深度学习驱动的光学相干断层扫描分析用于急性冠状动脉综合征患者心血管结局预测
Deep-learning-driven optical coherence tomography analysis for cardiovascular outcome prediction in patients with acute coronary syndrome.
作者信息
Hamana Tomoyo, Nishimori Makoto, Shibata Satoki, Kawamori Hiroyuki, Toba Takayoshi, Hiromasa Takashi, Kakizaki Shunsuke, Sasaki Satoru, Fujii Hiroyuki, Osumi Yuto, Iwane Seigo, Yamamoto Tetsuya, Naniwa Shota, Sakamoto Yuki, Fukuishi Yuta, Matsuhama Koshi, Tsunamoto Hiroshi, Okamoto Hiroya, Higuchi Kotaro, Kitagawa Tatsuya, Shinohara Masakazu, Kuroda Koji, Iwasaki Masamichi, Kozuki Amane, Shite Junya, Takaya Tomofumi, Hirata Ken-Ichi, Otake Hiromasa
机构信息
Division of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe, Japan.
Division of Molecular Epidemiology, Kobe University Graduate School of Medicine, Kobe, Japan.
出版信息
Eur Heart J Digit Health. 2024 Sep 27;5(6):692-701. doi: 10.1093/ehjdh/ztae067. eCollection 2024 Nov.
AIMS
Optical coherence tomography (OCT) can identify high-risk plaques indicative of worsening prognosis in patients with acute coronary syndrome (ACS). However, manual OCT analysis has several limitations. In this study, we aim to construct a deep-learning model capable of automatically predicting ACS prognosis from patient OCT images following percutaneous coronary intervention (PCI).
METHODS AND RESULTS
Post-PCI OCT images from 418 patients with ACS were input into a deep-learning model comprising a convolutional neural network (CNN) and transformer. The primary endpoint was target vessel failure (TVF). Model performances were evaluated using Harrell's -index and compared against conventional models based on human observation of quantitative (minimum lumen area, minimum stent area, average reference lumen area, stent expansion ratio, and lesion length) and qualitative (irregular protrusion, stent thrombus, malapposition, major stent edge dissection, and thin-cap fibroatheroma) factors. GradCAM activation maps were created after extracting attention layers by using the transformer architecture. A total of 60 patients experienced TVF during follow-up (median 961 days). The -index for predicting TVF was 0.796 in the deep-learning model, which was significantly higher than that of the conventional model comprising only quantitative factors (-index: 0.640) and comparable to that of the conventional model, including both quantitative and qualitative factors (-index: 0.789). GradCAM heat maps revealed high activation corresponding to well-known high-risk OCT features.
CONCLUSION
The CNN and transformer-based deep-learning model enabled fully automatic prognostic prediction in patients with ACS, with a predictive ability comparable to a conventional survival model using manual human analysis.
CLINICAL TRIAL REGISTRATION
The study was registered in the University Hospital Medical Information Network Clinical Trial Registry (UMIN000049237).
目的
光学相干断层扫描（OCT）能够识别急性冠状动脉综合征（ACS）患者中提示预后恶化的高危斑块。然而，手动OCT分析存在若干局限性。在本研究中，我们旨在构建一种深度学习模型，能够根据经皮冠状动脉介入治疗（PCI）后患者的OCT图像自动预测ACS预后。
方法与结果
将418例ACS患者PCI后的OCT图像输入到一个由卷积神经网络（CNN）和Transformer组成的深度学习模型中。主要终点是靶血管失败（TVF）。使用Harrell's C指数评估模型性能，并与基于人工观察定量（最小管腔面积、最小支架面积、平均参考管腔面积、支架扩张率和病变长度）和定性（不规则突出、支架血栓、贴壁不良、主要支架边缘夹层和薄帽纤维粥样斑块）因素的传统模型进行比较。通过使用Transformer架构提取注意力层后创建GradCAM激活图。共有60例患者在随访期间发生TVF（中位时间961天）。深度学习模型预测TVF的C指数为0.796，显著高于仅包含定量因素的传统模型（C指数：0.640），与包含定量和定性因素的传统模型相当（C指数：0.789）。GradCAM热图显示与众所周知的高危OCT特征相对应的高激活。
结论
基于CNN和Transformer的深度学习模型能够对ACS患者进行全自动预后预测，其预测能力与使用人工分析的传统生存模型相当。
临床试验注册
该研究已在大学医院医学信息网络临床试验注册中心注册（UMIN000049237）。
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