcium (CAC) scoring plays a crucial role in the early detection and risk stratification of coronary artery disease (CAD). In this study, we focus on non-contrast coronary computed tomography angiography (CCTA) scans, which are commonly used for early calcification detection in clinical settings. To address the challenge of limited annotated data, we propose a radiomics-based pipeline that leverages pseudo-labeling to generate training labels, thereby eliminating the need for expert-defined segmentations. Additionally, we explore the use of pretrained foundation models, specifically CT-FM and RadImageNet, to extract image features, which are then used with traditional classifiers. We compare the performance of these deep learning features with that of radiomics features. Evaluation is conducted on a clinical CCTA dataset comprising 182 patients, where individuals are classified into two groups: zero versus non-zero calcium scores. We further investigate the impact of training on non-contrast datasets versus combined contrast and non-contrast datasets, with testing performed only on non contrast scans. Results show that radiomics-based models significantly outperform CNN-derived embeddings from foundation models (achieving 84% accuracy and p<0.05), despite the unavailability of expert annotations.
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