在大量人群中通过机器学习预测心肌灌注单光子发射计算机断层扫描后的血管重建情况。
Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population.
作者信息
Arsanjani Reza, Dey Damini, Khachatryan Tigran, Shalev Aryeh, Hayes Sean W, Fish Mathews, Nakanishi Rine, Germano Guido, Berman Daniel S, Slomka Piotr
机构信息
Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Taper A238, Los Angeles, CA, 90048, USA.
David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
出版信息
J Nucl Cardiol. 2015 Oct;22(5):877-84. doi: 10.1007/s12350-014-0027-x. Epub 2014 Dec 6.
OBJECTIVE
We aimed to investigate if early revascularization in patients with suspected coronary artery disease can be effectively predicted by integrating clinical data and quantitative image features derived from perfusion SPECT (MPS) by machine learning (ML) approach.
METHODS
713 rest (201)Thallium/stress (99m)Technetium MPS studies with correlating invasive angiography with 372 revascularization events (275 PCI/97 CABG) within 90 days after MPS (91% within 30 days) were considered. Transient ischemic dilation, stress combined supine/prone total perfusion deficit (TPD), supine rest and stress TPD, exercise ejection fraction, and end-systolic volume, along with clinical parameters including patient gender, history of hypertension and diabetes mellitus, ST-depression on baseline ECG, ECG and clinical response during stress, and post-ECG probability by boosted ensemble ML algorithm (LogitBoost) to predict revascularization events. These features were selected using an automated feature selection algorithm from all available clinical and quantitative data (33 parameters). Tenfold cross-validation was utilized to train and test the prediction model. The prediction of revascularization by ML algorithm was compared to standalone measures of perfusion and visual analysis by two experienced readers utilizing all imaging, quantitative, and clinical data.
RESULTS
The sensitivity of machine learning (ML) (73.6% ± 4.3%) for prediction of revascularization was similar to one reader (73.9% ± 4.6%) and standalone measures of perfusion (75.5% ± 4.5%). The specificity of ML (74.7% ± 4.2%) was also better than both expert readers (67.2% ± 4.9% and 66.0% ± 5.0%, P < .05), but was similar to ischemic TPD (68.3% ± 4.9%, P < .05). The receiver operator characteristics areas under curve for ML (0.81 ± 0.02) was similar to reader 1 (0.81 ± 0.02) but superior to reader 2 (0.72 ± 0.02, P < .01) and standalone measure of perfusion (0.77 ± 0.02, P < .01).
CONCLUSION
ML approach is comparable or better than experienced readers in prediction of the early revascularization after MPS, and is significantly better than standalone measures of perfusion derived from MPS.
目的
我们旨在研究通过机器学习（ML）方法整合临床数据和灌注单光子发射计算机断层扫描（MPS）得出的定量图像特征，能否有效预测疑似冠状动脉疾病患者的早期血运重建。
方法
纳入713例静息（201）铊/负荷（99m）锝MPS研究，这些研究均在MPS后90天内进行了有创血管造影检查，并发生了372例血运重建事件（275例经皮冠状动脉介入治疗/97例冠状动脉旁路移植术）（91%在30天内）。分析了短暂性缺血性扩张、负荷仰卧/俯卧位总灌注缺损（TPD）、仰卧位静息和负荷TPD、运动射血分数和收缩末期容积，以及包括患者性别、高血压和糖尿病病史、基线心电图ST段压低、负荷时心电图和临床反应，以及通过增强集成ML算法（LogitBoost）预测血运重建事件的心电图后概率等临床参数。这些特征是从所有可用的临床和定量数据（33个参数）中使用自动特征选择算法挑选出来的。采用十折交叉验证来训练和测试预测模型。将ML算法对血运重建的预测结果与两名经验丰富的阅片者利用所有影像、定量和临床数据进行的灌注独立测量及视觉分析结果进行比较。
结果
机器学习（ML）预测血运重建的敏感性（73.6%±4.3%）与一名阅片者（73.9%±4.6%）及灌注独立测量结果（75.5%±4.5%）相似。ML的特异性（74.7%±4.2%）也优于两名专家阅片者（分别为67.2%±4.9%和66.0%±5.0%，P<.05），但与缺血性TPD（68.3%±4.9%，P<.05）相似。ML的受试者操作特征曲线下面积（0.81±0.02）与阅片者1（0.81±0.02）相似，但优于阅片者2（0.72±0.02，P<.01）和灌注独立测量结果（0.77±0.02，P<.01）。
结论
在预测MPS后的早期血运重建方面，ML方法与经验丰富的阅片者相当或更优，且显著优于MPS得出的灌注独立测量结果。
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