Suppr超能文献

冠状动脉 CT 血管造影中的深度学习钙评分。

Calcium Scoring at Coronary CT Angiography Using Deep Learning.

机构信息

From the Department of Radiology, Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing, China (D.M., W.C., H.Y., J.L., K.Y., H.L., Z.Q., B.Z.); Keya Medical, Shenzhen, China (J.B., H.Y.Y., J.Z., Y.Y.); Medical School of Nanjing University, Nanjing, China (K.H.); National Institutes of Healthcare Data Science at Nanjing University, Nanjing, China (K.H.); University of South Carolina School of Medicine-Columbia, Columbia, SC (H.W.M.); Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC (U.J.S.); Institute of Brain Science, Nanjing University, Nanjing 210008, China (B.Z.).

出版信息

Radiology. 2022 Feb;302(2):309-316. doi: 10.1148/radiol.2021211483. Epub 2021 Nov 23.

Abstract

Background Separate noncontrast CT to quantify the coronary artery calcium (CAC) score often precedes coronary CT angiography (CTA). Quantifying CAC scores directly at CTA would eliminate the additional radiation produced at CT but remains challenging. Purpose To quantify CAC scores automatically from a single CTA scan. Materials and Methods In this retrospective study, a deep learning method to quantify CAC scores automatically from a single CTA scan was developed on training and validation sets of 292 patients and 73 patients collected from March 2019 to July 2020. Virtual noncontrast scans obtained with a spectral CT scanner were used to develop the algorithm to alleviate tedious manual annotation of calcium regions. The proposed method was validated on an independent test set of 240 CTA scans collected from three different CT scanners from August 2020 to November 2020 using the Pearson correlation coefficient, the coefficient of determination, or , and the Bland-Altman plot against the semiautomatic Agatston score at noncontrast CT. The cardiovascular risk categorization performance was evaluated using weighted κ based on the Agatston score (CAC score risk categories: 0-10, 11-100, 101-400, and >400). Results Two hundred forty patients (mean age, 60 years ± 11 [standard deviation]; 146 men) were evaluated. The positive correlation between the automatic deep learning CTA and semiautomatic noncontrast CT CAC score was excellent (Pearson correlation = 0.96; = 0.92). The risk categorization agreement based on deep learning CTA and noncontrast CT CAC scores was excellent (weighted κ = 0.94 [95% CI: 0.91, 0.97]), with 223 of 240 scans (93%) categorized correctly. All patients who were miscategorized were in the direct neighboring risk groups. The proposed method's differences from the noncontrast CT CAC score were not statistically significant with regard to scanner ( = .15), sex ( = .051), and section thickness ( = .67). Conclusion A deep learning automatic calcium scoring method accurately quantified coronary artery calcium from CT angiography images and categorized risk. © RSNA, 2021 See also the editorial by Goldfarb and Cao et al in this issue.

摘要

背景 单独的非对比 CT 用于定量冠状动脉钙(CAC)评分,通常先于冠状动脉 CT 血管造影(CTA)进行。在 CTA 上直接定量 CAC 评分可以消除 CT 产生的额外辐射,但仍然具有挑战性。目的 从单次 CTA 扫描中自动定量 CAC 评分。材料与方法 在这项回顾性研究中,我们开发了一种深度学习方法,用于从 292 例患者和 73 例患者的训练集和验证集的单张 CTA 扫描中自动定量 CAC 评分,这些数据是 2019 年 3 月至 2020 年 7 月期间使用能谱 CT 扫描仪采集的。使用虚拟非对比扫描来开发算法,以减轻对钙区域的繁琐手动注释。该方法在 2020 年 8 月至 11 月期间从 3 台不同的 CT 扫描仪采集的 240 例 CTA 扫描的独立测试集中进行了验证,使用 Pearson 相关系数、决定系数 或 ,以及 Bland-Altman 图与非对比 CT 的半自动 Agatston 评分进行比较。根据 Agatston 评分(CAC 评分风险类别:0-10、11-100、101-400 和 >400),使用加权κ 评估心血管风险分类性能。结果 对 240 例患者(平均年龄 60 岁±11 [标准差];146 例男性)进行了评估。自动深度学习 CTA 与半自动非对比 CT CAC 评分之间存在极好的正相关(Pearson 相关系数=0.96; =0.92)。基于深度学习 CTA 和非对比 CT CAC 评分的风险分类一致性极好(加权κ=0.94[95%CI:0.91,0.97]),240 例扫描中有 223 例(93%)正确分类。所有被错误分类的患者都在直接相邻的风险组中。与非对比 CT CAC 评分相比,该方法在扫描仪( =.15)、性别( =.051)和节段厚度( =.67)方面的差异无统计学意义。结论 深度学习自动钙评分方法可从 CT 血管造影图像中准确定量冠状动脉钙,并对风险进行分类。©RSNA,2021 本期还刊登了 Goldfarb 和 Cao 等人的社论。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验