Yoneyama Hiroto, Nakajima Kenichi, Taki Junichi, Wakabayashi Hiroshi, Matsuo Shinro, Konishi Takahiro, Okuda Koichi, Shibutani Takayuki, Onoguchi Masahisa, Kinuya Seigo
Department of Radiological Technology, Kanazawa University Hospital, 13-1 Takara-machi, Kanazawa, 920-8641, Japan.
Department of Nuclear Medicine, Kanazawa University Hospital, Kanazawa, Japan.
Eur J Hybrid Imaging. 2019 Mar 18;3(1):4. doi: 10.1186/s41824-019-0052-8.
BACKGROUND: Detecting culprit coronary arteries in patients with ischemia using only myocardial perfusion single-photon emission computed tomography (SPECT) can be challenging. This study aimed to improve the detection of culprit regions using an artificial neural network (ANN) to analyze hybrid images of coronary computed tomography angiography (CCTA) and myocardial perfusion SPECT. METHODS: This study enrolled 59 patients with stable coronary artery disease (CAD) who had been assessed by coronary angiography within 60 days of myocardial perfusion SPECT. Two nuclear medicine physicians interpreted the myocardial perfusion SPECT and hybrid images with four grades of confidence, then drew regions on polar maps to identify culprit coronary arteries. The gold standard was determined by the consensus of two other nuclear cardiology specialist based on coronary angiography findings and clinical information. The ability to detect culprit coronary arteries was compared among experienced nuclear cardiologists and the ANN. Receiver operating characteristics (ROC) curves were analyzed and areas under the ROC curves (AUC) were determined. RESULTS: Using hybrid images, observer A detected CAD in the right (RCA), left anterior descending (LAD), and left circumflex (LCX) coronary arteries with 83.6%, 89.3%, and 94.4% accuracy, respectively and observer B did so with 72.9%, 84.2%, and 89.3%, respectively. The ANN was 79.1%, 89.8%, and 89.3% accurate for each coronary artery. Diagnostic accuracy was comparable between the ANN and experienced nuclear medicine physicians. The AUC was significantly improved using hybrid images in the RCA region (observer A: from 0.715 to 0.835, p = 0.0031; observer B: from 0.771 to 0.843, p = 0.042). To detect culprit coronary arteries in perfusion defects of the inferior wall without using hybrid images was problematic because the perfused areas of the LCX and RCA varied among individuals. CONCLUSIONS: Hybrid images of CCTA and myocardial perfusion SPECT are useful for detecting culprit coronary arteries. Diagnoses using artificial intelligence are comparable to that by nuclear medicine physicians.
背景：仅使用心肌灌注单光子发射计算机断层扫描（SPECT）来检测缺血患者的罪犯冠状动脉可能具有挑战性。本研究旨在利用人工神经网络（ANN）分析冠状动脉计算机断层扫描血管造影（CCTA）和心肌灌注SPECT的混合图像，以提高对罪犯区域的检测能力。 方法：本研究纳入了59例稳定型冠状动脉疾病（CAD）患者，这些患者在心肌灌注SPECT检查后60天内接受了冠状动脉造影评估。两名核医学医生对心肌灌注SPECT和混合图像进行解读，并给出四个置信等级，然后在极坐标图上绘制区域以识别罪犯冠状动脉。金标准由另外两名核心脏病学专家根据冠状动脉造影结果和临床信息共同确定。比较了经验丰富的核心脏病专家和ANN检测罪犯冠状动脉的能力。分析了受试者工作特征（ROC）曲线并确定了ROC曲线下面积（AUC）。 结果：使用混合图像时，观察者A检测右冠状动脉（RCA）、左前降支（LAD）和左旋支（LCX）冠状动脉中CAD的准确率分别为83.6%、89.3%和94.4%，观察者B的准确率分别为72.9%、84.2%和89.3%。ANN对每条冠状动脉的准确率分别为79.1%、89.8%和89.3%。ANN与经验丰富的核医学医生的诊断准确率相当。在RCA区域使用混合图像时，AUC显著提高（观察者A：从0.715提高到0.835，p = 0.0031；观察者B：从0.771提高到0.843，p = 0.042）。在不使用混合图像的情况下，检测下壁灌注缺损中的罪犯冠状动脉存在问题，因为LCX和RCA的灌注区域因人而异。 结论：CCTA和心肌灌注SPECT的混合图像有助于检测罪犯冠状动脉。使用人工智能进行的诊断与核医学医生的诊断相当。
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