Fanghu Wang1, Weiping Xu2, Wenbing Lv1, Dongyang Du1, Hui Feng1, Xiaochun Zhang2, Shuxia Wang3, Wufan Chen4, Lijun Lu5. 1. School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, Guangdong, China. 2. WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital and Guangdong Academy of Medical Sciences, 106 Zhongshan Second Road, Guangzhou, 510080, Guangdong, China. 3. WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital and Guangdong Academy of Medical Sciences, 106 Zhongshan Second Road, Guangzhou, 510080, Guangdong, China. wang_shuxia2002@aliyun.com. 4. School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, Guangdong, China. chenwufan_2016@gmail.com. 5. School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou, 510515, Guangdong, China. ljlubme@gmail.com.
Abstract
BACKGROUND: To investigate the diagnostic value of joint PET myocardial perfusion and metabolic imaging for vascular stenosis in patients with suspected obstructive coronary artery disease (CAD). METHODS: Eighty-eight patients (53 and 35 applied for training and validation, respectively) with suspected obstructive CAD were referred to 13N-NH3 PET/CT myocardial perfusion imaging (MPI) and 18F-FDG PET/CT myocardial metabolic imaging (MMI) with available coronary angiography for analysis. One semi-quantitative indicator summed rest score (SRS) and five quantitative indicators, namely, perfusion defect extent (EXT), total perfusion deficit (TPD), myocardial blood flow (MBF), scar degree (SCR), and metabolism-perfusion mismatch (MIS), were extracted from the PET rest MPI and MMI scans. Different combinations of indicators and seven machine learning methods were used to construct diagnostic models. Diagnostic performance was evaluated using the sum of four metrics (noted as sumScore), namely, area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RESULTS: In univariate analysis, MIS outperformed other individual indicators in terms of sumScore (2.816-3.042 vs 2.138-2.908). In multivariate analysis, support vector machine (SVM) consisting of three indicators (MBF, SCR, and MIS) achieved the best performance (AUC 0.856, accuracy 0.810, sensitivity 0.838, specificity 0.757, and sumScore 3.261). This model consistently achieved significantly higher AUC compared with the SRS method for four specific subgroups (0.897, 0.839, 0.875, and 0.949 vs 0.775, 0.606, 0.713, and 0.744; P = 0.041, 0.005, 0.034 0.003, respectively). CONCLUSIONS: The joint evaluation of PET rest MPI and MMI could improve the diagnostic performance for obstructive CAD. The multivariate model (MBF, SCR, and MIS) combined with SVM outperformed other methods.
BACKGROUND: To investigate the diagnostic value of joint PET myocardial perfusion and metabolic imaging for vascular stenosis in patients with suspected obstructive coronary artery disease (CAD). METHODS: Eighty-eight patients (53 and 35 applied for training and validation, respectively) with suspected obstructive CAD were referred to 13N-NH3 PET/CT myocardial perfusion imaging (MPI) and 18F-FDG PET/CT myocardial metabolic imaging (MMI) with available coronary angiography for analysis. One semi-quantitative indicator summed rest score (SRS) and five quantitative indicators, namely, perfusion defect extent (EXT), total perfusion deficit (TPD), myocardial blood flow (MBF), scar degree (SCR), and metabolism-perfusion mismatch (MIS), were extracted from the PET rest MPI and MMI scans. Different combinations of indicators and seven machine learning methods were used to construct diagnostic models. Diagnostic performance was evaluated using the sum of four metrics (noted as sumScore), namely, area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RESULTS: In univariate analysis, MIS outperformed other individual indicators in terms of sumScore (2.816-3.042 vs 2.138-2.908). In multivariate analysis, support vector machine (SVM) consisting of three indicators (MBF, SCR, and MIS) achieved the best performance (AUC 0.856, accuracy 0.810, sensitivity 0.838, specificity 0.757, and sumScore 3.261). This model consistently achieved significantly higher AUC compared with the SRS method for four specific subgroups (0.897, 0.839, 0.875, and 0.949 vs 0.775, 0.606, 0.713, and 0.744; P = 0.041, 0.005, 0.034 0.003, respectively). CONCLUSIONS: The joint evaluation of PET rest MPI and MMI could improve the diagnostic performance for obstructive CAD. The multivariate model (MBF, SCR, and MIS) combined with SVM outperformed other methods.
Authors: Riemer H J A Slart; Jeroen J Bax; Richard M de Jong; Jaep de Boer; Hildo J Lamb; Piet H Mook; Antoon T M Willemsen; Willem Vaalburg; Dirk J van Veldhuisen; Pieter L Jager Journal: J Nucl Med Date: 2004-02 Impact factor: 10.057