Application Value of AI-Assisted Quantitative Plaque Parameters Combined with CT-FFR in Predicting Major Adverse Cardiovascular Events

Application Value of AI-Assisted Quantitative Plaque Parameters Combined with CT-FFR in Predicting Major Adverse Cardiovascular Events | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Application Value of AI-Assisted Quantitative Plaque Parameters Combined with CT-FFR in Predicting Major Adverse Cardiovascular Events Xinwei Zhang, Mengyuan Bao, Yongshun Wu, Haicheng Qi, Yan Xing This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9321407/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Rationale and Objective: The objectives of the present study were to determine the plaque parameters and computed tomography fractional flow reserve (CT-FFR) derived from artificial intelligence (AI)-assisted coronary computed tomography angiography, calculate the safety threshold of plaque burden, and investigate the efficacy of the combination of these two metrics for predicting major adverse cardiovascular events (MACEs). Materials and Methods A total of 381 patients with coronary heart disease were included in the study, and plaque parameters and CT-FFR data were collected. Plaque parameter risk cutoff values that predict MACEs were obtained through subject operating characteristic curves. Patients were followed up, and univariate and multivariate Cox regression analyses were performed. Kaplan‒Meier survival curves were plotted, and a plaque model, CT-FFR model, and plaque combined CT-FFR model were established to predict MACEs. A LASSO-Cox model and Cox survival neural network model were constructed for MACE prediction. Results The calcified plaque volume, noncalcified plaque volume (NCPV), total plaque volume, calcified percent atheroma volume, noncalcified percent atheroma volume (NCPAV), and total percent atheroma volume correlated with the occurrence of MACEs. Multivariate Cox regression revealed that total-NCPV and total-NCPAV were the strongest predictors of MACEs [total-NCPV \(\:\text{H}\text{R}:\:4.752,\:95\text{\%}\:\text{C}\text{I}:2.708-8.340,\text{P}<0.001\) and total-NCPAV (HR 5.073, 95% CI: \(\:2.930-8.786\) , P < 0.001)]. The C indices of the LASSO-Cox model and Cox survival neural network model for predicting MACEs were 0.747 ( \(\:95\text{\%}\:\text{C}\text{I}:0.674-0.816,\text{P}<0.001\) ) and 0.730 ( \(\:95\text{\%}\:\text{C}\text{I}:0.628-0.833\) ), respectively. Conclusion The combination of AI-assisted measured quantitative plaque parameters and CT-FFR has high clinical value for the prediction of MACEs, among which the RCA plaque burden is the most critical factor for predicting MACEs. Coronary computed tomography angiography Computed tomography fractional flow reserve Major adverse cardiovascular events Coronary artery plaque Figures Figure 1 Figure 2 Figure 3 Figure 4 Abstract image Introduction Coronary artery disease (CAD) is a major disease affecting human health [ 1 , 2 ] , and its incidence is increasing annually. Coronary computed tomography angiography (CCTA) has become the first-line imaging modality for intermediate- and low-risk patients with CAD [ 3 , 4 ] . Plaque burden is a quantitative indicator of the ratio of total plaque volume to vessel volume, and numerous studies have demonstrated an association between plaque burden and major adverse cardiovascular events (MACEs) [ 5 – 7 ] . Coronary atherosclerotic plaques include lipid components, fibrous components, and calcified components, whereas low-attenuation plaques contain necrotic lipid cores or are complicated by intraplaque hemorrhage [ 8 ] . As unstable components of plaques, these substances can induce plaque rupture and subsequent MACEs [ 9 ] . An increase in total plaque volume can also cause coronary lumen stenosis, leading to myocardial ischemia and ultimately triggering MACEs [ 10 ] . Therefore, it is highly necessary for clinicians to implement early interventions in patients at risk of MACEs [ 11 ] . Currently, the measurement of plaque burden largely relies on postprocessing software, and some strategies require the transfer of CCTA images to dedicated postprocessing workstations [ 12 ] . Manually delineating the plaque-corresponding region of interest (ROI) to obtain specific plaque parameters is a form of semiautomatic segmentation, and it requires the images to be processed on dedicated postprocessing workstations. We have utilized AI-powered software for fully automated segmentation of coronary artery plaques [ 13 ] . Following CCTA scanning, clinicians and radiologists can promptly determine the conditions of the patient. This software provides one-stop, fully automated delivery of valuable morphological features (calcified and noncalcified plaques) and generates key hemodynamic data, such as CT-FFR, thereby enabling more precise and individualized treatment for patients. Previous studies have extensively focused on the total plaque burden of the three major coronary artery branches in patients with CAD [ 14 , 15 ] . Limited research has been conducted on the predictive value of the plaque burden in the left anterior descending (LAD) artery, left circumflex (LCX) artery, and right coronary (RCA) artery for MACEs, and the specific weight of each of these three vessels in the prediction of MACEs remains to be elucidated. Through the synergistic innovation of technical tools, research perspectives and methodologies, the present study overcomes the semiautomated limitations of traditional plaque burden measurements that rely on postprocessing workstations. By adopting an AI-based fully automated segmentation software, we achieved integrated and efficient quantification of plaque morphological features (calcified and noncalcified components) and CT-FFR functional data in a synchronous manner, compensating for the deficiency of previous CAD studies focusing mostly on the total plaque burden of the three major coronary arteries. MATERIALS AND METHODS Patient Population The present study was approved by the Medical Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University (approval number: 20210226-134), and informed consent from the participants was waived. A total of 381 inpatients who underwent CCTA and were diagnosed with CAD at our institution from January 2022 to May 2022 were consecutively enrolled. After discharge, information regarding the occurrence of MACEs in these patients was obtained by reviewing outpatient and inpatient medical records, as well as conducting telephone follow-ups, with a maximum follow-up duration of 18 months. MACEs were defined as cardiac death, worsening of stable angina or progression to unstable angina, fatal or nonfatal myocardial infarction, and performance of revascularization procedures, such as coronary artery bypass grafting or PCI. The inclusion criteria were as follows: patients diagnosed with CAD via comprehensive clinical evaluation; the availability of complete clinically relevant data; no history of known acute coronary events, stent implantation, or coronary artery bypass grafting prior to CCTA examination; clear CCTA images with adequate contrast medium filling; assessable quality on both subjective and objective evaluations; and the absence of significant artifacts. The exclusion criteria were as follows: prior history of confirmed acute myocardial infarction, severe cardiac insufficiency with New York Heart Association (NYHA) functional class III–IV, or comorbidity with nonischemic myocardial diseases, such as dilated cardiomyopathy, hypertrophic cardiomyopathy, and arrhythmogenic right ventricular cardiomyopathy; severe arrhythmias, such as severe atrial fibrillation and severe atrioventricular block; implantation of temporary or permanent cardiac pacemakers; heart valve replacement; postvalvuloplasty status; presence of malignant tumors, hematological diseases, or autoimmune diseases; presence of renal dysfunction or underlying renal diseases; contrast medium allergy; and loss to follow-up. Although 398 patients were initially enrolled, 12 patients with unclear images and incomplete clinical data were excluded, and 5 patients lost to follow-up due to unavailable telephone contact were also excluded. Thus, 381 eligible patients were enrolled in the final cohort. Figure 1 presents the flowchart of the study. CCTA Acquisition All CCTA examinations were performed using a Canon Aquilion ONE Genesis CT scanner (Canon Medical Systems, Japan). The anatomical scan range was set from 1.0 cm below the tracheal carina to 1.5 cm below the inferior border of the heart. The delay time for contrast-enhanced scanning was precisely determined using the bolus tracking technique. During the examination, a dual-syringe high-pressure injector was used to intravenously administer nonionic iodinated contrast medium, with an individualized injection protocol based on a standardized iodine delivery rate. The specific CCTA scan parameters were as follows: detector configuration, 320 × 0.5 mm; X-ray tube rotation time, 0.275 s per rotation; image matrix, 512 × 512; collimation, 128 × 0.5 mm; reconstruction slice thickness, 0.5 mm; tube voltage, 120 kV; and tube current, 300 mAs. All CCTA imaging data were reconstructed according to optimal image quality criteria. After reconstruction, all the images underwent subjective and objective assessments, and only those meeting the predefined quality standards were included for subsequent analysis. Imaging analysis After the patient underwent CCTA, the images were uploaded to AI platform software. The plaque volume (mm³) of each coronary artery lesion was calculated and then aggregated to determine the total plaque volume of the patient. To account for variations in the coronary artery volume, plaque volume was normalized to the total vascular volume of each patient using the following formula: plaque volume (mm³)/vascular volume (mm³) × 100%. This metric is termed the PAV. Patch types are classified using the Hounsfield unit (HU) range, where NCPV is defined as patches exhibiting any component at the pixel level with HU within the range of -30 HU to + 350 HU. CPVs are defined as patches with HU values > 350. For CT-FFR, the lowest value among the three major branch measurements was selected for collection. To avoid including gray-zone data, CT-FFR values < 0.75 were considered abnormal. The image analysis process is illustrated in Fig. 2 . Patient data for total-TPV, total-TPAV, total-CPV, total-CPAV, total-NCPV, and total-NCPAV were collected. In addition to the total plaque volume of the three major branches, the following values were collected separately: LAD-TPV, LAD-TPAV, LAD-CPV, LAD-CPAV, LAD-NCPV, LAD-NCPAV, LCX-TPV, LCX-TPAV, LCX-CPV, LCX-CPAV, LCX-NCPV, LCX-NCPAV, RCA-TPV, RCA-TPAV, RCA-CPV, RCA-CPAV, RCA-NCPV, and RCA-NCPAV. In total, 24 parameters were evaluated. Statistical Analysis Analyses were performed using SPSS statistical software (version 26.0; IBM, USA), R, and Python. Count data are expressed as counts (%) and were compared between categorical variable groups using chi-square tests. Continuous data following a normal distribution are presented as the mean ± standard deviation (x ± s) and were compared between groups using t tests. Continuous data that did not follow a normal distribution are expressed as medians (P25 and P75) for nonnormally distributed data, with comparisons between groups performed using the Mann‒Whitney U test. A receiver operating characteristic (ROC) curve was used to identify the optimal risk cutoff value for plaque parameters in predicting MACEs. On the basis of this optimal cutoff, plaque parameters were treated as categorical data for univariate and multivariate Cox regression survival analyses. The results are expressed as hazard ratios (HRs). Kaplan‒Meier survival curves were plotted. Cox regression survival analysis was performed on CT-FFR data to preliminarily assess the ability of total plaque burden to predict MACEs. The diagnostic efficacy of the plaque model, CT-FFR model and plaque + CT-FFR combined model was subsequently calculated, with the results expressed as the C-index. Finally, R software was used to construct a LASSO Cox model for predicting MACEs, and Python was used to construct a Cox neural network model for MACE prediction. A two-tailed significance level of α = 0.05 was used for all the statistical analyses, and a P value < 0.05 was considered clinically significant. A LASSO Cox model was constructed to predict MACEs. Through L1 regularization, this model achieves variable sparsification and selection, eliminates redundant variables (e.g., indices with poor predictive power among branch parameters), and simplifies the model structure while preserving the inherent survival analysis properties of the traditional Cox model, thus endowing the model with high interpretability for its predictive results. A Cox neural network model was constructed to predict MACEs. The network structure of this model was "8 (input features) → 32 (hidden layer 1) → 16 (hidden layer 2) → 1 (risk score output)", with ReLU as the activation function and a dropout rate of 0.2 (to prevent overfitting). The model was trained on a CPU for 100 epochs, using Adam as the optimizer (with a learning rate of 1e-3 and an L2 regularization coefficient of 1e-5), and the loss function adopted Cox partial likelihood loss (which is suitable for the event-time association learning of survival data). RESULTS 1. Clinical Characteristics Table 1 shows the general clinical information of the study population. A total of 381 patients were included in this study, among whom 67 developed MACEs. The MACE subgroup included 45 cases of percutaneous coronary intervention with stent implantation, 5 cases of coronary artery bypass grafting (CABG), 1 case of percutaneous transluminal coronary angioplasty, 2 cases of rehospitalization due to aggravated stable angina pectoris, 2 cases of rehospitalization due to aggravated unstable angina pectoris, 1 case of nonfatal myocardial infarction, 1 case of malignant arrhythmia, 5 cases of death from myocardial infarction, 1 case of death from heart failure, and 4 cases of death from other causes. The remaining 314 patients were categorized into the non-MACE group. No statistically significant differences were detected in the clinical indicators between the two groups, whereas statistically significant differences were detected in the CPV, NCPV, TPV, CPAV, NCPAV and TPAV between the two groups ( P <0.01). Table 1 Clinical baseline characteristics of patients in the MACE group and non-MACE group Parameters MACEs Group( n = 67) Non-MACEs Group( n = 314) t/Z/χ² P- value Age (years) 61.31 ± 10.95 60.70 ± 10.67 0.427 b 0.670 male (n, %) 49(73.1%) 220(70.1%) 0.251 a 0.616 BMI(kg/m²)* 27.04 ± 4.05 26.72 ± 4.39 0.558 b 0.577 hypertension (n, %) 46(68.7%) 234(74.5%) 0.975 a 0.323 Diabetes mellitus (n, %) 22(32.8%) 109(34.7%) 0.086 a 0.769 TG (mg/dL)* 1.57(1.10,2.05) 1.45(1.03,2.04) -0.704 c 0.481 TG (mg/dL)* 4.24 ± 1.21 4.03 ± 1.22 1.333 b 0.183 Prior use of statins (n, %) 26(38.8%) 144(45.9%) 1.112 a 0.292 Prior use of anticoagulants (n, %) 23(34.3%) 99(31.5%) 0.199 a 0.656 HDL-C (mg/dL)* 0.93 ± 0.24 0.94 ± 0.28 -0.453 b 0.651 LDL-C (mg/dL)* 2.70 ± 0.95 2.61 ± 0.96 -0.656 b 0.512 Total-CPV(mm 3 )* 134.21(30.13,264.59) 37.73(6.12,148.55) -3.972 c <0.001 Total-NCPV(mm 3 )* 158.19(91.12,216.84) 72.29(31.20,133.35) -5.922 c <0.001 Total-TPV(mm 3 )* 297.07(171.07,504.25) 128.29(56.86,261.68) -5.819 c <0.001 Total-CPAV(%)* 5.64(0.91,13.54) 1.38(0.25,5.74) -4.209 c <0.001 Total-NCPAV(%)* 7.10(3.73,10.31) 2.88(1.40,5.95) -6.280 c <0.001 Total-TPAV(%)* 13.78(7.21,22.94) 5.45(2.12,12.00) -6.105 c <0.001 BMI: body mass index; TG: triglyceride; TC: total cholesterol; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; Total-CPV: total-calcified plaque volume; Total-NCPV: total-noncalcified plaque volume; Total-TPV: total-total plaque volume; Total-CPAV: total-calcified percent atheroma volume; Total-NCPAV: total-noncalcified percent atheroma volume; Total-TPAV: total-total percent atheroma volume; a is χ²-value,, b is t-value, c is Z-value 2. Cox Analysis of Total Plaque Burden Based on Risk Cutoff Values The risk cutoff values for total-CPV, total-NCPV and total-TPV were 68.78 mm³, 108.81 mm³ and 206.78 mm³, respectively; those for total-CPAV, total-NCPAV and total-TPAV were 4.54%, 4.68% and 11.11%, respectively. Compared with the low-risk group, the high-risk group had an HR of 3.121 for total-CPV ( P < 0.01). Moreover, the HR for the high-risk group versus the low-risk group was 4.528 ( P < 0.01) for total-NCPV, 4.402 ( P < 0.01) for total-TPV, 3.175 ( P < 0.01) for total-CPAV, 4.859 ( P < 0.01) for total-NCPAV, and 4.003 ( P < 0.01) for total-TPAV. Detailed data are presented in Table 2 . The Kaplan–Meier (KM) survival curves are shown in Fig. 3 . After adjusting for sex, age, diabetes mellitus status, TC level, TG level, LDL-C level, and history of statin use, further multivariate Cox regression analysis demonstrated that total-NCPV and total-NCPAV were the strongest predictors of MACEs, with hazard ratios (HRs) of 4.7 ( P < 0.01) and 5.0 ( P < 0.01), respectively. Table 2 Predictive hazard ratios of plaque parameters above the risk cutoff value for MACEs Univariate Cox regression analysis Multivariate Cox regression analysis HR (95%CI) P- value HR (95%CI) P- value Hazard ratios of plaque volume above the optimal risk cutoff Total-CPV>68.78mm 3 3.121(1.874～5.198) <0.001 3.400(1.989～5.814) 108.81mm 3 4.528(2.637～7.775) <0.001 4.752(2.708～8.340) 206.78mm 3 4.402(2.564～7.559) <0.001 4.503(2.587～7.836) 4.54% 3.175(1.941～5.191) <0.001 3.476(2.087～5.788) 4.68% 4.859(2.829～8.344) <0.001 5.073(2.930～8.786) 11.11% 4.003(2.428～6.599) <0.001 4.190(2.515～6.979) <0.001 Total-CPV: total-calcified plaque volume; Total-NCPV: total-noncalcified plaque volume; Total-TPV: total-total plaque volume; Total-CPAV: total-calcified percent atheroma volume; Total-NCPAV: total-noncalcified percent atheroma volume; Total-TPAV: total-total percent atheroma volume. 3. Predicting MACEs with Plaque Parameters as Continuous Variables For each 1 mm³ increase in total-CPV, NCPV, and total-TPV, the HRs for MACEs was 1.002, 1.005, and 1.002, respectively. For each 1% increase in total-CPAV, total-NCPAV, and total-TPAV, the HRs were 1.049, 1.137, and 1.049, respectively. The detailed data are presented in Supplemental Table 3. Table 3 Performance of plaque burden as a continuous variable for predicting MACEs . Plaque Parameters Adjusted HR for gender, age, diabetes, TC, TG, LDL-C, and statin use history 95%CI P- value Total-CPV 1.002 1.001–1.003 0.001 Total-NCPV 1.005 1.003–1.006 <0.001 Total-TPV 1.002 1.001–1.003 <0.001 Total-CPAV 1.049 1.029–1.071 <0.001 Total-NCPAV 1.137 1.097–1.178 <0.001 Total-TPAV 1.049 1.034–1.065 <0.001 Total-CPV: total-calcified plaque volume; Total-NCPV: total-noncalcified plaque volume; Total-TPV: total-total plaque volume; Total-CPAV: total-calcified percent atheroma volume; Total-NCPAV: total-noncalcified percent atheroma volume; Total-TPAV: total-total percent atheroma volume. 4. Predictive Performance of the Combined Model of Plaque Parameters and CT-FFR for MACEs To further distinguish the predictive performance of total plaque parameters from that of plaque parameters in the LAD, LCX, and RCA, as well as to lay the foundation for the LASSO-Cox model and the Cox survival neural network model, Model 1 incorporated the following parameters: LAD-CPAV, LAD-NCPAV, LCX-CPAV, LCX-NCPAV, RCA-CPAV, and RCA-NCPAV (Table 4 ). Table 4 C-index of the plaque model, CT-FFR model, and combined model Model Type C index P- value 95%CI* Plaque Parameters Model (Model 1) 0.744 <0.001 0.686–0.801 CT-FFR Model (Model 2) 0.593 <0.001 0.527–0.659 Combined Model (Model 3) 0.750 <0.001 0.696–0.804 The 95% confidence intervals were estimated by the bootstrap method (1000 replicates) 5. LASSO-Cox Model The data in Table 2 indicate that total-NCPV and total-NCPAV were the strongest predictors of MACEs. Prior to constructing the LASSO Cox model, we performed separate univariate Cox regression analyses for TPV, TPAV, CPV, CPAV, NCPV and NCPAV in the LAD, LCX and RCA. The HR values of RCA-CPAV and RCA-NCPAV were relatively high, both exceeding 3. Therefore, a total of 10 indicators were initially incorporated into the LASSO Cox model. The demographic indicators included sex and age. The total plaque burden indicators included total-CPV and total-NCPAV. The vessel-specific plaque indicators included the LAD-NCPAV, RCA-CPAV, and RCA-NCPAV. The ROC validation threshold indicators included RCA-CPAV > 2.8%, RCA-NCPAV > 3.49%, and CT-FFR < 0.76 (functional indicator). L1 regularization was applied to eliminate redundant variables, achieving variable sparsity and selection. Ultimately, six variables with independent predictive values for MACEs were retained (all the coefficients were positive, indicating a positive correlation with MACE risk). The C-index of the LASSO-Cox model for MACE prediction was 0.747 (Supplemental Table 5). All the variables were ranked by the absolute value of their coefficients, with larger coefficients indicating greater contributions to risk prediction (Supplemental Table 6). RCA-CPAV > 2.8%, RCA-NCPAV, and RCA-NCPAV > 3.49% were identified as stronger predictors of MACEs. Table 5 C-index of the LASSO-Cox Model Model Type C-index P- value 95%CI LASSO-Cox 0.747 2.8% 0.552479871 Binary RCA-NCPAV>3.49% 0.254248032 Binary Total-NCPAV 0.076836587 Continuous CT-FFR<0.76 0.068289599 Binary RCA-CPAV 0.003903487 Continuous LAD-NCPAV 0.001658234 Continuous 6. Cox Survival Neural Network Model During the development of the Cox survival neural network model, the right coronary artery indicators were the strongest predictors of MACEs (Table 6). Total-NCPAV, a total plaque burden indicator, ranked third in terms of contribution value (Table 6) and was selected for inclusion. An interaction term of RCA-NCPAV × CT-FFR < 0.76 was constructed (a continuous × binary indicator, reflecting the morphological‒functional interaction effect). If RCA-NCPAV is 0 and CT-FFR is 1 (no ischemic risk), the value of the interaction indicator is 0, and if RCA-NCPAV is 5% and CT-FFR is 0 (positive for ischemia), the value of the interaction indicator is also 0. When both RCA-NCPAV and CT-FFR are positive, the value of the interaction indicator is equal to that of RCA-NCPAV. This indicator is an intensity quantification indicator under dual-positive conditions, which can directly capture the synergistic risk of “high morphological risk (abundant noncalcified plaques in the RCA) + functional ischemia (low CT-FFR)” and improve the clinical interpretability of the model. The baseline demographic indicators included sex and age, and the global plaque burden indicator was total-NCPAV. The vessel-specific plaque indicators included RCA-CPAV and RCA-NCPAV, while the ROC-validated threshold indicators were RCA-NCPAV > 3.49% and CT-FFR < 0.76. Moreover, the interaction-derived indicator was defined as RCA-NCPAV*CT-FFR < 0.76. For the Cox survival neural network model, the study dataset was partitioned into a training set and a test set at a ratio of 7:3. The model yielded C-indices of 0.751 in the training set ( P < 0.001) and 0.730 in the test set ( P < 0.001), indicating favorable predictive performance for MACEs (Table 7). Regarding the training curves, the ability of the model to predict MACEs gradually improved with increasing training epochs, suggesting that the Cox survival neural network model exhibited good discriminative ability for unseen data. Additionally, the model provided specific weights for 8 indicators (Supplemental Table 8). Total-NCPAV had the highest feature weight, and t the RCA plaque burden played a dominant role in predicting MACEs. On the basis of the feature weights, the test dataset was classified into low-, medium-, and high-risk groups (Fig. 4), providing more refined risk stratification. The risk prediction results for some samples in the test set are shown in Supplemental Table 9. Table 7 C-index of the Cox survival neural network model Model Type C index P- value 95%CI Cox Survival Neural Network Model Training Set 0.751 <0.001 0.674～0.810 Cox Survival Neural Network Model Test Set 0.730 <0.001 0.628～0.833 Total-NCPAV: total-noncalcified percent atheroma volume; RCA-NCPAV: right coronary artery-noncalcified percent atheroma volume; CT-FFR: computed tomography fractional flow reserve; RCA-CPAV: right coronary artery-calcified percent atheroma volume. Table 9 Examples of risk prediction results for partial samples in the test set Follow-Up Time(Months) MACE Predicted Risk Score Risk Stratification 13 1 0.042288002 Intermediate-Risk 3 1 0.757425785 High-Risk 18 0 -0.264241129 Low-Risk 10 1 0.742397368 High-Risk 18 0 -0.152615681 Low-Risk 18 0 0.117221415 Intermediate-Risk 1 1 -0.028739884 Intermediate-Risk DISCUSSION The present study revealed that AI-assisted measurements of plaque burden—CPV, NCPV, TPV, CPAV, NCPAV, and TPAV—were associated with the occurrence of MACEs. NCPV and NCPAV showed the strongest correlation with patient MACEs, while total-NCPV and total-NCPAV emerged as the most potent predictors of MACEs. Additionally, the present study provided optimal risk cutoff values for these indices. The plaque model, LASSO Cox model, and Cox survival neural network model all demonstrated good predictive performance for MACEs. Among these, the combined model (plaque + CT-FFR) showed the best predictive performance for MACEs. The plaque model provided incremental value to the CT-FFR model. The LASSO-Cox and Cox survival neural network models offered more specific coefficient weights for plaque burden across the three major branches, indicating that RCA plaque burden is the most critical factor in predicting MACEs. Furthermore, the Cox survival neural network model overcame the limitations of traditional models, providing more precise high-, medium-, and low-risk stratification for patients. Studies on the impact of coronary plaque burden on MACEs have made considerable progress [ 16 , 17 ] ; however, the research software component involves semiautomated segmentation or requires manual correction [ 12 ] . With the rapid advancement of AI [ 18 ] , the application of AI-QCT enables physicians to bypass postprocessing workstations and directly obtain the plaque burden of patients using their own computers. After adjusting for clinical risk factors, total-NCPV and total-NCPAV were the strongest predictors of MACEs. Abdelwahed et al. [ 19 ] demonstrated that noncalcified plaques are rich in fibrous and lipid components, which are critical factors in plaque rupture. Lipid components are present in 100% of the plaques in fibrous-capped ruptured acute coronary syndrome (ACS) patients. The coexistence of lipids and calcified components also leads to altered plaque stress and subsequent rupture. This conclusion aligns with the present findings, both indicating that noncalcified plaques strongly predict MACEs. The effects of calcification components on plaques are relatively complex [ 20 – 22 ] . In the present multivariate Cox analysis, total-CPV and total-CPAV demonstrated relatively lower MACE risks, with HRs of 3.40 and 3.47, respectively, indicating relatively low indicators. Punctate calcification is among the hallmark features of high-risk plaques [ 23 ] . As the number of calcified plaque components increases, plaque stress tends toward stability, thus reducing the risk of MACEs. This phenomenon may explain the findings of the present study—even with a relatively low calcified plaque burden, the MACE risk remained more than three times greater than that in the low calcification group. Although studies [ 17 , 24 , 25 ] have indicated that statins alter the plaque phenotype, reduce the lipid content, and promote plaque transformation toward calcification or fibrous components, thereby lowering the risk of MACEs, coronary anatomical stenosis may still persist, and patients may continue to exhibit coronary hemodynamic abnormalities. Therefore, patients with coronary plaques require early initiation of statin therapy to lower lipid levels and prevent plaque progression. At this stage, identifying the risk cutoff point for plaque burden becomes particularly crucial. The HR for total-TPV and total-TPAV ranked second, which may also indicate an interactive relationship between calcified and noncalcified components. Compared with total-NCPV and total-NCPAV, incorporating calcification indicators slightly reduced the risk associated with total-TPV and total-TPAV. The risk cutoff point in the present study overlaps with the safe threshold for plaque burden as reported by Bär [ 26 ] . If the plaque burden of a patient does not exceed the optimal risk cutoff point, early pharmacological intervention may suffice, potentially eliminating the need for invasive interventional procedures and reducing unnecessary patient suffering. Further surgical treatment can be reserved when the condition of the patient progresses. Gall et al. [ 7 ] utilized the CAD-RADS score to investigate the effect of plaque composition on MACEs in patients with obstructive coronary artery disease; however, these researchers did not provide specific detailed parameters for plaque burden. In a multicenter study [ 27 ] examining sex differences in plaque burden, AI-Quantitative CT was employed, but the included metric was total coronary plaque burden without specific comparisons of plaque burden in the three major branches. Tummala et al. [ 28 ] investigated the impact of proximal plaque in the three major branches on MACEs. However, none of these studies provided data on the specific influence of plaque burden in these three major branches on MACEs. In the present study, a C-index model was constructed on the basis of Cox analysis, incorporating LAD-CPAV, LAD-NCPAV, LCX-CPAV, LCX-NCPAV, RCA-CPAV, and RCA-NCPAV. The percentage plaque volume demonstrated greater generalizability than the total plaque volume. When combined with the CT-FFR model, the C-index for the plaque model reached 0.744 ( P < 0.001), whereas that for the CF-FFR model was 0.593 ( P < 0.001). The combined model achieved 0.750 ( P 0.75 is considered excellent. These findings indicated that relying solely on functional indicators may lead to an underestimation of cardiovascular risk. Compared with single-dimensional approaches, the integrated assessment combining morphological (plaque parameters) and functional (CT-FFR) aspects provides a more comprehensive evaluation, avoiding the one-sidedness of “focusing only on plaque without considering ischemia” or “focusing only on ischemia without considering plaque.” The LASSO-Cox model established in the present study incorporated 10 indicators, ultimately identifying RCA-CPAV > 2.8%, RCA-NCPAV > 3.49%, and total-NCPAV as strong predictive factors, with contribution values of 0.55, 0.25, and 0.07, respectively, highlighting the significance of the right coronary artery in predicting MACEs. The C-statistic for the LASSO Cox model was 0.747 ( P < 0.001). Although the conventional plaque model and combined model had lower predictive values, the LASSO-Cox model demonstrated superior performance in predicting MACEs, Although it did not significantly improve upon the standard plaque model or the combined model, it yielded meaningful results when the specific weight coefficients for the three major branches were identified. The Cox survival neural network model sensitively captures nonlinear covariates and resists overfitting, overcoming the limitations of traditional Cox models that analyze only linear variables. In the present study, the training-to-test ratio for the Cox survival neural network was 7:3. The training set achieved a C-index of 0.751 ( P < 0.001) for MACEs, while the test set achieved a C-index of 0.730 ( P < 0.001) for MACEs, demonstrating the generalizability of the model. The weights of the total noncalcified plaque volume percentage and right coronary artery indicators were slightly different between the LASSO-Cox model and Cox survival neural network model. This stems from the differing core logic underlying the two model types. The LASSO-Cox model operates under the assumption of linear independence, where coefficients solely reflect the linear independent contribution of features to MACE risk. Additionally, the coefficients undergo compression because of L1 regularization. For example, a one-unit increase in the percentage of total noncalcified plaque volume increases the risk of MACEs by 0.07 times. By contrast, the Cox neural survival network is a nonlinear model. The feature importance in the Cox neural survival network integrates nonlinear effects and cross-indicator interactions. While the percentage of total noncalcified plaque volume represents its importance within the model, both the LASSO-Cox and Cox survival neural network models simultaneously emphasize the critical role of right coronary artery plaque burden in MACE risk. The Cox proportional hazards survival network model enabled more refined grouping by not only dividing patients in the test set into high- and low-risk cohorts but also identifying an intermediate-risk cohort. This allowed for more precise stratification of patients at potential risk for MACEs. Patients in the low-risk cohort exhibited extremely low MACE risk. Further, there were clear differences in MACE risk across the three groups, fully demonstrating the clinical value of the model. In a study of coronary plaque burden in CAD patients, Bax [ 29 ] first reported that the LAD artery has the highest coronary plaque burden, and Bax later reported that the LAD artery carries the highest risk of progressing to obstructive CAD [ 30 ] . Giesen et al. [ 31 ] reported that noncalcified plaque burden in the LAD artery is most closely associated with the fractional attenuation index (FAI) (odds ratio 1.22) and that noncalcified plaque is most strongly correlated with MACEs; this study indicates that plaque burden in the RCA is most closely associated with MACEs, which contradicts the present findings. However, a meta-analysis encompassing 17 studies [ 32 ] suggested that an elevated FAI around the RCA is most strongly associated with MACEs, with a risk ratio of 1.22 ( P = 0.001). The LAD artery showed no significant association, whereas the LCX artery demonstrated only a borderline association, supporting the present findings. Thus, the weaker impact of the LCX artery on MACEs may be less controversial. It remains debatable whether plaque burden or the FAI in the LAD artery and RCA constitute the strongest predictors of MACEs. However, the coronary plaque burden threshold determined by AI in the present study serves as a reference for clinicians. AI measurements of plaque burden are rapid, and the relevant AI software can be installed on the computers of all cardiac surgeons and cardiologists, offering both convenience and scalability. LIMITATIONS First, the present study was a single-center investigation. The data in the present study were divided into training and testing sets for simulation within the Cox neural survival network model, and the C-index for the testing set was acceptable, indicating some generalizability. However, the present findings have not yet been validated at other centers. Future collaboration with other centers could advance this research and validate its clinical value. Second, as the current AI cannot identify high-risk plaques, the present study focused solely on plaque burden parameters. Further validation will be conducted once AI capabilities are upgraded. CONCLUSIONS Overall, the present study provides risk cutoff values for plaque models. The combined model of plaque burden + CT-FFR demonstrates the best predictive performance for MACEs, offering early warning information to guide patient treatment strategies. Additionally, the LASSO-Cox model and Cox survival neural network model further provide the weights of three specific parameters, indicating that RCA plaque burden is the most important predictor of MACE risk. AI-assisted quantitative coronary plaque parameters combined with CT-FFR enhance risk assessment and early warning capabilities for MACEs. Abbreviations AUC Area under the curve ACS Acute coronary syndrome AI Artificial intelligence BMI Body mass index CAD Coronary artery disease CABG Coronary artery bypass grafting CCTA Coronary computed tomography angiography CT-FFR Computed tomography fractional flow reserve CPV Calcified plaque volume CPAV Calcified percent atheroma volume FAI Fractional attenuation index HDL‑C High-density lipoprotein cholesterol HR Hazard ratio HU Hounsfield unit KM Kaplan–Meier LAD Left anterior descending LCX Left circumflex LDL-C Low-density lipoprotein cholesterol MACEs Major adverse cardiovascular events NCPV Noncalcified plaque volume NCPAV Noncalcified percent atheroma volume RCA Right coronary artery ROC Receiver operating characteristic ROI Region of interest TPV Total plaque volume TPAV Total percent atheroma volume TG Triglyceride TC Total cholesterol Declarations Ethics approval and consent to participate The present study was approved by the Medical Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University (approval number: 20210226-134), and informed consent from the participants was waived. All procedures involving human participants were conducted in accordance with the ethical standards of the institutional research committee and with the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The present study was supported by the National Natural Science Foundation of China (Grant No. 82460346), the Natural Science Foundation of Xinjiang Uygur Autonomous Region (Grant No. 2025D01D39) and the Xinjiang Medical University Smart Healthcare Innovation Center Construction Project (Grant No. ZHYL-001). Authors' contributions The authors thank the relevant staff for their guidance, assistance, support and collaboration. (I) Conception and design: Yan Xing and Mengyuan BAO; (II) Funding acquisition: Yan Xing; (III) Collection and assembly of data: Xinwei Zhang; (IV) Data analysis and interpretation: Mengyuan BAO, Haicheng Qi, and Xinwei Zhang; (V) Manuscript writing: Xinwei Zhang, Mengyuan BAO; (VI) Methodological support: Yongshun Wu. (VII) Final approval of manuscript: All authors. Acknowledgements Not applicable Authors' information Xinwei Zhang 1* , Mengyuan Bao 1* , Yongshun Wu 1 , Haicheng Qi 1 ,Yan Xing 1,2 * These authors contributed equally to this work and share first authorship. 1 Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, No. 137 South Liyushan Road, Xinshi District, Urumqi, Xinjiang, P.R.China 2 Corresponding Author Corresponding Author Yan Xing Imaging Center, The First Affiliated Hospital of Xinjiang Medical University No. 137 South Liyushan Road, Xinshi District, Urumqi, Xinjiang, P.R.China Email: [email protected] Telephone:13579973586 References Antoniou V, Kapreli E, Davos CH, et al. Safety and long-term outcomes of remote cardiac rehabilitation in coronary heart disease patients: a systematic review. Digit Health. 2024;10:20552076241237661. 10.1177/20552076241237661 . Li Y, Zhang W, Hu Y, et al. Pericoronary adipose tissue radiomics features as imaging markers for coronary artery disease risk assessment: insights from gene expression analysis. Cardiovasc Diabetol. 2024;23(1):444. 10.1186/s12933-024-02530-6 . Smail MAM, Singh RB, Fedacko J, et al. Evolving Role of Coronary Computed Tomography Angiography (CCTA) in Quantifying Atherosclerotic Coronary Artery Disease: A Narrative Review. Diseases. 2025;13(10):343. 10.3390/diseases13100343 . Luo H, Zhu W, Fan RJ, et al. Evaluation of the clinical value of CCTA as the preferred screening method in patients with chronic coronary syndrome. BMC Cardiovasc Disord. 2025;25(1):130. 10.1186/s12872-025-04587-x . Bell JS, Weir-McCall J, Nicol E, et al. Plaque quantification from coronary computed tomography angiography in predicting cardiovascular events: A systematic review and meta-analysis. J Cardiovasc Comput Tomogr. 2025;19(4):423–32. 10.1016/j.jcct.2025.05.003 . Kjøller-Hansen L, Maehara A, Kelbæk H, et al. Impact of Lipidic Plaque on In-Stent and Stent Edge–Related Events After PCI in Myocardial Infarction: A PROSPECT II Substudy. Circ Cardiovasc Interv. 2024;17(10):e014215. 10.1161/CIRCINTERVENTIONS.124.014215 . Gall E, Pezel T, Toupin S, et al. Prognostic value of coronary plaque composition in symptomatic patients with obstructive coronary artery disease. Eur Radiol. 2025;35(7):3937–47. 10.1007/s00330-025-11353-2 . Dawson LP, Layland J. High-risk coronary plaque features: a narrative review. Cardiol Ther. 2022;11(3):319–35. 10.1007/s40119-022-00271-9 . Sakamoto A, Suwa K, Kawakami R, et al. Significance of Intra-plaque Hemorrhage for the Development of High-Risk Vulnerable Plaque: Current Understanding from Basic to Clinical Points of View. Int J Mol Sci. 2023;24(17):13298. 10.3390/ijms241713298 . Mortensen MB, Dzaye O, Steffensen FH, et al. Impact of plaque burden versus stenosis on ischemic events in patients with coronary atherosclerosis. J Am Coll Cardiol. 2020;76(24):2803–13. 10.1016/j.jacc.2020.10.021 . Budoff MJ, Kinninger A, Gransar H, et al. When Does a Calcium Score Equate to Secondary Prevention? Insights From the Multinational CONFIRM Registry. JACC Cardiovasc Imaging. 2023;16(9):1181–9. 10.1016/j.jcmg.2023.03.008 . Wu W, Sun XX, Pan Y, et al. Predictive value of change in percent calcified plaque burden based on serial coronary computed tomographic angiography for cardiovascular events. Quant Imaging Med Surg. 2025;15(4):3401–15. 10.21037/qims-24-1846 . Wu Y, Qi H, Zhang X, et al. Predictive value of CCTA-based pericoronary adipose tissue imaging for major adverse cardiovascular events. Acad Radiol. 2025;32(1):91–101. 10.1016/j.acra.2024.08.022 . Dahdal J, Jukema RA, Maaniitty T, et al. CCTA-Derived coronary plaque burden offers enhanced prognostic value over CAC scoring in suspected CAD patients. Eur Heart J Cardiovasc Imaging. 2025;26(6):945–54. 10.1093/ehjci/jeaf093 . Bar S, Maaniitty T, Nabeta T, et al. Artificial intelligence-based automated plaque quantification from CCTA to predict acute coronary syndrome on top of obstructive or ischemic coronary artery disease. Eur Heart J. 2024;45(Supplement1):ehae666–172. 10.1093/eurheartj/ehae666.172 . Vatsa N, Faaborg-Andersen C, DONG T, et al. Coronary atherosclerotic plaque burden assessment by computed tomography and its clinical implications. Circ Cardiovasc Imaging. 2024;17(8):e016443. 10.1161/CIRCIMAGING.123.016443 . Nicholls SJ, Kataoka Y, Nissen SE, et al. Effect of Evolocumab on Coronary Plaque Phenotype and Burden in Statin-Treated Patients Following Myocardial Infarction. JACC Cardiovasc Imaging. 2022;15(7):1308–21. 10.1016/j.jcmg.2022.03.002 . Nurmohamed NS, Min JK, Anthopolos R, et al. Atherosclerosis quantification and cardiovascular risk: the ISCHEMIA trial. Eur Heart J. 2024;45(36):3735–47. 10.1093/eurheartj/ehae471 . Abdelwahed YS, Nelles G, Frick C, et al. Coexistence of calcified- and lipid-containing plaque components and their association with incidental rupture points in acute coronary syndrome-causing culprit lesions: results from the prospective OPTICO-ACS study. Eur Heart J Cardiovasc Imaging. 2022;23(12):1598–605. 10.1093/ehjci/jeab247 . Onnis C, Virmani R, Kawai K, et al. Coronary Artery Calcification: Current Concepts and Clinical Implications. Circulation. 2024;149(3):251–66. 10.1161/CIRCULATIONAHA.123.065657 . Yong Y, Giovannucci J, Pang SN, et al. Coronary Artery Calcium Density and Risk of Cardiovascular Events: A Systematic Review and Meta-Analysis. JACC Cardiovasc Imaging. 2025;18(3):294–304. 10.1016/j.jcmg.2024.07.024 . Liu C, Yang F, Hu Y, et al. Relationship between coronary artery calcification and plaque vulnerability, a qualitative and quantitative optical coherence tomography study. Int J Cardiol. 2025;440:133707. 10.1016/j.ijcard.2025.133707 . Gallone G, Bellettini M, Gatti M, et al. Coronary plaque characteristics associated with major adverse cardiovascular events in atherosclerotic patients and lesions: a systematic review and meta-analysis. Cardiovasc Imaging. 2023;16(12):1584–604. 10.1016/j.jcmg.2023.08.006 . Patti G, Grisafi L, Azzolina D, et al. Modifications of coronary plaque phenotype on lipid-lowering therapies and risk of cardiovascular events: a systematic review and meta-regression analysis. Atherosclerosis. 2025;120433. 10.1016/j.atherosclerosis.2025.120433 . Park HB, Arsanjani R, Sung JM, et al. Impact of statins based on high-risk plaque features on coronary plaque progression in mild stenosis lesions: results from the PARADIGM study. Eur Heart J Cardiovasc Imaging. 2023;24(11):1536–43. 10.1093/ehjci/jead110 . Bär S, Knuuti J, Saraste A, et al. Derivation and validation of an artificial intelligence-based plaque burden safety cut-off for long-term acute coronary syndrome from coronary computed tomography angiography. Eur Heart J Cardiovasc Imaging. 2025;26(7):1163–73. 10.1093/ehjci/jeaf121 . Feuchtner GM, Lacaita PG, Bax JJ, et al. AI-Quantitative CT Coronary Plaque Features Associate With a Higher Relative Risk in Women: CONFIRM2 Registry. Circ Cardiovasc Imaging. 2025;18(6):e018235. 10.1161/CIRCIMAGING.125.018235 . Tummala R, Han D, Friedman J, et al. Association between plaque localization in proximal coronary segments and MACE outcomes in patients with mild CAC: results from the EISNER study. Am J Prev Cardiol. 2022;12:100423. 10.1016/j.ajpc.2022.100423 . Bax AM, Yoon YE, Gianni U, et al. Vessel-specific plaque features on coronary computed tomography angiography among patients of varying atherosclerotic cardiovascular disease risk. Eur Heart J Cardiovasc Imaging. 2022;23(9):1171–9. 10.1093/ehjci/jeac029 . Bax AM, Lin FY, van Rosendael AR, et al. Marked variation in atherosclerotic plaque progression between the major epicardial coronary arteries. Eur Heart J Cardiovasc Imaging. 2022;23(11):1482–91. 10.1093/ehjci/jeac044 . Giesen A, Mouselimis D, Weichsel L, et al. Pericoronary adipose tissue attenuation is associated with non-calcified plaque burden in patients with chronic coronary syndromes. J Cardiovasc Comput Tomogr. 2023;17(6):384–92. 10.1016/j.jcct.2023.08.008 . Tan N, Marwick TH, Dey D, et al. Association of Pericoronary Adipose Attenuation With Major Adverse Cardiovascular Events and High-Risk Plaque. JACC Cardiovasc Imaging. 2025;18(8):884–94. 10.1016/j.jcmg.2025.04.008 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 28 Apr, 2026 Reviews received at journal 27 Apr, 2026 Reviews received at journal 21 Apr, 2026 Reviews received at journal 21 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers invited by journal 17 Apr, 2026 Editor invited by journal 07 Apr, 2026 Editor assigned by journal 07 Apr, 2026 Submission checks completed at journal 07 Apr, 2026 First submitted to journal 04 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9321407","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627090639,"identity":"e5993f41-c2c8-4c57-a91e-ad2a84722c50","order_by":0,"name":"Xinwei Zhang","email":"","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinwei","middleName":"","lastName":"Zhang","suffix":""},{"id":627090640,"identity":"41b2bf66-340f-4652-8de2-1c859c469d88","order_by":1,"name":"Mengyuan Bao","email":"","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mengyuan","middleName":"","lastName":"Bao","suffix":""},{"id":627090641,"identity":"b3082caa-8c04-43f4-bd2d-701e81a70cc0","order_by":2,"name":"Yongshun Wu","email":"","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yongshun","middleName":"","lastName":"Wu","suffix":""},{"id":627090642,"identity":"669cfe1e-f11f-466c-b714-514a271acc9e","order_by":3,"name":"Haicheng Qi","email":"","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haicheng","middleName":"","lastName":"Qi","suffix":""},{"id":627090644,"identity":"9e24ca2a-b079-47f2-8f3f-6efb607a933c","order_by":4,"name":"Yan Xing","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIie3PPQrCMBiA4UggXYJdWxT1CJ8EHPUqFtfgLv6C0MkD6C2cimOKYJeIa8XF6qqDo5tpJqe0o2De5QshD0kQstl+tT5CDeKs9JpWyxLmUomQUISUvSjYrLkmqJBAcjx42W5a2Z6f8f3Fu3WCcHZLTUQOBxDIBMNFLeJooB5GGOMmInj7GoQHAhfe8eIIK0JJzUhODxCKUDjLnMxLkFTfMvH8Nc3Jvpj46YNBEApwKWdwjBJKcMFfqife8d/hbB46sn0dReOe6yyzu4m0hB77ry1sOJ7XXOgxKzhms9lsf90H+TJLsVA1pb4AAAAASUVORK5CYII=","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Xing","suffix":""}],"badges":[],"createdAt":"2026-04-04 15:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9321407/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9321407/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107870123,"identity":"db148b8e-0400-4370-b744-df5cde02029c","added_by":"auto","created_at":"2026-04-27 07:38:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":816130,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study population.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9321407/v1/557d5ead63a85ba6ba3b8286.png"},{"id":107839294,"identity":"5d11b200-0e32-4ae3-b4d2-271735111c1f","added_by":"auto","created_at":"2026-04-26 17:17:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":246016,"visible":true,"origin":"","legend":"\u003cp\u003ePlaque parameters and CT-FFR extraction process. (1A) Axial view showing a mixed plaque in the LAD artery of the patient. (1B) Plaque composition analysis of the patient; from right to left in the following order: blue for calcified components, orange for fibrous components, yellow for fibro-lipid components, and red for lipid components. (1C) Plaque composition analysis at a single axial view of the LAD artery. (2) Curved planar reformation (CPR) image of the LAD artery, clearly demonstrating the mixed plaque in the proximal segment of the LAD artery in its entirety. (3A) Three-dimensional color-rendered CT-FFR image of the proximal segment of the LAD artery. (3B) Analysis results revealing a CT-FFR value of 0.62 in the mid-segment of the LAD\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9321407/v1/32cd50dc54ff78727fd76416.png"},{"id":107870098,"identity":"fa330843-61f7-45ef-b005-13a53598bd6e","added_by":"auto","created_at":"2026-04-27 07:38:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":403611,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan‒Meier survival curves of plaque parameters by risk cutoff.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9321407/v1/6c2707ebb0a8812449e44094.png"},{"id":107839296,"identity":"4bc4bc21-c57e-4dba-9f6f-46de8e272f4f","added_by":"auto","created_at":"2026-04-26 17:17:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":178683,"visible":true,"origin":"","legend":"\u003cp\u003eThe training loss curve is shown on the left. The survival curves of risk groups according to the survival neural network are shown on the right.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9321407/v1/155daa4ace3d3ef21f0784ec.png"},{"id":107873021,"identity":"9137e5c8-6b88-4f81-9817-980ffa0c5ba3","added_by":"auto","created_at":"2026-04-27 08:01:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2305187,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9321407/v1/66f9082a-763d-4238-bbcf-3d043c944026.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application Value of AI-Assisted Quantitative Plaque Parameters Combined with CT-FFR in Predicting Major Adverse Cardiovascular Events","fulltext":[{"header":"Abstract image","content":"\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eCoronary artery disease (CAD) is a major disease affecting human health\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, and its incidence is increasing annually. Coronary computed tomography angiography (CCTA) has become the first-line imaging modality for intermediate- and low-risk patients with CAD\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Plaque burden is a quantitative indicator of the ratio of total plaque volume to vessel volume, and numerous studies have demonstrated an association between plaque burden and major adverse cardiovascular events (MACEs)\u003csup\u003e[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Coronary atherosclerotic plaques include lipid components, fibrous components, and calcified components, whereas low-attenuation plaques contain necrotic lipid cores or are complicated by intraplaque hemorrhage\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. As unstable components of plaques, these substances can induce plaque rupture and subsequent MACEs\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. An increase in total plaque volume can also cause coronary lumen stenosis, leading to myocardial ischemia and ultimately triggering MACEs\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Therefore, it is highly necessary for clinicians to implement early interventions in patients at risk of MACEs\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCurrently, the measurement of plaque burden largely relies on postprocessing software, and some strategies require the transfer of CCTA images to dedicated postprocessing workstations\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Manually delineating the plaque-corresponding region of interest (ROI) to obtain specific plaque parameters is a form of semiautomatic segmentation, and it requires the images to be processed on dedicated postprocessing workstations. We have utilized AI-powered software for fully automated segmentation of coronary artery plaques\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Following CCTA scanning, clinicians and radiologists can promptly determine the conditions of the patient. This software provides one-stop, fully automated delivery of valuable morphological features (calcified and noncalcified plaques) and generates key hemodynamic data, such as CT-FFR, thereby enabling more precise and individualized treatment for patients. Previous studies have extensively focused on the total plaque burden of the three major coronary artery branches in patients with CAD\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Limited research has been conducted on the predictive value of the plaque burden in the left anterior descending (LAD) artery, left circumflex (LCX) artery, and right coronary (RCA) artery for MACEs, and the specific weight of each of these three vessels in the prediction of MACEs remains to be elucidated.\u003c/p\u003e \u003cp\u003eThrough the synergistic innovation of technical tools, research perspectives and methodologies, the present study overcomes the semiautomated limitations of traditional plaque burden measurements that rely on postprocessing workstations. By adopting an AI-based fully automated segmentation software, we achieved integrated and efficient quantification of plaque morphological features (calcified and noncalcified components) and CT-FFR functional data in a synchronous manner, compensating for the deficiency of previous CAD studies focusing mostly on the total plaque burden of the three major coronary arteries.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient Population\u003c/h2\u003e \u003cp\u003eThe present study was approved by the Medical Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University (approval number: 20210226-134), and informed consent from the participants was waived. A total of 381 inpatients who underwent CCTA and were diagnosed with CAD at our institution from January 2022 to May 2022 were consecutively enrolled. After discharge, information regarding the occurrence of MACEs in these patients was obtained by reviewing outpatient and inpatient medical records, as well as conducting telephone follow-ups, with a maximum follow-up duration of 18 months. MACEs were defined as cardiac death, worsening of stable angina or progression to unstable angina, fatal or nonfatal myocardial infarction, and performance of revascularization procedures, such as coronary artery bypass grafting or PCI. The inclusion criteria were as follows: patients diagnosed with CAD via comprehensive clinical evaluation; the availability of complete clinically relevant data; no history of known acute coronary events, stent implantation, or coronary artery bypass grafting prior to CCTA examination; clear CCTA images with adequate contrast medium filling; assessable quality on both subjective and objective evaluations; and the absence of significant artifacts. The exclusion criteria were as follows: prior history of confirmed acute myocardial infarction, severe cardiac insufficiency with New York Heart Association (NYHA) functional class III\u0026ndash;IV, or comorbidity with nonischemic myocardial diseases, such as dilated cardiomyopathy, hypertrophic cardiomyopathy, and arrhythmogenic right ventricular cardiomyopathy; severe arrhythmias, such as severe atrial fibrillation and severe atrioventricular block; implantation of temporary or permanent cardiac pacemakers; heart valve replacement; postvalvuloplasty status; presence of malignant tumors, hematological diseases, or autoimmune diseases; presence of renal dysfunction or underlying renal diseases; contrast medium allergy; and loss to follow-up. Although 398 patients were initially enrolled, 12 patients with unclear images and incomplete clinical data were excluded, and 5 patients lost to follow-up due to unavailable telephone contact were also excluded. Thus, 381 eligible patients were enrolled in the final cohort. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the flowchart of the study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCCTA Acquisition\u003c/h3\u003e\n\u003cp\u003eAll CCTA examinations were performed using a Canon Aquilion ONE Genesis CT scanner (Canon Medical Systems, Japan). The anatomical scan range was set from 1.0 cm below the tracheal carina to 1.5 cm below the inferior border of the heart. The delay time for contrast-enhanced scanning was precisely determined using the bolus tracking technique. During the examination, a dual-syringe high-pressure injector was used to intravenously administer nonionic iodinated contrast medium, with an individualized injection protocol based on a standardized iodine delivery rate. The specific CCTA scan parameters were as follows: detector configuration, 320 \u0026times; 0.5 mm; X-ray tube rotation time, 0.275 s per rotation; image matrix, 512 \u0026times; 512; collimation, 128 \u0026times; 0.5 mm; reconstruction slice thickness, 0.5 mm; tube voltage, 120 kV; and tube current, 300 mAs. All CCTA imaging data were reconstructed according to optimal image quality criteria. After reconstruction, all the images underwent subjective and objective assessments, and only those meeting the predefined quality standards were included for subsequent analysis.\u003c/p\u003e\n\u003ch3\u003eImaging analysis\u003c/h3\u003e\n\u003cp\u003eAfter the patient underwent CCTA, the images were uploaded to AI platform software. The plaque volume (mm\u0026sup3;) of each coronary artery lesion was calculated and then aggregated to determine the total plaque volume of the patient. To account for variations in the coronary artery volume, plaque volume was normalized to the total vascular volume of each patient using the following formula: plaque volume (mm\u0026sup3;)/vascular volume (mm\u0026sup3;) \u0026times; 100%. This metric is termed the PAV. Patch types are classified using the Hounsfield unit (HU) range, where NCPV is defined as patches exhibiting any component at the pixel level with HU within the range of -30 HU to +\u0026thinsp;350 HU. CPVs are defined as patches with HU values\u0026thinsp;\u0026gt;\u0026thinsp;350. For CT-FFR, the lowest value among the three major branch measurements was selected for collection. To avoid including gray-zone data, CT-FFR values\u0026thinsp;\u0026lt;\u0026thinsp;0.75 were considered abnormal. The image analysis process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Patient data for total-TPV, total-TPAV, total-CPV, total-CPAV, total-NCPV, and total-NCPAV were collected. In addition to the total plaque volume of the three major branches, the following values were collected separately: LAD-TPV, LAD-TPAV, LAD-CPV, LAD-CPAV, LAD-NCPV, LAD-NCPAV, LCX-TPV, LCX-TPAV, LCX-CPV, LCX-CPAV, LCX-NCPV, LCX-NCPAV, RCA-TPV, RCA-TPAV, RCA-CPV, RCA-CPAV, RCA-NCPV, and RCA-NCPAV. In total, 24 parameters were evaluated.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAnalyses were performed using SPSS statistical software (version 26.0; IBM, USA), R, and Python. Count data are expressed as counts (%) and were compared between categorical variable groups using chi-square tests. Continuous data following a normal distribution are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x\u0026thinsp;\u0026plusmn;\u0026thinsp;s) and were compared between groups using t tests. Continuous data that did not follow a normal distribution are expressed as medians (P25 and P75) for nonnormally distributed data, with comparisons between groups performed using the Mann‒Whitney U test. A receiver operating characteristic (ROC) curve was used to identify the optimal risk cutoff value for plaque parameters in predicting MACEs. On the basis of this optimal cutoff, plaque parameters were treated as categorical data for univariate and multivariate Cox regression survival analyses. The results are expressed as hazard ratios (HRs). Kaplan‒Meier survival curves were plotted. Cox regression survival analysis was performed on CT-FFR data to preliminarily assess the ability of total plaque burden to predict MACEs. The diagnostic efficacy of the plaque model, CT-FFR model and plaque\u0026thinsp;+\u0026thinsp;CT-FFR combined model was subsequently calculated, with the results expressed as the C-index. Finally, R software was used to construct a LASSO Cox model for predicting MACEs, and Python was used to construct a Cox neural network model for MACE prediction. A two-tailed significance level of α\u0026thinsp;=\u0026thinsp;0.05 was used for all the statistical analyses, and a \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered clinically significant.\u003c/p\u003e \u003cp\u003eA LASSO Cox model was constructed to predict MACEs. Through L1 regularization, this model achieves variable sparsification and selection, eliminates redundant variables (e.g., indices with poor predictive power among branch parameters), and simplifies the model structure while preserving the inherent survival analysis properties of the traditional Cox model, thus endowing the model with high interpretability for its predictive results.\u003c/p\u003e \u003cp\u003eA Cox neural network model was constructed to predict MACEs. The network structure of this model was \"8 (input features) \u0026rarr; 32 (hidden layer 1) \u0026rarr; 16 (hidden layer 2) \u0026rarr; 1 (risk score output)\", with ReLU as the activation function and a dropout rate of 0.2 (to prevent overfitting). The model was trained on a CPU for 100 epochs, using Adam as the optimizer (with a learning rate of 1e-3 and an L2 regularization coefficient of 1e-5), and the loss function adopted Cox partial likelihood loss (which is suitable for the event-time association learning of survival data).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cb\u003e1. Clinical Characteristics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the general clinical information of the study population. A total of 381 patients were included in this study, among whom 67 developed MACEs. The MACE subgroup included 45 cases of percutaneous coronary intervention with stent implantation, 5 cases of coronary artery bypass grafting (CABG), 1 case of percutaneous transluminal coronary angioplasty, 2 cases of rehospitalization due to aggravated stable angina pectoris, 2 cases of rehospitalization due to aggravated unstable angina pectoris, 1 case of nonfatal myocardial infarction, 1 case of malignant arrhythmia, 5 cases of death from myocardial infarction, 1 case of death from heart failure, and 4 cases of death from other causes. The remaining 314 patients were categorized into the non-MACE group. No statistically significant differences were detected in the clinical indicators between the two groups, whereas statistically significant differences were detected in the CPV, NCPV, TPV, CPAV, NCPAV and TPAV between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical baseline characteristics of patients in the MACE group and non-MACE group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMACEs Group(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;67)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-MACEs Group(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;314)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et/Z/χ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.31\u0026thinsp;\u0026plusmn;\u0026thinsp;10.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.70\u0026thinsp;\u0026plusmn;\u0026thinsp;10.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.427\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49(73.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220(70.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.251\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m\u0026sup2;)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.04\u0026thinsp;\u0026plusmn;\u0026thinsp;4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.72\u0026thinsp;\u0026plusmn;\u0026thinsp;4.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.558\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehypertension (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(68.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e234(74.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.975\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(32.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109(34.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mg/dL)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.57(1.10,2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.45(1.03,2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.704\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mg/dL)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.333\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior use of statins (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(38.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144(45.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.112\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior use of anticoagulants (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(34.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99(31.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.199\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mg/dL)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.453\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mg/dL)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.656\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal-CPV(mm\u003csup\u003e3\u003c/sup\u003e)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134.21(30.13,264.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.73(6.12,148.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.972\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal-NCPV(mm\u003csup\u003e3\u003c/sup\u003e)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158.19(91.12,216.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.29(31.20,133.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.922\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal-TPV(mm\u003csup\u003e3\u003c/sup\u003e)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e297.07(171.07,504.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128.29(56.86,261.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.819\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal-CPAV(%)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.64(0.91,13.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38(0.25,5.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.209\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal-NCPAV(%)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.10(3.73,10.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.88(1.40,5.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.280\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal-TPAV(%)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.78(7.21,22.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.45(2.12,12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.105\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBMI: body mass index; TG: triglyceride; TC: total cholesterol; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; Total-CPV: total-calcified plaque volume; Total-NCPV: total-noncalcified plaque volume; Total-TPV: total-total plaque volume; Total-CPAV: total-calcified percent atheroma volume; Total-NCPAV: total-noncalcified percent atheroma volume; Total-TPAV: total-total percent atheroma volume; \u003csup\u003ea\u003c/sup\u003e is χ\u0026sup2;-value,, \u003csup\u003eb is\u003c/sup\u003e t-value, \u003csup\u003ec\u003c/sup\u003e is Z-value\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2. Cox Analysis of Total Plaque Burden Based on Risk Cutoff Values\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe risk cutoff values for total-CPV, total-NCPV and total-TPV were 68.78 mm\u0026sup3;, 108.81 mm\u0026sup3; and 206.78 mm\u0026sup3;, respectively; those for total-CPAV, total-NCPAV and total-TPAV were 4.54%, 4.68% and 11.11%, respectively. Compared with the low-risk group, the high-risk group had an HR of 3.121 for total-CPV (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Moreover, the HR for the high-risk group versus the low-risk group was 4.528 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) for total-NCPV, 4.402 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) for total-TPV, 3.175 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) for total-CPAV, 4.859 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) for total-NCPAV, and 4.003 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) for total-TPAV. Detailed data are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The Kaplan\u0026ndash;Meier (KM) survival curves are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. After adjusting for sex, age, diabetes mellitus status, TC level, TG level, LDL-C level, and history of statin use, further multivariate Cox regression analysis demonstrated that total-NCPV and total-NCPAV were the strongest predictors of MACEs, with hazard ratios (HRs) of 4.7 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and 5.0 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictive hazard ratios of plaque parameters above the risk cutoff value for MACEs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate Cox regression analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate Cox regression analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHazard ratios of plaque volume above the optimal risk cutoff\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal-CPV\u0026gt;68.78mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.121(1.874～5.198)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.400(1.989～5.814)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal-NCPV\u0026gt;108.81mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.528(2.637～7.775)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.752(2.708～8.340)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal-TPV\u0026gt;206.78mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.402(2.564～7.559)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.503(2.587～7.836)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHazard ratios of plaque volume percentage above the optimal risk cutoff\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal-CPAV\u0026gt;4.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.175(1.941～5.191)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.476(2.087～5.788)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal-NCPAV\u0026gt;4.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.859(2.829～8.344)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.073(2.930～8.786)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal-TPAV\u0026gt;11.11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.003(2.428～6.599)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.190(2.515～6.979)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTotal-CPV: total-calcified plaque volume; Total-NCPV: total-noncalcified plaque volume; Total-TPV: total-total plaque volume; Total-CPAV: total-calcified percent atheroma volume; Total-NCPAV: total-noncalcified percent atheroma volume; Total-TPAV: total-total percent atheroma volume.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3. Predicting MACEs with Plaque Parameters as Continuous Variables\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor each 1 mm\u0026sup3; increase in total-CPV, NCPV, and total-TPV, the HRs for MACEs was 1.002, 1.005, and 1.002, respectively. For each 1% increase in total-CPAV, total-NCPAV, and total-TPAV, the HRs were 1.049, 1.137, and 1.049, respectively. The detailed data are presented in Supplemental Table\u0026nbsp;3.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003ePerformance of plaque burden as a continuous variable for predicting MACEs\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlaque Parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted HR for gender, age, diabetes, TC, TG, LDL-C, and statin use history\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal-CPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.001\u0026ndash;1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal-NCPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.003\u0026ndash;1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal-TPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.001\u0026ndash;1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal-CPAV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.029\u0026ndash;1.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal-NCPAV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.097\u0026ndash;1.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal-TPAV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.034\u0026ndash;1.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTotal-CPV: total-calcified plaque volume; Total-NCPV: total-noncalcified plaque volume; Total-TPV: total-total plaque volume; Total-CPAV: total-calcified percent atheroma volume; Total-NCPAV: total-noncalcified percent atheroma volume; Total-TPAV: total-total percent atheroma volume.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4. Predictive Performance of the Combined Model of Plaque Parameters and CT-FFR for MACEs\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo further distinguish the predictive performance of total plaque parameters from that of plaque parameters in the LAD, LCX, and RCA, as well as to lay the foundation for the LASSO-Cox model and the Cox survival neural network model, Model 1 incorporated the following parameters: LAD-CPAV, LAD-NCPAV, LCX-CPAV, LCX-NCPAV, RCA-CPAV, and RCA-NCPAV (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eC-index of the plaque model, CT-FFR model, and combined model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlaque Parameters Model (Model 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.686\u0026ndash;0.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT-FFR Model (Model 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.527\u0026ndash;0.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined Model (Model 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.696\u0026ndash;0.804\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe 95% confidence intervals were estimated by the bootstrap method (1000 replicates)\u003c/p\u003e \u003cp\u003e \u003cb\u003e5. LASSO-Cox Model\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe data in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e indicate that total-NCPV and total-NCPAV were the strongest predictors of MACEs. Prior to constructing the LASSO Cox model, we performed separate univariate Cox regression analyses for TPV, TPAV, CPV, CPAV, NCPV and NCPAV in the LAD, LCX and RCA. The HR values of RCA-CPAV and RCA-NCPAV were relatively high, both exceeding 3. Therefore, a total of 10 indicators were initially incorporated into the LASSO Cox model. The demographic indicators included sex and age. The total plaque burden indicators included total-CPV and total-NCPAV. The vessel-specific plaque indicators included the LAD-NCPAV, RCA-CPAV, and RCA-NCPAV. The ROC validation threshold indicators included RCA-CPAV\u0026thinsp;\u0026gt;\u0026thinsp;2.8%, RCA-NCPAV\u0026thinsp;\u0026gt;\u0026thinsp;3.49%, and CT-FFR\u0026thinsp;\u0026lt;\u0026thinsp;0.76 (functional indicator). L1 regularization was applied to eliminate redundant variables, achieving variable sparsity and selection. Ultimately, six variables with independent predictive values for MACEs were retained (all the coefficients were positive, indicating a positive correlation with MACE risk). The C-index of the LASSO-Cox model for MACE prediction was 0.747 (Supplemental Table\u0026nbsp;5). All the variables were ranked by the absolute value of their coefficients, with larger coefficients indicating greater contributions to risk prediction (Supplemental Table\u0026nbsp;6). RCA-CPAV\u0026thinsp;\u0026gt;\u0026thinsp;2.8%, RCA-NCPAV, and RCA-NCPAV\u0026thinsp;\u0026gt;\u0026thinsp;3.49% were identified as stronger predictors of MACEs.\u003c/p\u003e \u003cdiv\u003e\u0026nbsp;\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eC-index of the LASSO-Cox Model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eModel Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eC-index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLASSO-Cox\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.674～0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 6\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eParameter weights of the LASSO Cox model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePredictive Variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCoefficient Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eVariable Type\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRCA-CPAV\u0026gt;2.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.552479871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eBinary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRCA-NCPAV\u0026gt;3.49%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.254248032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eBinary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotal-NCPAV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.076836587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCT-FFR\u0026lt;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.068289599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eBinary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRCA-CPAV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.003903487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLAD-NCPAV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.001658234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e6.\u003c/strong\u003e \u003cstrong\u003eCox Survival Neural Network Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the development of the Cox survival neural network model, the right coronary artery indicators were the strongest predictors of MACEs (Table 6). Total-NCPAV, a total plaque burden indicator, ranked third in terms of contribution value (Table\u0026nbsp;6) and was selected for inclusion. An interaction term of RCA-NCPAV × CT-FFR \u0026lt; 0.76 was constructed (a continuous × binary indicator, reflecting the morphological‒functional interaction effect). If RCA-NCPAV is 0 and CT-FFR is 1 (no ischemic risk), the value of the interaction indicator is 0, and if RCA-NCPAV is 5% and CT-FFR is 0 (positive for ischemia), the value of the interaction indicator is also 0. When both RCA-NCPAV and CT-FFR are positive, the value of the interaction indicator is equal to that of RCA-NCPAV. This indicator is an intensity quantification indicator under dual-positive conditions, which can directly capture the synergistic risk of “high morphological risk (abundant noncalcified plaques in the RCA) + functional ischemia (low CT-FFR)” and improve the clinical interpretability of the model. The baseline demographic indicators included sex and age, and the global plaque burden indicator was total-NCPAV. The vessel-specific plaque indicators included RCA-CPAV and RCA-NCPAV, while the ROC-validated threshold indicators were RCA-NCPAV \u0026gt; 3.49% and CT-FFR \u0026lt; 0.76. Moreover, the interaction-derived indicator was defined as RCA-NCPAV*CT-FFR \u0026lt; 0.76. For the Cox survival neural network model, the study dataset was partitioned into a training set and a test set at a ratio of 7:3. The model yielded C-indices of 0.751 in the training set (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) and 0.730 in the test set (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), indicating favorable predictive performance for MACEs (Table 7). Regarding the training curves, the ability of the model to predict MACEs gradually improved with increasing training epochs, suggesting that the Cox survival neural network model exhibited good discriminative ability for unseen data. Additionally, the model provided specific weights for 8 indicators (Supplemental Table 8). Total-NCPAV had the highest feature weight, and t the RCA plaque burden played a dominant role in predicting MACEs. On the basis of the feature weights, the test dataset was classified into low-, medium-, and high-risk groups (Fig. 4), providing more refined risk stratification. The risk prediction results for some samples in the test set are shown in Supplemental Table 9.\u003c/p\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 7\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eC-index of the Cox survival neural network model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eModel Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eC index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCox Survival Neural Network Model Training Set\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.674～0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCox Survival Neural Network Model Test Set\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.628～0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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i8VCJz8ZKcTyqrqBt9vtwjpZlulVXi19bvRrOp3qt9xL1efSNPrc9Ho9vcpeyLLs0T63phgOh7i4uMBsNit8RpvK3XA4zNd9rHsgNYf9+VeVgcViUVj+kGfbbDbD7e2tTi5JkgSHh4c6uWCbdTbR97OqF5+VRc8WwI1GIxhj4LoufN+HMabw0gX3vi4vL3XSk+p0OvkxAMB8Pi9dZLe3t4jjGEEQwBiDVqtVWP6aybkBgDiOC2UiTVN8//5dv+Ve5vO5TmocYwzCMAQApGmKq6srvcpe+PLli0560RaLBbIsyz8P+3NaV+6yLMuXx3G81cOVXpbZbJY/Fz3PK5WBfr+POI7hui6MMej3+4XlT6HT6eT35DrbrLMNuddXPQM8z8PPnz/1W161ZwvgNnmMh1GSJGtvmE/N8zx4nofBYMBq5UfQarUwm8108s421YLQ48myDOPxWCe/aIPBoLKcep4HrFofqnz+/Bm+7+tkeoVOT09LwZv48eMHJpOJTm68IAh0UsHJyYlOevX2LoDLsqxUYwXVtKaX62Y3rL4FS5XuYDCAs2q+0FWx8l67+clZNdVJM6/sTzePbkMC0W2ql6uOA6tzIvmwm54lKJS/q2ot1523Jql66K37PPQyCdp6vV4e1Durz12XAfu9cs6k6SLLsnx9YZerfQgO7b4tki85Nrv82OVBjs9+z6bypJfLfqW8Oo4D13UBAOPxGI5VZnu9Xr6Oo5pG2u02hsNh4XPQn79uStKeq9xPp1N4nldZq358fAzP82oD2uVyiY8fP+pkQJ0vfS4eei5p/4xGI6RpWll2Ly4uSjVv68qAfOb6eSZlw2ZvQ1/fwr62tmnStLe5riJjNBrppIJ+v48fP35UHqfcg+U+NxwO0ev1CuVeH6t9n9r2WPaOeWau6xrf9/O/gyAwYRgW1gFg4jg2xhgThmHhb8/zjOd5xhhj0jQ1AEwQBIW/7e3J+9M0zdPsbQAwAIzneSYIAuO6rgnD0IRhaFzXLbzH/ruKzpe9fhzHeT5lXb1+EAT5/+X98h5ZXz7CqmNdd972WRzHpbzqc73u89DnIgiCwmcuf9vs82/UNuTcSbmQv+V9urzZn+tT0+XZ9/28rEge5XzaefV9Pz9fcj7kJeQ9wi5/8resb+9X9mNfU/b77H3LcrkHyDZlO6bi85LrUtjrmmcu957nFe5nIgxDEwRB/lnoMhIEQWG5LvtSlmW5/P3Qc0n7S3+2ZvX56/Kl17PLAGqeZ1I27G251rNYlzP5G9a9xr7+7XWk7Mr9UN937WdvHb0tW901LXmX56Ict7G2J9ed/G2/1/67KZ49x1II7JcdhIRhWCqwsAqevmHahVA/yE3FA89UPLztD95exy4wdYXIZm9D1rcvEPsmvs1x2OtX3YjtdTadt31m3yzsl23d5yHnS5brm0HVudNlQJcd2b5dltI0Ld1gvS0C+8dUVZ6rbkZ22TAV50D/baelaZrvx6ZvirJffYPW+/Z9v3Cu9bnXAZnsR7arPwd5MJk9KPf6WEW4CuCM9YCxyd+6rMZxXLoX2efnoeeS9pcuC2b1eetnzqYygIrnmamoPHHVFzS7LFflRd8j9TrB6kuJXr/q+tD0tjR9jHEcF+4JermkyX3C9/3C+rI/O60J9qIJ1R7EIJ19xfX1NebzeaGqE6vqT6yaKGezWV5VmqZp4f33dXx8XPg7iiIcHh7meRgMBsCqP8I2+v0+fN/HfD6vbMLY5jjevXunk2ptOm9NoDuw2tZ9Hq1WC8YYdDodDIfDrZqvt/Xbb7/l/7+5uUGapoVzHEVR5Wf3q0nT5UO8f/8eAPDz509cX1+XttnpdOC6bmFgieu6lc2Httlshqurq7wJI4oivUrlNn7+/JmX37dv3+bpo9Eo7y/UhHJ/dnYGWN0CptNpbf+fb9++IYqiwvGkaZof70POJe23TqcDz/Py/m5ZlmG5XKLT6RTW26YM6OdZldvbW4xGo0J3inWkXN3d3elFAICvX7/mXSccqzvFYwxEOzk5QRRF+XV9eXlZalbWjo+P83vzcrnMu1Y5jpM/I+qOZV/tRQBnsx+QomqUqvQtk4BnMpnArEbvPBU9KtLsOApoNpvlfWC+fftWWPYUx7HuvDVN1Q1o3echfSKOjo4eZXRUHRkNpl9UTx403W4XpiI4f6h9L/fyYJa+cOfn5/jw4YNeLed5Xul4JIB76nNJz0sClSRJ8OXLl8rBC49VBiRwu76+frR7mMy0YL+qBvjsqt/vw3XdfIT7fb6ghWFYytumfnj7Zu8CuFarVQiKDg4OsFwuC+vAGkl4eHiIMAy3vkHb39x34bpuZdBV1cl0Hcmn7si863Fssum8NY2+sNZ9HovFAvP5HGaHALuqlmKTt2/fIk3T0s2jqedYk9rlTqeDg4MDpGla6oScpimOjo4KaZt0u10EQVA7ym4d+Zz0Z79YTd3x3OXe87ytahikFq7dbqPb7daWv3fv3lXWqMjxPORc0v6T+9dkMsH5+Xnl/ewxykCWZRgMBojjeOsAS+57dV8+Wq0Wvn79qpMf7Vo8PT3FeDzGYrHYaoTq9+/f8+C23W7j+vq6sDxr4HyVexfAaTIax/7QkyTBwcFBXoCk2jPLskLzlV3Fq4Otm5sbYHXjj6IIURStnQh1MplgPB4XHmCXl5eVNYab6Ca2TcdxH+vO20uw7vOwzyMqHvbSFJ1lWeH82A9KSR8MBrVButSkdLvdQroO6JrEvoENBoO8aW80GsF13UJz9HQ6heu6lQ8VmzSzyg0yVfP5VQUo6wRBUPrsr6+v0Wq1nr3cHx8fVwaQmpSdNE3x6dMnvTgnNQ32vckuXw89l7T/giBAFEWl+4x4jDIgTerypU1/URP2vKrdbhe+79d++fj06ROiKCrcUxaLxc5f+OpI4DgYDCrvQVJzCWtKMfnidHZ2Vpqj9cuXL3m3kcbQneJ+Fekgbb/qOiya3+tz85fdOVE6TsMabSX/t5fXvUc6gHqel3eytJfZpCO3vOo6PNrbr1vP7tRs1hyHfgVBUNq+dMCUl51vO1136txHelDLugEB6z4PfT7k/3LON71Hti2fk71Ml1P9Wf0q9j6x+nx1XqrKtC4/aZrmx2gfa1VnY/vzscuT3q9NtimfpT6fOj/29us+Y/0e/Zno7fxKUIME9HHJsliNKFxX9u1ldvpjnEvab3pQlrZtGdj0XLCvYW/V4d9eru8ldvmpK1v6PfqZWmXddaDJ/UyTe6F9TPr86edm1f1u3znm9w+TiF6x6XSK8Xj8aH1fXrPFYoGLi4tH6w5BRNWkBk3XwEmN9Uu/Bve+CZWIqEn6/T6Oj49rJ0IlosdRNanxa8IAjojokY1GI1xcXDxah20iKkqSpLb/3WvBJlSiV244HBZ+MzhN01d/YySi/SRz1HmeV9lE2m6380GArus+aHTuvmMAR0RERNQwbEIlIiIiahgGcEREREQNwwCOiIiIqGEYwBERERE1DAM4IiIiooZhAEdERETUMAzgiIiIiBqGARy9eMPhcK9/1shxnPyVJIle/GDtdvtJfxHAcRxMp1Od/KIsFgs4joMsy/SiWs9xXp76syai/cEAjvZKr9crBDTyo8RiOp3uFPDoXxnYN47jIE1TGGPgeR5OTk70Khu12+3aQMGelfwpyKzou5pOpzsFQ+voMtHr9ZBlWSHNLit62aYyhNXvmxpjtv6Fim3Py2MHW7e3t5jNZjqZiF4gBnC0V66uriA/DuL7fumnUkajEcIwhOd5MMag0+kUlmuz2Qy+7+vkvbBYLAAgDwqurq52/tmXxWKBNE1xfn6uFwGrB7rrujr50dz3h1zq8nsfo9GoVGZarVYeFGOVTykrssx1XaRpurEM3cc25yXLMiyXS51MRLQVBnC0l3zfr62hubu7w9nZmU5unLu7O520s4uLCwRBgDRN84Bw302n0yepFQyCoBQQSTnR52axWKDb7W5do/YUHrv2jYheFwZwtJc+ffqEKIoqm7e+fv1aqjWxm8TW9XcbDof5ekL+1g/UdrudL9MBwDbsfTmq/5TjOBiPx/n/t21ys8m5GY1GwCqYW8duatRNrpKeJEnpPOhm7XXknEnTd5IkhXM4HA7z43Zdt/BZ2edL52Eb79+/R5qmhTLz5s0boOLcXF9f4+PHj4U0+/zopvuqfpT2sellNuk/J+cAq/MURRHSNC0cr+Sh1+shSZKdyp00DdvvkT5xdl71Z09EDWWI9pTnecbzvEJaGIYmCIJCmuu6xvd9Y4wxcRwbACYMw3y57/vGdd387yAIjC769jbM7+1fJo5jY1b7tP/ehu/7hbz7vl/aRlU+duH7fr492X6apno147quAZCfN9mvnCPP8/LteJ5XOFee5xXOi+d5pf3Y25Z17GNP07SwPzmf9jY8z8u3Ievrz3kb+nMMwzA/fnt/9jGa1fnTn7/8LefWfo+UM33+7XzLe+zt2NvQf8dxnJ+3qnK8jpwz+z1y3K7r5tt9aJkjov3BGjjaWycnJ4iiqFBzdXFxgQ8fPhTWA4CDgwMAyGvmHtI8uVgs4Pt+vq1+vw8AuLy8VGtWy7IM8/m80MwrHcsnk4m15v1J/ynJ46dPnwAAX758UWv+zvf9vKZuNBrB87xCrdSPHz+AVT88qU1KkgRRFOXbhnUcnz9/ztMeKssy3N7e5vlrtVrwPO9e/eROT08Lg1YuLi7y47y5uQFWx3V6epqvAwDz+bzQ+d/3/Xw7Vf0ov337Btd1S+c/juP8OACg2+3m2z06OtrYdCx9IDudDsIw1ItrtVqt0ral/2O73c77kr5//x5YnXMiajYGcLS3+v0+XNfNgwVpGtP9luThL01VD3V9fY35fJ43Ock2t33oSaAgzXfC9/2dBynU+fLlSyEY3DXoabVaeV6Oj48xGAzy5jd52H/79i1f136f53lbn4tt3Nzc5E2J8pLmxV1JgGIPEOl0OnBdNw/kLi8vC18CpFzZ+5fgbd1x2vmTcySBsJAvFra6bXY6nfw8tNtt9Pv9/MvDQ+jrBQB+/vypk4ioYRjA0V6TGpUsy3B5eVk5eEECt+vr661G/23D930YYwovPSL2OY3H4zzo0kHPLv2msKqRk2BkMBis7c/1VFzXLZ3v+3yWdrC2WCxwdHQErGrCoigCVgFUVVCj923WTBsitWxyruXft2/fFtbblTEGvu/ngdyunyURvR4M4GivSU3J58+fC02GIssyDAYDxHG89fxX796900kFBwcHpdGM2GHUoDzEdXNmlmXodruFtPuYTqcIgqAUbEjAozvsV8myLG9GHA6H+dQaaZrmQaCcJx1E3N7e4vj4uJBmqwt66rx9+xZpmpZqprY939rp6SmiKMLFxUVegyUDFobDYSnvUlOqB8xsmqsuCII8iB4MBgjDsFQ+dyGDFmazWR7IVX1hISICAzjad61WK++PpPstwWoKkqYr/RBeR9aVaS3m8zmGw2FeI2UHEEmSVDaHVel0OvB9H+PxON9HVX+y+8iyDOPxuNDPyhYEAaIoKgVddr+w6XSKKIrybWRZlh+rBF9v375Fv9+H53kYDAZ5ILNYzTtXt38htV2wArHBYIDFYpEHuD9//sR0OsWbN2/geV4puF0XPK0jzag2qZmbz+elPpRSxg4PDwvpX79+rQ1GF4sFvn//Xgied23uPDg4yGs+7XMkx31wcPAstaFE1BB6VAPRvpEReVUjLI01MhJAPopS/m8vs4u7nR4EQWn0ovm9Oquw3V3ZIxP1pWanb7t9Gb0pLz1CUe9Pjyat25fv+4Vt69Gf9nvtbdojH2GN2jTq+GTbdn71yFij9oMH3ppc1608P/rYbfr8SXmrypeUSf3yPK/yvMjoT3nFcVxYL45jE8dxYdSs7GsbOj96f57nbSw/RNQsjpF2FyIi2kqWZbi5uSnVuknNp04nInpsDOCIiHbUbrcrRxRLk/lD+sIREW2DfeCIiHaQZVmpj6SkTyYTBm9E9EuwBo6I6B5kfkDhum5lrRwR0VNgAEdERETUMGxCJSIiImoYBnBEREREDcMAjoiIiKhhGMARERERNQwDOCIiIqKGYQBHRERE1DAM4GjvZVkGx3FKP9D+K7Tb7dKEra/FdDrFdDrVyUREtAcYwNFekqApyzK4rqsX35sEg+te8nNIAHB7e4vZbFbYxq7WBYB633py2Oc0Go3w/ft39Ho9vYiIiJ4ZAzjaO0mSIMsyAECr1UKapnqVe2u1WjDGIAgCAIAxpvAKwxA/fvzQb7s3+1iqyD4BIE1T7Nu82rPZDK1Wa20QSkREvx4DONo7JycnOumX6ff76Pf7OvnenvNYHstsNsN8Pi/UTBIR0fNiAEd7xXEcpGmKKIrgOE6pD9Z0Oq1s6tTL7tPsp/dV1fdO8iT7kmV2nqS2atOx3Ifd1NputwEAi8WikC41fsPhsHQuJM1xnEKtWpIkef57vV5hOwDg+z4uLy/zv4mI6HkxgKO9YoyB53nwPA/GGIxGo3zZ2dlZYZ3JZJIvGw6H+P79e94UGkXRTs1+WZbh69evhb913ztn1T9N1pPlvV4PcRzDGIPJZILlcglsOJb7aLfb8H0fxhjEcYw0TbFYLNDv9/Nm2DiO0Wq1gFXNme/7uLq6Alb5PDg4gDEGaZpiPp9jOp0iSRIcHh4CACaTSWWt4dHREebzuU4mIqJnwgCOGuP09DQPgo6Pj3F7e5svm8/nhcEGvu9vFXBIbZQO1qr63tn900ajEW5vb/PmVuk3d3V1ldeMPYWDgwMAQKfTAQDc3d0Bq6ZfHdQmSYKjoyNgFZDe3t7m56/VasHzPJyfn6PT6SCOY2B1Xvv9PowxeSAIAG/fvs23Q0REz48BHDXGu3fvCn9LgCVNqXYzogRvmwIOqbHbZfDA8fFx6e/BYABn1aQqNV6PTQIwaTLVTk5OEEVRfsyXl5d5gHlzc4M0TQvnKIqiUpD6/v37wt9ERLSfGMDRi2EHY/Kya5E20YHZtkajUR4IDQaDR62By7Is72cngdv19XVlwNnv9+G6Lr58+QJUBK+u65bOT9V2iIho/zGAo8Z78+YNYNXEiel0Wgpi1rlvH7XhcJhPT5Kmad437TF8/vwZv/32G7Isw2AwQBzHa+elOz09xXg8xmKxKPRle/v2LdI0LZ2PbfsJShPxLgExERE9HQZwtHdarVbev22bAKPVasH3/bwjvvj69esvCTiyLMvzKfuTPmO7HMvPnz8Lfy8WC8znc7RarXyZBFI6WBUfPnwAVjWB9nQonU4Hnueh2+1aa5dr6epcX1/D932dTEREz8UQ7Zk4jg0AA8CEYZj/H4AJgsD4vl9IS9PUGGNq021pmhbWAWB839erFfIAwIzH49r3+L5fyGcQBJXbqcqPzot+eZ6Xr+t5XiHddd3SOmaVn6pjMmobWF3++hyHYajfZgCYOI51MhERPRPHsBMM0YsizbePNSHxdDrF9+/f1zbdEhHRr8UAjuiF6fV6jzYSdjgcIsuyR9seERE9DvaBI3pBkiR5tH5/0+kUBwcHDN6IiPYQa+CIXgCZF87zPAZcRESvAAM4IiIiooZhEyoRERFRwzCAIyIiImoYBnBEREREDcMAjoiIiKhhGMARERERNQwDOCIiIqKGYQBHRERE1DAM4IiIiIgahgEcERERUcMwgCMiIiJqGAZwRERERA3DAI6IiIioYRjAERERETWMY4wxjuPodCIiIiLaU44xxuhEIiIiItpfbEIlIiIiahgGcEREREQNwwCOiIiIqGEYwBERERE1DAM4IiIiooZhAEdERETUMAzgiIiIiBqGARwRERFRw7zoAC7LMjiOg8VioRcRERERNdaTBnASQG167WI4HOqkSlmWwXVdnVzS6/UKecmyrLB8sVgUluv9T6fTwvJerwestgugdKzbHvd0OkW73dbJQEWeHJVvfd51nmk/2J9T3WdtS5Kk8LlOp1O9CmCVySRJ9KJS2ZByqg2Hw1K5qtJutyvzL+ny0vR1V0WXc51XyWPV/omIXjzzhNI0Na7r5n+HYWgAmDRN8zTXdQt/rxPHsfE8TyfXStPUADBhGOpFJUEQGACF/Nqq9gvA6FMYx3FpO/q4JV/6XNhkeRzHelHO9/3S/m3rtk/PS8qAfL5BENSWPbNa3y6DUqaCICis57ru2rKj3+N5nvF9v7TOpvIp+6+6LjzPK6TrY/N9v5A3ybO27t7g+35hH77vrz1/REQvzZPWwAHAZDLRSQWnp6c6qdbJyYlOelRBECBN09I3/Srtdhuu60L/lGyn00GapkjTtJBua7VaCMMQAPD582e9GIvFAkEQABvO38ePH4FVrYqWZRl830er1dKLaA98/vwZvu+j0+kAAEajEdI0rW3uv7m5wdXVVf53v9+H53n4+vVrYb3b21vEcVxIE1Ij9/79+zzt+Pi4VH6MMXn5rLJYLDAYDBCGYSFPIooiHB8f53+/f/++cD0cHR3lxw0AFxcXgJU/rPbR7XYry2+WZZjP5zg7O8vTZrPZ2vNHRPTSPGkA12q10O/3dXLBaDQq3KTrmkwcx0GapoiiCI7VfKSbMB9yA3///j2CIEAURbXNU1g9XNI0rQ0+W60WfN/XyQVv374FaoKvs7MzfPjwAb7vI4qiynWwChY9z6sMAr98+ZIHeLR/5vM5jo6OCmm+7+P6+rqQJqquo1arVRng1JGgyf4idH5+jtlsZq21XpZlGAwGCIKgMk8A4HkexuNxXm4vLy8LAaF+35s3bwr/YnUNzOdzOBXNuD9//gTU+ljtt+78ERG9NE8awO3KcRyEYQhjDIwxiKIo79tijIHnefA8D8YYjEYjLBYLjMdjpGmaL7e/ld/HaDSC7/sYj8e1waA8JOyaDG3TQ/HHjx/A6iFsS5Ikr3mQAOzLly+FdWwnJyeYz+elh9z5+XmhloP2h3xWEsTblsulTqq1XC53DtKldli+8CyXy1IZXEe+LLx79662/9nV1RVc14XrunAcB0dHR6WgzXZzcwPP8/J8ZFmG5XIJYwx834frupX9OG9ubnRS6TogInqp9iaAm06ncF23cKMPw3CrZhG58R8fH69tutzWbDaD67oYDAaVDwRJ0zUAuxgMBgCAT58+FdInk0n+UJYatvPz88I6NjlfdpC3WCxqawfpZZBAf9cgvdVq5c3zqAmC1lkul3BdF9fX1zDG5AGh7nYgzaKwvvDUubi4KHzhsWsWpWl0Pp/n94FOp5Nfn7YoinYKRomImmxvArivX7+WvslLcHJ3d1dIF/1+P++D1m63MR6P9Sr3JjUh24xk3ZbUSDirUXdpmhYeOBIY2g/lk5MTpGlaOaJQBEFQOHZpgqWXazKZbKzlrSI1WVK7NRgMNn5B0k5PT/N9S0BoN/UvFgtMJpO8L918Pq+sQcNq3ZOTk7WBl+zDDgRvb28B1eUCq/51RLSdjFNtNdreBHD3JdMVLJfLQs1CFd1fTgeMtlarlXcG17UL0kF719oLaeqVl35off78Oe/jJy+pZVg3mEGCtel0WmiCpf0kn400o4ssy9DtdgtpVXq93r2CtyRJMJ/PMRqNgFXtlu/7D+528O7du8Lfg8Eg32a/38+DOF2bnSQJrq+v1zavCr0PrIJQeQVBUKrBJ2oK+57vrKYAWiwWjx5Y6S9SrVYLxhheNw21NwFcq9UqddiX/9f1NRsOh2i325XBUJXRaFS46cu3+DqdTievXYiiKE+XB+C6B58EU9uy+/3o16bBDK1WK+84fnl5uXO/KPr1qgYsRFG0sQZpOBzi7Oxsq/Ku6YARq5HMu3Q76Ha7lU36ruui1WpVllF5OMjgA6zK+y61iHd3d7XnRvrC2s22RE0glQpBEBTu+ZPJpNRF4DHM53OdRE2m5xV5Sno+NA1q/rR1cz3JMnt9z/MK80ntOg9c1bxZpma+NZnvTaebVT7subY2HbdZvacun3IcVXNuiar552h/Vc0Dt+7zNatyqMtIGIalNCkLujzLPu1533zfL80DZ9aUWdmGXb71Nea6bmkeRPvvVM0PKeqOPwzD2mVyzetjJdp3co3p61f4vl+a5/Ehqp5j1Gy/7NOUYEdedTdkex39YLGDpjRNC3/L+vL/P/3pT4VldReCPJDkVfVgMauHUhV5gNgv+2Gil1Ud97rl+hj19m2u69YeJ+0f+7PVn7vc3OUaqCpnqCivej19DenyrpcbNRkwKq4dvY2qB5C9DTuPVeVZb0eOXV46j/b+9Xkjagq5RuqkaVq49vS1o69LSbOvH7mm9H1Brhv9Rc2u9JDJ7WE9cyTP8n47T/Z9YF1eJU2uazu/sh/7Gk/TtHQPoN/Vlx4iIiJ6dNu0qtgkIJJAS/6WwEaCHdd18zRf/TqJBGTCDpzSNC0ETXZlgKd+WUX/bQd9Zou8mlUgaP8t69iBorzfdd2tz9Nrszd94IiIiF4D6Q+6bV/WyWRS+GWdTqcD3/fzwUG/x3C/91GVfqVHR0dr+7fKACPRarXy9U9PT/O+3sfHxxv7i9s25XVbco522fdrwwCOiIhoj0VRhIODg0KaDFazBwfpdXDPya31qO91gaC2bV7X6Xa7ODw8zEfkVv1kHzGAIyIi+qVkrs9dfnnlNZnNZvk0XoeHh6WpvOh3DOCIiIh+sSAINv7SkAQuruuWpu+RaYF2/TWWh9rU7LtNXtfNwYrVdEmdTgfGGMRxjCiKdpqW67VgAEdERPSLjUYjeJ6HwWCA6XRaWJYkCRzHyZsOLy4ukKZpYSLes7OzjZPX26RZNMuy0oS+u7LnRZVtya+6bJtXu/bx5OQEWNW2JUmC5XKZnxP5ycqH/HTli6VHNRAREdGvoafcQM3oVL2ejPrU0/rI/HF2mozutN+r16maesuemgvWyFI7rWo+u7q8Cp1n2YbkUx+Dfj/9zjEyfIWIiIiIGoFNqEREREQNwwCOiIiIqGEYwBERERE1DAM4IiIiooZhAEdERETUMAzgiIiIiBqGARwRERFRwzCAIyIiImoYBnBEREREDcMAjoiIiKhhGMARERERNQwDOCIiIqKGYQBHRERE1DAM4IiIiIgahgEcERERUcMwgCMiIiJqGAZwRERERA3DAI6IiIioYRjAERERETUMAzgiIiKihmEAR0RERNQwDOCIiIiIGoYBHBEREVHDMIAjIiIiahgGcEREREQNwwCOiIiIqGEYwBERERE1DAM4IiIiooZhAEdERETUMAzgiIiIiBqGARwRERFRwzCAIyIiImqYJw3gsiyD4zi1r8Viod+Sq3pvlmWYTqdIkkSvDgBYLBZwHEcn10qSpLD94XCoV0G73S7lwabzmCQJFosFFosFptNpabm8ptNpYTtau92uXUfnSed7OByuzTPtL/tz21av16tdX5fxumsH1ro2uab0q9frFdYTdtmzy52d3m63C+8RdrnWZVaXaXnVXSNERC+e+QWCIDB6V57nGQAmCIJCujHG+L5vAJgwDAvprusaACaO40K6kOX6fdsAUPveOI5L+ZRj0ulyXPZ2XNc1vu/nf8vx2Wm2MAwNAOO6rl6US9O0cv8iCILa7dN+ssuNlIF1pAzIS4vj2AAwaZoW/q67fqq2U1WGfN8vXSd2XjTf943neYW/ddl2XTcvyzrfaZpWlnPXdfN1iIhem/Ld9glUBXBm9cDQN3JZt+4h43le6eFhVjd9eTDobW7D87w8+NL71gGcPFyr8mFWDyh7fR3ASZr9kLJ5npefh7p9mNV6ervC9/3KbdN+CoKgEOQYFdSsU3d9VZUPHUwJu/yLNE0ry5DelwRvel/2Mn1N2WU7DMPSNWvnXb9X0qqOg4jotXjSJtR1pIlEN6eMx2O4rotOp1NIF2dnZzoJADCZTPDx40ecnp4iTdO1TUV1rq6uAACHh4d6UYHkod/v60UAgE+fPumkEjnunz9/FtIl36PRCABwcXFRWG47OzvDfD4vNTdhdX5brZZOpj11fn6O4+PjQtrp6Sm+fv1aSNvF7e0tDg4OCmlHR0eIoqiQtlgscHx8XNp/q9UqlaEkSeB5XiGt2+3C8zzMZrNCOqzy/ebNm0K653m4vr4GVmW82+0Wlp+cnGC5XAJA5b3g27dvpfwSEb0mzxbAua4LqIBMghd9M7d1Op1S4CQBTKfTwYcPHwAAl5eXhXW2laYpUBFYiizLkKZp6SFma7VaeQBW5/b2Fqh4sF1eXubnxPd9RFFUGaBhdbyu6+Lz58+F9Ol0ipOTk0Ia7bc0TfHu3TudXAq2dlUXAEqZyrIMFxcXG8uruLy8LJStxWKBNE1xcnJS6Jumy+zNzU3hb1h5qAo0sTonejvi/Pw8v9aJiF6jXxrA2Tf4MAxhjCl8u/7x4wcAVN7M1/n8+XP+UGm1WvB9H/P5XK+2lVarhTAMkaZpaXAArBoFXTOxi+l0ijRN4ft+YTtZlmG5XObnRGryvnz5kq+jnZ6elo71/Py8FOTS63N6eoooigqDhaTWS8rdcDjMa563MZ/PC2XLrkVbdcmA67r5FzT5kjEYDPL3YBWY3vcaSpIE7Xb73u8nInoJfmkAZ4zJa7jkxv8Ylstl4aHy8eNHYFU7cB/9fj8PAh9rlNt8Ps+D1/F4DN/3S01OX758wWQyyf9utVrwPA/n5+eF9WxScyL5XCwWa2sw6fUYjUbwfR+DwSAve/P5PK89nk6ntV0SqiwWC/i+r5PheV4hCJRmf7n+pLbZ/gKHVXPufehaQCK6n2w128N9n5X0zHSnuKegO1nXDQKQDs+7dE6WbVe9dMdovdzef9U+pVN3EASFzuRV216nahBDFZ2/urxq/mpUq1nluarjOe23qgELdQMONH191dEDCmQgTdVL58XUjD6tyqPsR68rgiAoXD9Vgy30OjaOPqWXRl9/cRybMAxrr6H70tcZNdvmu/4jqHrASNChb8SSXjXyzKweDnYh1NsVss+67Wj6ISSqHmjbjhAV2wRwOki0YUNQK9MuVD1MqRmCJxiFqkkZqbNpO1XL5MuYfR1LAKevbWOtb1+Xm0ah2jj6lF4Sueb0dS6VB+ueMfdRdQ1Tc/2ST7PuwSA1ADrIknRdePWN3lfTdWjYoaas7qEgDyO9H7t2zibBlG1TACf7qLNtwLhpHdpv9ueny/o6ddeXkKBpXRk0G7YThmHt+13XLVw/dcGXlFF9vZuaeeCqVNUCEjWRXJd15XnT821XUjlCL8eTfpoSmNgv+8aul+tv5eveazf/6Aed3i42XCSb1gvDsPJCkgeN/bIfZPJAtF/64aWPU+9f508fq5DtUHPZ5VZ/zrJMp+vyZZcfCZhQUxumrQvgPM8rlV2bfT3WXeN1X5KEfRx11i0jahK5ZuqkagJr/bzRzyRJs58pcj+w7wX2tSjryv1Brld55sn6utuFvN/Ok33vWZdXSZP7hJ1f2Y9930hVqxv9i/rSQ0RERI9OApRNX2qEBEQSaMnfEthIsGO39vjqF0/0FzQ7cEpXk3bb25Ggy1tN8i3033bQZ7bIq6lolZJ17EBR3q9r+Olf/NJRqERERK/drtNRTSaTwrRTnU4nnykhyzL8HsP9PoeqzG5wdHSUz/pQpd/vIwzD/O9Wq5Wvf3p6ms9wcHx8nI8k38amvG5LztEu+35tGMARERHtsSiKSvOjynRZ9q/56HVgTZi9Cz2p+LpAUNs2r+t0u10cHh7CcRwkSbLTXJWvCQM4IiKiX0gma5efi6Oi2WyGOI6B1U9b9no9vQoxgCMiIvr1giBAmqZrJ9GVwMV13dKE7vLLRVW/FfyUNjX7bpPXup+qFMPhEJ1OB8YYxHGMKIru9fvmLx0DOCIiol9sNBrB8zwMBoPSL/4kSQLHcfKmw4uLi9LPO56dnSEIAutd60mzaJZllT8TuQv7N5plW4PBAIvFYuu82rWP8ssqh4eHSJIEy+UyPyfye+H6d8PJHpJCREREv5SecgM1o1P1ejLq0x49KqM99RRWMrrTfq9e509/+lPh7yAIStNYychQO01Gs66bRkRPj6XzLNuQfOpj0O+n3zlGhq8QERERUSOwCZWIiIioYRjAERERETUMAzgiIiKihmEAR0RERNQwDOCIiIiIGoYBHBEREVHDMIAjIiIiahgGcEREREQNwwCOiIiIqGEYwBERERE1DAM4IiIiooZhAEdERETUMAzgiIiIiBqGARwRERFRwzCAIyIiImoYBnBEREREDcMAjoiIiKhhGMARERERNQwDOCIiIqKGYQBHRERE1DAM4IiIiIgahgEcERERUcMwgCMiIiJqGAZwRERERA3DAI6IiIioYRjAERERETUMAzgiIiKihmEAR0RERNQwDOCIiIiIGoYBHBEREVHDMIAjIiIiahgGcEREREQNwwCO6JlkWQbHceA4Dtrttl5cq91u164/nU7zbTqOoxeXlidJolfBcDjcOl/tdrtyPUmvyweAjcuxWqfX6+nknOQ1yzK9iIjoRWMAR/QMsiyD67qI4xjGGJyenpaCIC1JEjiOgzRN9SJgFZydn5/DGANjDIIgKARHi8UCAPLlvu/j8PCwEMQNh0NkWZav0+12K/O1WCzywM0Yg9vb23xZr9fL0yUfehuO4yAMQxhjEIZhKYiT7a/jOA7m87lOJiJ6HQwR/XK+7xvf9wtpAEwYhoW0Kr7vG9d1dXLl+13XNUEQGGNMaZlZvUeWp2lqAJg4jkvr2O8Nw7CUZrO3aYwxcRwb+1YTBIHxPC//26h82jzPK61rk7ykaaoXERG9aKyBI3oG8/kcR0dHhTTf93F9fV1I25Y0Ib59+7aQ3u128fXrVwBAv98vLAMA13Xx7t07AMDPnz8BAG/evCms43lenq8syzAYDBAEQeX2sFp/PB7nebq8vEQYhvny8/NzHB8fW+8ATk9P83wSEdFmDOCIfrG6YAsAlsulTtrJt2/fdFKhedOWZRnSNC0FYjc3N4W/YeX58+fPAIB3797l/dd08+jV1RVc14XrunAcB0dHR4V9pGmaB422KIp0EhER1WAAR/QCtFqtUs0XVgGhDrDEly9fCjVjnU4HrutiMBgU1ouiCK1WC1htz3VdXF9fwxiDNE2RpmlpoMHFxUX+//vWKhIRUT0GcEQvhK75kgEPurkSqxq179+/l2rfpLZO3i8DCezm3tPTU8xmM2AVOAZBgCiK8sBxsVhgMpnkAxTm8zmGw2H+fiLa3XA4rP0yRq8TAziiX0xqs378+FFIz7IM3W63kLar29vbfPSn1K6NRiO9Grrdbh6EafJ+GUHqum4p0LPp5tDBYICzszNg1e9OgjgJ8FzXxd3dXeE9379/h+d5hTSil6rX6xW+JOlX1bQ4s9mstjsEvU4M4IieQdWAhSiKSgMb7itJEgwGg0ITqWi321s9CBaLBcbjcaE5tNvt4vz8vLAeVkFZq9WqfPBI8CeDJKoGLCyXy8qaQqKX6OrqKp8OSKbTkZfUosu0P0S19LBUInp6esqOqqk16tRNIyJ836+d5sN13dKUG77vl9I8z6ucUkTybU/5offlum4hf2EYlvJrv6dqueA0IvRSybVUdZ0CWFvuicwq4ieiZyDzo1XdrCUwseeKkxu+/bIDLEmrC4b0e+Ul+7C3r/Nj0/moegC5rrs2P/Y2qpbb50ZeOkiz9wEVVBLtu10DONd1C/cD+4ucXAP6PfJlTl72/UK+HFVda/b1JtvQ26bn96AALgiC0oduv9bR75XCsa6Q1E32WWdd4TUVDwm9b3mIyksuFllPP0Dsl37Y2GS/VevoPKEi3/YynWdjjPlf/+t/Vd4UiIhoP9QFcFU16PKskQBO1pF0Yz077Fp9WSbbsJ+z8vzwPC+fWFyeefqZYweOtD/WR1lb0t8M7G/XVUGKLLNJ4av6Nm6sYKpu+Tp2IFalqglJLgw73T4uoS8aYx2fvgiEXDzrglG5+HS+hOu6tdv/61//anzfN57nmf/7f/+vXkxERM9M12Tbryr6OStBnA1W4BcEQeF5adfYGes5pElQJ+I4LgWZtB+eZBBDq9XKO09/+fKlsKzdbsN1Xfxe1v5Fp9PJ55SqcnFxgSAIkKbpvTp3BkEArPa/Sa/XQ5qmMMbkIwaxOi7Jd1VnbRHHMQBgMpnoRfn7ZM6uOh8+fACsjt9au91Gp9PRyQCAP/zhD5jNZjg7O8N//I//EdPpFP/8z/+sVyMiomemBzH4vg/HcUpzK1ZxXVcn5SO8R6NRYVqgqt8Nrhr5fXJyUpgW6PLycu0odHo+TxLAwSpE9hQDi8UCaZri9PTUWvNftFot+L6vk/Mf25bpEOxRcdt69+4dwjBEmqZr56RKkgRRFFXmQ0gwWEd+iqhqpN/nz59xcnKCk5MTwPqBcU3ORVUQuFgs8vev0+l08Oc//xl/8zd/g16vh3/8x3/UqxAR0R6ZzWbwfR9RFNU+H7Yl05JIYLiNfr8P13Xzypd1lRX0vJ4kgJPpB/T8UTJtwvv37621i6rmprq8vMznlZKCfZ9C1e/3EQQB5vM5ptOpXgxYP0W0bjqH0WhUqJnTpNasqrZvuVyi3+/n52VdMPrx40dEUZQHsOLs7Gynb0R//OMf8Q//8A/4b//tv231rY6IiJ7PwcGBTtpZr9eD7/ul1q5tnJ6eYjweb11ZQM/j0QK4+XyeR/uDwQDGmFINlARd+sey18myDMvlMm8u/PTpE1DRNLut0WiUN1/qwAirCUVR8zuV25ICL0GnmE6nhdpHPYO9Jj9tdHl5maclSbLzZK9/+ctfMBwO4bpuZYBMRET7QSpAYM2heB+3t7eln9XblnThGQwGD8oDPTHdKe4+7M6V0vm+qoO9dJqs65hfJQiCUgdKz/N2GswQhmFpG9JZNE3TwiAG6Rhalf8qVaNG696v8yydWNcNZpABGJI/bzXsextybLu8h4iInpY8C+te+llhL5NBBnaaHhBhjyi10/R+ULEvISNTaX89egBn1oxukeBOB1Pr6MJmv+zt6MJq778qgJMCL3mXAEm2sy6oslWNQq1Slb+qvFbB6uJL07QwOqjOX//6V/Of//N/Nq7rlo6biIhok6rnJu2XR2tCtV1dXQGrNnibDELQTYu26XSaN21Op1MEQVAYoSMvqP5j/X6/cp06MlI2TdPC6BzpwLluhOhisdi5c+nZ2Vk+stV+yWjdddvzfR/z+TwfALHJly9f8Dd/8ze4vb1l9TcREe3s4uKCz4899yQBHFZTaURRVOrIH8cx0jSF4ziFdFgBX6fTQZZlGI/HlT/EDav/2LrAZxMZ1KBJX4GqHxUeDoe4vr7eqWBPp1N0u93KgQ8SMA4GA70oJ/3+5vP5Vvv94x//WHveiIiI1kmSpPJ5RXtGV8ntQv+agm5KtJfrdnbdhm+/Vzc36mpc3Zavt23T+6jieV5lvzy9H50Xmex3XT70cWp6+3VNt95qtmwiIqKnIM+hbbrq0PNzzKa2RiIiIiLaK0/WhEpERERET4MBHBEREVHDMIAjIiIiahgGcEREREQNwwCOiIiIqGEYwBERERE1DAM4IiIiooZhAEdERETUMAzgiIiIiBqGARwRERFRwzCAIyIiImoYBnBEREREDcMAjoiIiKhhGMARERERNQwDOCIiIqKGYQBHRERE1DAM4IiIiIgahgEcERERUcMwgCMiIiJqGAZwRERERA3DAI6IiIioYV5cAJckCRzHyV9JkuhV1ur1ephOpzoZ7XY73+ZwOCwss/epl4nhcFi7jIiIiGgXLyqAS5IEk8kExhgYY+D7Pg4PD/VqtabTKaIo0slot9s4PT3Nt7tcLgvB2OHhIcIwRJqmmM/nWCwWhffL37PZrJBOREREdB8vKoB78+YNrq6u8r8/ffoEAMiyzFqrWpIk+Pr1q07GYrFAmqYYjUZ52sXFBebzObIsy2v4fvvtN7RaLbiui+vr63zdLMtwdnbG4I2IiIgezYsK4FqtVuHvnz9/wvf9UnqVk5OTQvAn7u7u4LpuIa3T6QAAbm5uCulVut0ulsulTiYiIiK6txcVwNmkOXWbmq/hcIiLiwudnEvTtLIW7+7urhDMZVmGNE1xdHQErLY7mUy2CiCJiIiItvUiA7h2u43Dw0NEUYR2u60XFyRJgoODgzwQ0z58+ACsgjEhzabv3r0DAMRxjMFgANd14fs++v1+3u+t3+8DQD7IYVN+iIiIiDZ5kQHc7e0tjDHwPA9pmlaOKhWTyaTQv01rtVqI4xhRFOVBmAyM+O2334BVk6oMcJjNZqV+b71eD77vwxgDqGCQiIiIaFcvMoATV1dXpf5rtsViUQjMHMcBAIzH4/z/UAGaBIbr+tbpfm9RFOXNqnoZERER0a5edACHVXOqNHVq/X6/EJhJDVkQBPn/teFwiCiKavvWsd8bERERPbUXHcAtFgvc3t7m/dAeynEczOfz2uBO93sTnuflU4ssl0t0u93CciIiIqJdvKgAbjgcFppDr6+vcXt7W1in1+vB2eEXGhaLRb69dTVzi8Widr63q6srzOfzvFm2ah0iIiKibTmmLiIhIiIior30omrgiIiIiF4DBnBEREREDcMAjoiIiKhhGMARERERNQwDOCIiIqKGYQBHRERE1DAM4IiIiIgahgEcERERUcMwgCMiIiJqGAZwRERERA3DAI6IiIioYRjAERERETUMAzgiIiKihmEAR0RERNQwDOCIiIiIGoYBHBEREVHDMIAjeiZZlsFxHDiOg3a7rRfXarfbtetPp9N8m47j6MWAtd/hcKgXAQCGw+HGfNnrOI6D6XRaWN5ut/NldfsRsl6v1yuk2/tYLBaFZcLOg+M4yLJMr0JE9CIxgCN6BlmWwXVdxHEMYwxOT09rgyWRJAkcx0GapnoRsArezs/PYYyBMQZBEMBRQdx0OoXruoU023A4RJZl+Ta63W4pX1mWYblc5usYYzAajfLl7XYbp6en+bLlclkZxEmw6fs+jDG4urrKl+l8DAaDUhA3nU4RBEEhH61Wq7AOEdGLZYjol/N93/i+X0gDYMIwLKRV8X3fuK6rkyvf77quCYKgkCbpev9pmhoAJo7jQrreru/7pf2IMAyNvq3EcWwAmDRN87QgCCr3Zax86PX1Mev9EBG9JqyBI3oG8/kcR0dHhTTf93F9fV1I25Y0Hb59+7aQ3u128fXr10JanZ8/fwIA3rx5U0j3PC/PV5ZlmM/nGAwGpZo5ALi7uyvV8HU6HQDAzc0NsKpJHI/HCMMwX2b78uULXNct1KZ9+PABaZrmxylNtlXNt0RErwEDOKJfrC7YAoDlcqmTdvLt2zedhNvbW520lgRaNsnzz58/8+ZKrAIo3bRpB1q2u7s7AMBkMoHruri7u6vs//b9+/fK4BBW3t6/fw9jDOI4xng8LjUVExG9dAzgiF6AVqsFz/MwHo8LwdNyuawNhrROpwPXdTEYDArpURTltWF2jdnt7S3CMMRgMMj3+eHDB2DVh00kSQIAePfuHbDanpAgLIqiyn5ydSQfnU4Hxhh4nlcaBEFE9JIxgCN6Ia6uruC6LlzXzWu20jTF8fGxXrWW1NbZIzsBlJp7Rb/fh+d5ec1Yq9XKAzJ5/+HhIQDgt99+y983mUzygQ+dTge+72M+n+fLdzWbzQqBIdFLMxwOt/4yRq8DAziiX0xqs378+FFIz7IM3W63kLar29vbvIkzDEMAKIwQ3YY9qjMIAriui36/r1fL6ZGfUismL8/z4Pt+aT3bwcFB4f+62Vf659lBoG3dton2Ta/XK3xJ0q+qLgiz2ax0XdDrxgCO6BlUDViIoqi2pmtXSZJgMBjkQdx9LBYLjMdjXFxc6EUFWZbVBlbD4RBRFGE2m+VpnudVbtPzPKBiwAJWffv0wAZblmX5+4n23dXVVT4dUBiGhS88Uouu+5YSlehhqUT09PSUHUEQGM/z9GqV6qYREb7vl6b+0KqmEbF5nlc7zYfN9/3KaUrM76McKqf6kGlFJH/6XJjVdu3zoZdretoRon0n5b7qOgWw9f2AXq/y3ZWIfgkJZKpu1jKfmh1kyQ3fftlBjaStC+5ku/ZL2NvX+RESHMpLP3zs7dcFdkYduz4OIUFk1fK6YyBqil0DOP2ly/4iJ9eBfo++Xu3ryPM843le6VqUl3whkm3obdPze/Q7ny4wUuDkw9fLq151N/66gma/0jQ1ruuW0vX2ZSLRutdT+etf/2qCICg9kGz2Q3BdLYnQeZdXVY2Eve2qGwcRET29ugCuqgZdnmnyPJB1JN1Yz0e7Vl+WyTbkOSxfjiSI81cTi8vzQT+ftnkO0a/3aJGKFEb97V8KhB3A6YJpF44wDDcWFvubh80OjHRhFmmaFrav92/XQlQFQA/xf/7P/zH/6l/9q9oA1VTMZC8XV50gCEr5jOO48tuSXOz6nBAR0a9VVaMuryr6WSVBnA1W4Kd/vUQ/NyWI0ySoE3Ecl4JM2g+PNojBdV14nlcaJdPv9xHHcZ5+cHCwdkTbumWbjEajypndba1Wq9ChWmu1WnnH7y9fvujF95JlGfr9Pv7n//yf+Id/+Ie1owLPzs4KHc/Pzs4wn88rRyVhNaGp7th9eXmJk5OTQpoMPzfGbDxHRET0a+hBDL7vlya3rqN/9QTWhNmj0agwLVDVND1VA39OTk4QRVH+zLm8vHzQc5mezqMEcPJTNjpoEJ1OJ58eYV3wItYFWHW2+TmdbdaBdQHIxKMPMZ1O8e///b/Hf/gP/wFXV1elYMuWJAnSNC2M6JPJVatmx4eaWFUsl8vCNobDIdI0LQXXRES0X2azGXzfRxRFDx6JKtOSSGC4jX6/D9d18wqMusoDen6PEsDJby3WTSWAewZl20qSBN+/f9fJJefn5zqpRKZO2DT31SZZluFf/+t/je/fv+Pq6gr/9t/+W71KicwLVhXk6Skn6iRJgna7nW8jW/12ZRAEaLfb+QX90BsDERE9DXtexPvq9XrwfR+/t6zu5vT0FOPxGIvForZihp7fowRwUrNTFXg8lTRNSzO9Vzk8PMzXk3l3tPl8nq8zGAxgjHlwbVWr1cL/+B//A3/+85/xX//rf8U///M/61WehG4+lZq78/NzLJfL/JvYYDDIf+KIiIj2g1Qi4IFdim5vb0s/q7ct+Um8wWDwoDzQ03qUAO45uK6b9xmoC8wAII7jfL2q9n6sJlU1q1nnYf1240N1Oh38+c9/xtHREXq9HqbT6ZMHcvP5vPKCu729zQNsqQ29vLxUaxER0VPr9Xp5/7XBYFD4FYbBYJA/34RUQMznc/R6PfR6Pczn87wiI8syOKufvRuPxxgOh5hMJoWftOt2u4WKjyiKEEVR5c9ztVot+L6/dbMrPY9HCeCkf9tjBT5YfQuxC7UUziqtVmurKudNvwk5Go3ged7aGr376Pf7+POf/wwA+Lu/+7vavnhv374FKvocpGm61Qz9SZLUBqlaVedXIiJ6eldXV4WBC/qlW4DsZVdXV6X3t1qtwt+z2Qz9fr+UpvdjKvYljo6Otnru0PN5lADu06dPwOoHqusMh8NSYLKOLnxmQzv+NoMjtlnn6uoKWH1Demyj0Qj/9E//hP/3//5fZbBbNWBB1lvXv1Do5lNY76vq88aLk4iIqlxcXFS25tD+eJQATqbeqKuObbfbODo6+qV95B4ijuPaY3moP/zhD/jv//2/V44exSoIHgwGhb+DINjq3FU1n0pVuL1NqQHU6xIRESVJstUzh56Znhjuoap+BUFPNGusWaLt16YJZqt+iaFqUlydh7pJf/W27P3by6ve/5Tsfevjq5ql22wxAbK87zmOh4iI9p88I6omgqf945hNbZNEREREtFcepQmViIiIiH4dBnBEREREDcMAjoiIiKhhGMARERERNQwDOCIiIqKGYQBHRERE1DAM4IiIiIgahgEcERERUcMwgCMiIiJqGAZwRERERA3DAI6IiIioYRjAERERETUMAzgiIiKihmEAR0RERNQwDOCIiIiIGoYBHBEREVHDMIAjIiIiahgGcEREREQNwwCOiIiIqGEYwBERERE1DAM4IiIiooZhAEdERETUMAzgiIiIiBqGARwRERFRwzCAIyIiImoYBnBEREREDcMAjoiIiKhhGMA9QK/XQ6/X08lERERET+pFB3C9Xg+O48BxHLTbbb24YLFY5Os6joMsy/QqBb1eD1EU6WSirWVZtnX5tLXb7dr1p9NpoRxXkf0Oh0O9CAAwHA4L20iSpLB8m3202+18ed1+RK/Xw3Q61cmla9L+sqTzsO2+iIheihcdwF1dXSFNUwBAmqalB5Ht7OwMABAEAYwxaLVaepWCq6sreJ6nk4m2kmUZXNdFHMcwxuD09LQ2KBNJksBxnLxMa9PpFOfn5zDGwBiDIAhKAdZ0OoXruoU023Q6xXw+z7cRxzEODw/zLzQSaMly13VLtdDtdhunp6f5Osvlsjawmk6ntV+Ezs7OkKZpvp2rq6t82ffv3/N0eXmeh48fPxa2QUT0YpkXLk1T43meAWA8z9OLjTHGhGFofN83AEwQBHpxLc/zardJtI7v+8b3/UIaABOGYSGtiu/7xnVdnVz5ftd1K8u067ql/ZuaMg3AxHFszOp6soVhWMhLGIZG31biODYASu+N4zi/NnUe5ZqsInmxpWlaeU6IiF6qF10DZwuCAFEUVdbCXVxc4NOnTzoZAArNM5tqSKCafbZZn16n+XyOo6OjQprv+7i+vi6kbUtqyN6+fVtI73a7+Pr1ayFtnePjY0RRhMViAazKs+/76HQ6AFCqmb67u8NkMin8rWv45L03NzeF9JOTk0Ktmu3s7Azz+RxORXcG2Z7t5uYG3W5XJxMRvVivJoAbjUYAUHjYYNUshYoHE1ZNQb7v501JaZrmD7Yq0+kUFxcXeZMOVv17iGx1wRYALJdLnbSTb9++6STc3t7qpFqj0Qi+72MwGMBxHFxfX2M2m+nVAKs5td/vF9LTNC0FXVgFd2I4HOLi4qKwXGRZhuVyCWMMfN+H67q1TbDi4uKCzadE9Kq8mgAONbVwk8kk7/9W5eDgALC+9dsPIe38/LzwsDs9PUUURZUPM6LH1Gq14HkexuNxobwtl8uda4LtMlwVUMogiPF4jPF4XAiuPnz4AKwCNCHX27t37/K/Dw4OKmvSsDoW+UI1m82Qpinm83ntl6csy3B7e1u7PSKil+hVBXC6Fk4eLHU3/tvbW4xGo7xZdJ0sy5CmKVzXzZtQx+MxAODnz596daJHd3V1Bdd1C2UwTVMcHx/rVWtJcGaMQZqmSNO0FAC2Wq1CLfN8Pi/UZMdxjCiK8jwcHh4CAH777Tdgdf3JtbiNVquFIAhqm5e/fPmC09NTnUz0ogyHw9K1SK/bqwrgoGrhLi8vcXJyolfJSeB2fX2dP6w2sUfNyasuQKTXSWqXfvz4UUjPsuzB/bhub2/zcheGIWB9cdnGcDhEEATAKp8SxNXVflWNiO10OoXy73kefN9Hq9XCYrEoBHfyxWg8Hq/9kiS1d1W+fv2K9+/f62SivWVPcVX1qmq1mc1mO3WHoJfv1QVw8jA7OTnBcrks9d8RWZZhMBggjuPaPkA2eSjrjtqLxaJy4AS9blUDFqIoKg1suK8kSTAYDPIgblv6ASFNs3VdB6Tcv3nzRi8CVgFhFEX5NdTv90tfcGBN31Pn7u6u8tyw+ZSayJ7iKgzDwvUgteh1X5qIxKsL4LB6WKRpWhrQYJNmT6kl2SYIC4IAg8Gg8O3p4uKCDxcq+fTpU6HpcTqdwvO82i8UuxgOhzg8PEQYhjtvr9vtFvrRZVmGKIryvm3acDjMa9c0x3HyOeUeYrFY4OvXr5XHwuZTemnkS1TdIB+inJ5X5CWROabkZc+RZc8ZJXPA2S97/jis5pBzXbf0fwCFbQVBUNhO1ZxVRMaaH03KlE3mU7PnQkvTtFRO7fJVVR412a790vT1YM/fZpd7VMw7Z29fz+1WR6+r81g3H5xZ5UfPL0fUBHI962vIrK4JfU/Qczfa80HKtaLfo69l+34hcz7a9yH7JdeVbENvm55f+e5NRERET6ougJOASVc42F9mZB1JN9YXQgnSpDLB3oYEYVI5IUGcTCwuX550xcO6L1H0fBzz0PYNIiIi2km2+jm9KlWP5Xa7jW63m/cnHQ6HpS4KjuPkXSfkp/WkSXY4HGK5XOZ/y+95633J3KUyyXaSJPjx40dlFwZ6Xq+yDxwREdE+0IMYfN+H4zhbTQJfFQDKgKPRaJQHa9IfVav6Pe+Tk5PC/KWXl5cM3vYUAzgiIqI9MZvN4Pt+4Sft7kumJZHAcBv9fh+u6+LLly+A9csxtH8YwBEREe0R+QWgh+j1evlPQe7q9PQU4/EYi8Vi7Vyp9LwYwBEREe2JxWKR/4rPQ5oub29vSz+rty2ZNmgwGDwoD/S0OIiBiIjoF5IBBHVc1y1Mqm3/Son0W7PfLz/jKHzfx9HREQaDQSGtqh+c3peQ3zPeZiJ7eh4M4IiIiKhA+t+xBm5/MYAjIiKigl6vl08lQvuJfeCIiIgolyRJ5c/j0X5hDRwRERHlfe08z2PtWwMwgCMiIiJqGDahEhERETUMAzgiIiKihmEAR0RERNQwDOCIiIiIGoYBHBEREVHDMIAjIiIiahgGcEREREQNwwCOiIiIqGEYwBERERE1DAM4IiIiooZhAEdERETUMAzgiIiIiBqGARwRERFRwzCAIyIiImoYBnBEREREDcMAjoiIiKhhGMARERERNQwDOCIiIqKGYQBHRERE1DAM4IiIiIgahgEcERERUcMwgCMiIiJqGAZwRERERA3DAI6IiIioYRjAERERETUMAzgiIiKihmEAR0RERNQwDOCIiIiIGoYBXIMtFgv0ej2dTERERC/c1gFckiRwHKf2lSSJfkuu6r0AMBwOkWWZXh0AMJ1O0W63dXKtxWJR2P50OtWrVOZBZFlWWp5lGabTKZIkwXA4LC2X12KxKGxLW7eO3pbOd6/XKyzXbm9vddJWhsOhTqJfzC5zu5T1drtdu/50Ol1bNnU518ux2r4s31ROZF07P/p612VayDVblQebfe3Z9wt9Ta67BxERvThmR77vG9d1C2mu6xoAJgzDQroxxnieZwCYOI4L6QAMAJOmaSFdyHL9vk3SNF373jAMS/n0fb8y/3Jc9nYAmCAI8r/l+Ow0WxAEBoDxPE8vysVxXLl/4ft+aZ+e55k4jo3v+yYMw7XnUgvD0Pi+r5PpF5JyKmUrCILSdaVJOQFQua4uB3ofdX/b5c513fz968ql7EuX6zRNC2mynr4+5Lqp275R17Im15WQvG57DRARNV35zrhBVQAnN1p9M5fAqO6m6rpubZC1TeBTJU3TPI9V+9YBnOynKh9m9aCx1696GNU9ZMzqGDftw1jrVak6B67rGs/zKoPMTQAwgHtmvu+XPoN1wYyt6hqsS3ddN9+m7/ulsqQDR329eJ5XKpcSlFXltSpNvnBoVQGkXqbPkaja5q7XARFRk23dhLrOz58/AQCtVitPy7IM8/kcnucV0m2TyUQnAQDOzs7w4cMH+L6PKIpqm1nXWS6XAIBut6sXFYzHY7iui06noxcBq7xs4rousDpm22KxQLfbxYcPHwAAl5eXheW2yWSC8Xisk5FlWe35i6JIJ+V0M1aWZXnzGQDM5/Otmq/oaczncxwdHRXSfN/H9fV1IW0XR0dHSNM0b7JMkgTtdhv9fh+oKUvv379HmqZ52dXLb29vMRqN8r+zLMNgMEAQBPl2bVVprVartN1Nut0uPM/DbDbTiwAAx8fHiKIoL7+LxQK+79dex0REL82DA7gsy3B4eAgA+PTpU55+c3MDrG60dfr9fumGmyQJut0uWq0WPn78CAD48uVLYZ1ttFotxHGMNE1rO/pLn5l1QV6n06l8KNnSNAUqHn4XFxf49OkTWq0WPM/DfD4vLLfJPnR/oc+fP+fnQfR6PbTbbcRxjG63izAMcXh4mD+EF4sFDg8Psaphhed5+Tk1xsB1Xfi+D2PMxmOjxyef09u3b/Wi/IvHffT7fQRBgPF4DMdxMJlMcHV1VVinbvvyJczWbrdL63/+/BkA8O7du/zLQV1/PLFcLktleJ3FYoE0TXFyclL6EiJGoxF838dgMIDjOLi+vq4N9oiIXqJ7BXBpmuY3Vdd1EccxjDGFAObu7g5Y3eh3MZlM8pt9p9OB53k4Pz/Xq22l0+kgCAJEUVQKjADgx48fAICDgwO9aGvSyTsIgkK6BIdyTqQmb12Nl+/7pWNdLpelIPfq6gpXV1f49u0blssl+v1+4fyfnZ0V8nNycpIHmfSyjUajvEZY19BKObCvhW/fvgEA3rx5k6fJQIg0TeG6bmFwwHK5hOu6uL6+hjEGaZpu/JLU7XZLZXgdqYW8uLjIv4S4rpsfl7ADNh1oEhG9dPcK4FzXhTEGcRwD1kPgoeQbtn2zl4fOfUeYjUYjeJ6H8Xi8NnjahdRwOI6D+XyOIAgKzUxYNZfaza+dTgeu6+Li4qKwnu3Tp09I0zTP53Q6xenpqV4t9+7du8rajzRNC4GzBHj08jmOg+VymX/e0mSOiho6x3HyLgT2l6/RaARjDMIwBFbXoO309DQPnlqtVv4lSXchwOoL2X1qxjzPK9QeynUj14Z0B7CDyKprgYjqyXX0WM9G+rXuFcAJqeEaj8elAOv9+/eA9W16G58/f0YURYVmk8FgAKj+clLo7Jfev+3q6gqu62IwGOQ1gwDw22+/AQC+fv1qrb1ZEAR5zYAxphS8Zav+f4eHh4U8pmmKKIpq8ypNrRL4nZ+fl7Zt6/f7pSay+7CnjXDWTPtAj0OCJakBFlmWrW3O32Q4HML3/Xz7EsTZn6cEZ/LCmr6oEvBtUlfL3uv17hW8VbFrCLE6Vslbq9XKgzg+iKiJqp5ni8Xi0cuznhZIutWwK00zPSiAg1XDJf3ghDR/zufzym/mWDWvyMMly7K85kC/9GAGKXT2a1MTjTSx2AMFWq1Wvu26oCrLslKh3+Tz588Iw7CUR2nGXDeYQWoch8PhvR/m0sRlS5Kk9hhvb28L+VwXNNLjqBqwEEVRaWDDLqquM9/38f37d50MrAJ3z/PW3rx1LW+32y0182NV5uxavOFwiLOzs1K/0G0cHR3V1ujJly49/6F8+bG/oBHtO+muoCsFJpNJXnnxmNb1w6YG0sNSN6maqsCY+nndJF0P7w+CoDANgJ6uw1Y3TUkVmUakSt28VjIVh04Pw7B0rKiYRsQWx3HpPTaZWkWfD9t9pgaxVU3zYJ8TewqGunNFT0vPyaavh3XqrsGq6Wr038a6DrbZn36/5Nu+BqrKWtW1pNPWTSMi0+QIz/MKZVWuI7nfyLb0/YdoX1Xdp22+mv/zoeSaoZdj609Tbvr2yy5cerl9I5UHS9177XT9UNHbRcUDSXjW5KB16wVBUHnByMVkv6oeGPZLPyz0cer96/zpYxV6bq770Hmxj9k+Vno+dtnWZUE+I7sMSpCyrozpz91ebpdh/T5Tc61V0fmwy5Yu4/LS5VlfT/r4jfVFBjXzwelt6OuRaJ9J+a6TpunaZ6wO7iTNvr/LtamvS7neZF39RSi05mKFdb+QPMv77TzZ94F1eZU0uabt/Mp+7HvMukqZ166+9BAREdGjkwCl6otLFQmIJNCSvyWwkWDHdd08zVc19RKQCTtwStO0EDS51sTynpo0W/9tB31mi7yaVSBo/y3r2IGivF/XxtO/eHAfOCIiItpe1eT360wmk8IApU6nA9/38z7mv8dwv/dRlYFDMrF3nX6/n480hzUYCKuR5tIX+vj4uNTndJ1Ned2WnKNd9v3aMIAjIiLaY1EUleYrlflS7Um49TqoGdy0iR5Zvi4Q1LbN6zrdbjefxSFJkkeZbeElYgBHRET0C8msCZyAutpsNsvnmT08PKydKPy1YwBHRET0iwVBsHHuQglcXNctTd8j80humkLrsW1q9t0mr5sm3R4Oh+h0OjCrHwxYN9XXa8YAjoiI6BeTOVQHg0Fp8vQkSeA4Tt50eHFxkc8PKvRPJm4izaL3mdtUs3+mT7Y1GAywWCy2zqtd+yi/9nJ4eIgkSbBcLvNzIpN468m8yR6SQkRERL+UnnIDNaNT9Xoy6lNP6yPzx9lpMrrTfq9e509/+lPh7yAIaqfqsdNkNOu6aUTsZaYiz7INyac+Bv1++p1jZPgKERERETUCm1CJiIiIGoYBHBEREVHDMIAjIiIiahgGcEREREQNwwCOiIiIqGEYwBERERE1DAM4IiIiooZhAEdERETUMAzgiIiIiBqGARwRERFRwzCAIyIiImoYBnBEREREDcMAjoiIiKhhGMARERERNcyzBXC9Xg+O49S+hsMhFotFKV2/siwrpdmvxWKhd/1okiTBf/kv/0UnF9h52WQ4HJby7zgOptOpXhUA0G634TgO2u22XkREREQv2LMFcFdXV0jTFAAQhiGMMYVXlmXo9/swxsDzPHieV1rH930AgDEGQRDk/5eX53kYDAa1AdB9/fM//zOGwyEmkwn+3b/7d3pxznGc/NjCMFwbxGVZhoODg9Ixuq6LDx8+FNaVwLbdbsMYg9vb28JyIiIietmeLYDb5OrqSieVzGYztFotnZyTbZyfn+tF97ZYLPB3f/d3+Nu//VtcXV3hb//2b/UqAIDpdArP89Dv9wEA/X4fruvWBpM/f/7EaDQqpCVJgna7XTjGxWKBwWCAMAy3OkdERET08uxdALdYLJBlmU4uqQuEbLKdx2hi/Mtf/oJer4fr62v80z/9E/74xz/qVQrOz89xfHxcSDs9PcXXr18LaaLT6egkfPv2rbCNLMswGAwQBEEeGBIREdHrs3cB3MXFhU4q2bZfm+u6AICzszO9aCd///d/j3/zb/4Nzs7OMJvN8Ic//EGvUpKmKd69e6eTEUWRTqp1fn5eaD79/PkzAODdu3d5/7jHCE6JiIioWfYigBsMBnlAUhfgRFGUrzMYDPTinN35X/qfVdVu7eLDhw/4T//pP+Hk5AT/+I//qBc/iarm0+VyCdd1cX19DWMM0jRFmqbo9XqF9xIREdHLthcBnD2IQQYmaPYghjiO9eKcBDYAcH19rRffyx/+8AeMRiMsl0v87//9v9Hr9ZAkiV7tUV1eXuLk5EQn4/T0FLPZDADQarUQBAGiKNqq2ZmIiIhehr0I4GxHR0c6qWRTjVqr1UIYhpjP51s3t26j1WphNpshCAJMJhP0ej385S9/0asBq+bbu7u7Qtr379/heV4hrc5yucRvv/2mk0uqmmmJiIjoZdu7AK7f768dWSr0iE2t3+/D930MBoNHr52SEahnZ2f4+7//e70YqBmwsFwuSwMbqlQ1nwJAt9utHFHrum5pXSIiInq59i6Ae0yz2Qyu68J13Sdp8ux0OnlzpjYajRBFUV4DKP9uCjyxpvn006dPSNO0MAJ3MBhgMpkU1iMiIqKXzTHGGJ34K/R6vdKABZ0VmfPMFoZhYQqNLMvy0abC9/08sNLL4zje2AT7WOx9u65bmHBXlul0rAZi6HMh9PHo80FEREQv37MFcERERER0Py+6CZWIiIjoJWIAR0RERNQwDOCIiIiIGoYBHBEREVHDMIAjIiIiahgGcEREREQNwwCOiIiIqGEYwBERERE1DAM4IiIiooZhAEdERETUMAzgiIiIiBqGARwRERFRwzCAIyIiImoYBnBEREREDcMAjoiIiKhhGMARERERNQwDOCIiIqKGYQBHRERE1DAM4IiIiIgahgEcERERUcMwgCMiIiJqGAZwRERERA3DAI6IiIioYRjAERERETUMAzgiIiKihmEAR0RERNQwDOCIiIiIGuZVB3BJksBxHGRZphcRPbksy+A4DhzHQbvd1otrtdvt2vWn02m+TcdxsFgs9Cro9Xr58iRJCsv0++U1HA4L64nhcFi7DNY1tk6v18N0OtXJhf1XLR8Oh/c6f0REL8GrDuAmkwkA4MuXL3oR0ZPKsgyu6yKOYxhjcHp6ujEIkWAoTVO9CACwWCwwHo+RpimMMUjTFIPBoBCk9Xo9tFqtfPnh4WFh+ffv32GMKbw8z8PHjx/zdcRiscB8PtfJBYeHhzqpYDqdIooinQzHcRCGYZ6H8XhcCOKGwyGyLMuXd7vdjeePiOhFMa9UmqbG8zzjeZ55xaeBnonv+8b3/UIaABOGYSGtiu/7xnVdnVyZ7rpuvs04jktl3fd943levlxL07S0TTvddd3ScYhN11ccx/nyIAjy9CAISvsMwzDfTpqmBkApv9uePyKil+DV1sB9/vwZJycnODk5AVa1CVXsZq66piC7KWddcxKRmM/nODo6KqT5vo/r6+tC2i6Ojo6QpmleU5UkCdrtNvr9PgDg8vISnucV3vPx48e8BqzT6RSWAcDNzQ263a5OxnA4xHK51Mm5xWKB4+NjHB8f60W5k5MTXF1d6WR8//69VJv222+/Aatj+vnzJwDgzZs3hXU8z3vQ+SMiapJXG8Atl0v0+/384XZxcaFXyZu5pCknCIJSoNbr9XBwcJA3Sc3n88r+OkRC+ly+fftWL1obFG3S7/cRBAHG4zEcx8FkMikESFmWodVqFd4jdF84cXFxUWo+nU6nODk5qd1WlmW4uLjAaDTSi3LD4bDymhNVzaoA8OPHj/z/Nzc3hWWwzi0R0Uv3KgO46XSK09PT/O8gCBBFUenmLw8ICfLkgRSGIWazGbIsw+3tbZ7earXgeR7Oz8+trRD9OqPRCK7rAmuCoG1J+bZr5rIsw/fv3/NrospwOKysWRNJkuDg4KCyxg+rWkGstiPkWnz79i06nQ5c18VgMMiXY3W8dUElEdFL8yoDuPPz80LtwIcPH4A1gxnswM51Xdzd3QGrh0qapoUm1iiKajuZEz01x3GwXC7xe5ew3/++ry9fvhS+6GAVVM1ms0KabTqd4uzsTCcXTCaTtbVznU4HYRhiPp/n15UEaxL03d7eAmqkKlbNyES0HekiVNeFiPac7hT30kln6LqX5rpu3sFaOoFLR+kwDEudrYm2UdXh3vO82gEBtqrBCpKu328PELAHLAh7cIDmeV5hoICU/7qXDGzQ6fIKgmDn60/Y12GVqoEPRE2hr4M4jk0YhqV7xEPp+wM1W/0d84VyXdekaaqT8weLvmD0A8d+iMgDTW+PFwltUhds6fJXpS6AqwoA7f1sGoVqqxt9qq0bhWpWgZXep6avK83zvLV5kWtUj0ol2ndyfejyL6Ozt7kf7GLTtUjN8qo+zSAI1j5spPZAyDQH61Q9XDa9h0hPhREEwdblpi6Ak4eBHcjov+0gT/Kgv4CY1bb0Q6XKUwZwkr+qYxXyoGPwRk1TV2kgfN+vvC7uy/f9jdciNcur+TTlRi8vzV4mDxR5gOiXfqBs2jZRFbtJUgdvcnO3g6Oq8qgDFwmY6pYb64sKaoI3s6amWnuKAM4+hqqHm30e9HkjagpdYaClaVq4LnQXBh3cSZrdaiTXj35GyXUj68q1LtdWGIaF61DuI5Jneb+dJ/taXZdXSZP7hp1f2Y99jadpuvYe85rVlx4yZvUw0eI4rkwnIiLaRAKUbb+ASEAkgZb8LYGNBDv2FypdU6+/TNmBU5qmhaDJtfqceqsJuYX+2w76zBZ5NRVf/GQdO1CU97uuu/V5em1e5SjUbfV6vXyEqu3Nmzd49+6dTiYiItpIJqPedtqbyWQC3/fz9TudDnzfx3w+z39SDgC63W4+Slwm9q7T7/cRhmH+d6vVytc/PT3NR4ofHx/no763sSmv25JztMu+XxsGcGtEUVT5ywrdbnftPFhERESPJYoiHBwcFNJkvkQJdACU1sE9J7fWFRTrAkFt27yu0+12cXh4CMdxkCTJ2nklXzMGcGsYY3B7e1uYa8pxHH4jICKie5P5DB/yyysv2Ww2QxzHAIDDw0P0ej29CjGA2+z29harvoL5i4iI6CGCIECapmsn0ZXAxXXd0i/8yM/K1f2iyVPZ1Oy7TV71bx1rw+EQnU4HxhjEcYwoimp/7u81YwBHRET0i41GI3ieh8FgUPr97CRJ4DhO3nR4cXGBNE0LXXrOzs4QBIH1rvWkWTTLssquQbuwf6ZPtjUYDLBYLLbOq137eHJyAqxq25IkwXK5zM/JmzdvCv+SRY9qICIiol9DT7mBmtGpej0Z9amnF5L54+w0Gd1pv1ev86c//anwd7CaN9VOk5GhdlrVfHZ1eRU6z7INyac+Bv1++p1j2CZIRERE1ChsQiUiIiJqGAZwRERERA3DAI6IiIioYRjAERERETUMAzgiIiKihmEAR0RERNQwDOCIiIiIGoYBHBEREVHDMIAjIiIiahgGcEREREQNwwCOiIiIqGEYwBERERE1DAM4IiIiooZhAEdERETUMAzgiIiIiBqGARwRERFRwzCAIyIiImoYBnBEREREDcMAjoiIiKhhGMARERERNQwDOCIiIqKGYQBHRERE1DAM4IiIiIgahgEcERERUcMwgCMiIiJqGAZwRERERA3DAI6IiIioYRjAERERETUMAzgiIiKihmEAR0RERNQwDOCIiIiIGoYBHBEREVHDPFsA5zhO7Ws6nerVC/T6SZJgsVhgsVjoVQEAi8UCjuPo5J2s2+dwOCwtl1ddnsS6dfS29Hnp9XqF5dQsWZbln1273daLa7Xb7cr1pZzrV6/Xy9ex9+lUlL3pdFp6v+M4GA6HhfXa7Xa+LMuywjKbvb0kSfRiYFWOddmGKv9Vx2tbt30iohfJPKMwDA0Ak6Zpnub7vgFgfN8vrGuMMUEQGAAmCIJCuud5BoAJw7CQLlzXXbt8nV32qdeTdfR7hWzb8zy9KBfHcWk/Nt/3a7dP+ytNUwPAxHFszKosuK6rVyuQsgCgct2qa8b3/bzs6H3K33bZqtqG53n5e8zqepIyJ3myr2Ehea1aJuquL9d1C3nxfb/2OpHrzM4jEdFLt3cBnLECLpusu2sgE8ex8X3fuK5b+dBbZ9d9Vj2I5CFWRR6Emx4+9gNTq3uo0X7zfb8ULK0razYpz7Y0TUvXkVltU1QFQXbgWFUG0zQt7CsMw9K+Pc+rPBa9Ly2O48ovOVWBZdV+zSr/DOCI6DV6tibUddI0heu6hbSzszMAQL/fL6SLT58+6SQAwGQywcePH3F6eoo0TXdqZrnvPm1yHLqZabFYoNvt4sOHDwCAy8vLwnLbZDLBeDzWyciyDK1WSydTA8zncxwdHRXSfN/H9fV1IW1brVarVBaSJIHnefnfVeXl/fv3SNMUWZah0+kUlgHAzc0Nut1u/vfFxUXhbwA4OTnBcrnM/+71enBdF1dXV4X1tJOTk8p1Wq0WXNfFYDDI0y4uLnBxcVFYL8syfP36Nb9OiYhek70L4KSvy2QyydOyLEOapoWHkdZqtTAajQppEjR1Op2tAiXbffeppWkKrNa1XVxc4NOnT2i1WvA8D/P5vLDcJgGk7if0+fNnfPz4sZBG+0/K5du3b/WiQiD0UJeXlzg5OSmk1W3/58+fOglYlVO7jN3e3uLg4KCwDlblPMsyJEmCKIowmUwKfdj0F6fhcFgKyGy3t7eA1Q/u7OysFGB2u93KAJCI6DXYiwDOdd38Rn16egpjTKHWSx4uOgja5PPnz/kDrNVqwff9tYGS7b77tEnH7yAICunyMJNtSw2C7lBu830f5+fnhbTlcll6qBGJ+XxeuI5OTk6Qpmnhi8C3b98AAG/evMnTRJZluL293amMyfbOzs6w6qIBz/NweHiYB65JkuDg4GDjdsMwzP8v2xXT6XRtAEhE9NLtRQCXpil+7zYDfP36VS++t+VyWXiASU3CukDpocbjcR6MzudzBEFQqqW7vLwsNPt0Oh24rrv2gfTp0yekaZrnfTqd4vT0VK9GBKzKuO/7hbR+v48gCApldDwew3Xdyi8qX758uVcZc103r0EDgNlsBqy2h1Xtur4mtOl0iuvraxhj8jxL4ClfgDYFgES0noxKf8pnIj0h3SnuV9KDGGREW1WHfdSMvKsjgwOqXno7MmhCXrL/qnXXqcu7TTpo173WdcT2PC/Pzy75ov2DigELVYMBqlQNYtDs0afrVOVD6NGnkqbzaA+EsP9vk2tDrvm6l7GuEXtQhlzPxhqpXvXaNHCCaB/pchzHsQnDsPbavC997VKz7VUAZ6wbtX5wSPq6Am3fvOVmr9Vtv8p99rkpgKt7sMpDa90FJueragQjNUvVZ7iprIltAri68m9zXbc24NGjT0XVaFA7qJMvYfr6qkoT+rqpmpqkKqgTdfsk2nfyjNHPDRlZvc39YBfb3BeoOZ7106wK4Myawls15YCxbuDCr5lSRGCHmrVt92kqHkRaHMdr9ys1C+seRFJbuG4d2n8SkMjnGKymw9jGpgAuDMNScGiTsrtuf0EQ1JZlt2IeOJtdU2xqpi+xVV03On/rzg8DOGoief7p55zY9BzblTxf6OV4tk8TG5o+7GV2AZebdd17JcBBRZBW1XxZd/HYNu1TLgz7pYNS+aYlL/2wkUCxavu2oKaJiprHLlf687ZrW0VV+dXlyNQ0fRpVTquW21zXLZVhm52HKnZ51semoSKAk/RttsEAjppInlV10jQtXBf6OaSvGUmTewes51vd80VXosg9JgzDwjNLri3Js7zfztO657SdV0mTe5udX9mPfa9L03TtF9LXrL70EBER0aOTAGXdFxObBEQSaMnfEthIsONav2Cia+olIBN24JSuJgK3tyNBl+d5hXzqv+2gz2yRV1PxSyv6S5j9BdJd09XjtduLUahERESvxa7TVE0mE/i+n6/f6XTyabGyLMPvMdzvcyPKqO+jo6N8HtIq/X6/MFVPq9XK1z89Pc1Hih8fHxdGlW+yKa/bknO0y75fGwZwREREeyyKotIE2jItlj0Jt14HFb8CtI13794V/l4XCGrb5nWdbreLw8PDfBJwTthdjQEcERHRLyRzGNb9MsprN5vNEMcxAODw8BC9Xk+vQgzgiIiIfr0gCAqTs1eRwMV13dIv8fz48QN4hgmtNzX7bpNX+cnMOsPhEJ1OB8YYxHGMKIpKP8dHDOCIiIh+udFoBM/zMBgMSr9znSQJHMfJmw4vLi6Qpmn+84xY/Vyd/pnGdaRZNMuywnbuI4qi/P+yrcFggMVisXVe7dpH+cnLw8NDJEmC5XKZnxP5mb+qn/t79fSoBiIiIvo19JQbqBmdqteTUZ96eiGZP85Ok9Gd9nv1On/6058KfwdBUJoiS0aG2mlV89nV5VXoPMs2JJ/6GPT76XeOkeErRERERNQIbEIlIiIiahgGcEREREQNwwCOiIiIqGEYwBERERE1DAM4IiIiooZhAEdERETUMAzgiIiIiBqGARwRERFRwzCAIyIiImoYBnBEREREDcMAjoiIiKhhGMARERERNQwDOCIiIqKGYQBHRERE1DAM4IiIiIgahgEcERERUcMwgCMiIiJqGAZwRERERA3DAI6IiIioYRjAERERETUMAzgiIiKihvn/AblvXJMKQ/qlAAAAAElFTkSuQmCC\"\u003e\u003c/p\u003e\n\u003cp\u003eTotal-NCPAV: total-noncalcified percent atheroma volume; RCA-NCPAV: right coronary artery-noncalcified percent atheroma volume; CT-FFR: computed tomography fractional flow reserve; RCA-CPAV: right coronary artery-calcified percent atheroma volume.\u003c/p\u003e\n\u003cdiv\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 9\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eExamples of risk prediction results for partial samples in the test set\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFollow-Up Time(Months)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMACE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePredicted Risk Score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eRisk Stratification\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.042288002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eIntermediate-Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.757425785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eHigh-Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.264241129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eLow-Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.742397368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eHigh-Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.152615681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eLow-Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.117221415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eIntermediate-Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-0.028739884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eIntermediate-Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n"},{"header":"DISCUSSION","content":"\u003cp\u003eThe present study revealed that AI-assisted measurements of plaque burden\u0026mdash;CPV, NCPV, TPV, CPAV, NCPAV, and TPAV\u0026mdash;were associated with the occurrence of MACEs. NCPV and NCPAV showed the strongest correlation with patient MACEs, while total-NCPV and total-NCPAV emerged as the most potent predictors of MACEs. Additionally, the present study provided optimal risk cutoff values for these indices. The plaque model, LASSO Cox model, and Cox survival neural network model all demonstrated good predictive performance for MACEs. Among these, the combined model (plaque\u0026thinsp;+\u0026thinsp;CT-FFR) showed the best predictive performance for MACEs. The plaque model provided incremental value to the CT-FFR model. The LASSO-Cox and Cox survival neural network models offered more specific coefficient weights for plaque burden across the three major branches, indicating that RCA plaque burden is the most critical factor in predicting MACEs. Furthermore, the Cox survival neural network model overcame the limitations of traditional models, providing more precise high-, medium-, and low-risk stratification for patients.\u003c/p\u003e \u003cp\u003eStudies on the impact of coronary plaque burden on MACEs have made considerable progress\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e; however, the research software component involves semiautomated segmentation or requires manual correction\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. With the rapid advancement of AI\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, the application of AI-QCT enables physicians to bypass postprocessing workstations and directly obtain the plaque burden of patients using their own computers. After adjusting for clinical risk factors, total-NCPV and total-NCPAV were the strongest predictors of MACEs. Abdelwahed et al.\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e demonstrated that noncalcified plaques are rich in fibrous and lipid components, which are critical factors in plaque rupture. Lipid components are present in 100% of the plaques in fibrous-capped ruptured acute coronary syndrome (ACS) patients. The coexistence of lipids and calcified components also leads to altered plaque stress and subsequent rupture. This conclusion aligns with the present findings, both indicating that noncalcified plaques strongly predict MACEs. The effects of calcification components on plaques are relatively complex\u003csup\u003e[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. In the present multivariate Cox analysis, total-CPV and total-CPAV demonstrated relatively lower MACE risks, with HRs of 3.40 and 3.47, respectively, indicating relatively low indicators. Punctate calcification is among the hallmark features of high-risk plaques\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. As the number of calcified plaque components increases, plaque stress tends toward stability, thus reducing the risk of MACEs. This phenomenon may explain the findings of the present study\u0026mdash;even with a relatively low calcified plaque burden, the MACE risk remained more than three times greater than that in the low calcification group.\u003c/p\u003e \u003cp\u003eAlthough studies\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e have indicated that statins alter the plaque phenotype, reduce the lipid content, and promote plaque transformation toward calcification or fibrous components, thereby lowering the risk of MACEs, coronary anatomical stenosis may still persist, and patients may continue to exhibit coronary hemodynamic abnormalities. Therefore, patients with coronary plaques require early initiation of statin therapy to lower lipid levels and prevent plaque progression. At this stage, identifying the risk cutoff point for plaque burden becomes particularly crucial. The HR for total-TPV and total-TPAV ranked second, which may also indicate an interactive relationship between calcified and noncalcified components. Compared with total-NCPV and total-NCPAV, incorporating calcification indicators slightly reduced the risk associated with total-TPV and total-TPAV. The risk cutoff point in the present study overlaps with the safe threshold for plaque burden as reported by B\u0026auml;r\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. If the plaque burden of a patient does not exceed the optimal risk cutoff point, early pharmacological intervention may suffice, potentially eliminating the need for invasive interventional procedures and reducing unnecessary patient suffering. Further surgical treatment can be reserved when the condition of the patient progresses.\u003c/p\u003e \u003cp\u003eGall et al.\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e utilized the CAD-RADS score to investigate the effect of plaque composition on MACEs in patients with obstructive coronary artery disease; however, these researchers did not provide specific detailed parameters for plaque burden. In a multicenter study\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e examining sex differences in plaque burden, AI-Quantitative CT was employed, but the included metric was total coronary plaque burden without specific comparisons of plaque burden in the three major branches. Tummala et al.\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e investigated the impact of proximal plaque in the three major branches on MACEs. However, none of these studies provided data on the specific influence of plaque burden in these three major branches on MACEs. In the present study, a C-index model was constructed on the basis of Cox analysis, incorporating LAD-CPAV, LAD-NCPAV, LCX-CPAV, LCX-NCPAV, RCA-CPAV, and RCA-NCPAV. The percentage plaque volume demonstrated greater generalizability than the total plaque volume. When combined with the CT-FFR model, the C-index for the plaque model reached 0.744 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas that for the CF-FFR model was 0.593 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The combined model achieved 0.750 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). An AIC\u0026thinsp;\u0026gt;\u0026thinsp;0.75 is considered excellent. These findings indicated that relying solely on functional indicators may lead to an underestimation of cardiovascular risk. Compared with single-dimensional approaches, the integrated assessment combining morphological (plaque parameters) and functional (CT-FFR) aspects provides a more comprehensive evaluation, avoiding the one-sidedness of \u0026ldquo;focusing only on plaque without considering ischemia\u0026rdquo; or \u0026ldquo;focusing only on ischemia without considering plaque.\u0026rdquo;\u003c/p\u003e \u003cp\u003eThe LASSO-Cox model established in the present study incorporated 10 indicators, ultimately identifying RCA-CPAV\u0026thinsp;\u0026gt;\u0026thinsp;2.8%, RCA-NCPAV\u0026thinsp;\u0026gt;\u0026thinsp;3.49%, and total-NCPAV as strong predictive factors, with contribution values of 0.55, 0.25, and 0.07, respectively, highlighting the significance of the right coronary artery in predicting MACEs. The C-statistic for the LASSO Cox model was 0.747 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although the conventional plaque model and combined model had lower predictive values, the LASSO-Cox model demonstrated superior performance in predicting MACEs, Although it did not significantly improve upon the standard plaque model or the combined model, it yielded meaningful results when the specific weight coefficients for the three major branches were identified. The Cox survival neural network model sensitively captures nonlinear covariates and resists overfitting, overcoming the limitations of traditional Cox models that analyze only linear variables. In the present study, the training-to-test ratio for the Cox survival neural network was 7:3. The training set achieved a C-index of 0.751 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for MACEs, while the test set achieved a C-index of 0.730 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for MACEs, demonstrating the generalizability of the model. The weights of the total noncalcified plaque volume percentage and right coronary artery indicators were slightly different between the LASSO-Cox model and Cox survival neural network model. This stems from the differing core logic underlying the two model types. The LASSO-Cox model operates under the assumption of linear independence, where coefficients solely reflect the linear independent contribution of features to MACE risk. Additionally, the coefficients undergo compression because of L1 regularization. For example, a one-unit increase in the percentage of total noncalcified plaque volume increases the risk of MACEs by 0.07 times. By contrast, the Cox neural survival network is a nonlinear model. The feature importance in the Cox neural survival network integrates nonlinear effects and cross-indicator interactions. While the percentage of total noncalcified plaque volume represents its importance within the model, both the LASSO-Cox and Cox survival neural network models simultaneously emphasize the critical role of right coronary artery plaque burden in MACE risk. The Cox proportional hazards survival network model enabled more refined grouping by not only dividing patients in the test set into high- and low-risk cohorts but also identifying an intermediate-risk cohort. This allowed for more precise stratification of patients at potential risk for MACEs. Patients in the low-risk cohort exhibited extremely low MACE risk. Further, there were clear differences in MACE risk across the three groups, fully demonstrating the clinical value of the model.\u003c/p\u003e \u003cp\u003eIn a study of coronary plaque burden in CAD patients, Bax\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e first reported that the LAD artery has the highest coronary plaque burden, and Bax later reported that the LAD artery carries the highest risk of progressing to obstructive CAD\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Giesen et al.\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e reported that noncalcified plaque burden in the LAD artery is most closely associated with the fractional attenuation index (FAI) (odds ratio 1.22) and that noncalcified plaque is most strongly correlated with MACEs; this study indicates that plaque burden in the RCA is most closely associated with MACEs, which contradicts the present findings. However, a meta-analysis encompassing 17 studies\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e suggested that an elevated FAI around the RCA is most strongly associated with MACEs, with a risk ratio of 1.22 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). The LAD artery showed no significant association, whereas the LCX artery demonstrated only a borderline association, supporting the present findings. Thus, the weaker impact of the LCX artery on MACEs may be less controversial. It remains debatable whether plaque burden or the FAI in the LAD artery and RCA constitute the strongest predictors of MACEs. However, the coronary plaque burden threshold determined by AI in the present study serves as a reference for clinicians. AI measurements of plaque burden are rapid, and the relevant AI software can be installed on the computers of all cardiac surgeons and cardiologists, offering both convenience and scalability.\u003c/p\u003e"},{"header":"LIMITATIONS","content":"\u003cp\u003eFirst, the present study was a single-center investigation. The data in the present study were divided into training and testing sets for simulation within the Cox neural survival network model, and the C-index for the testing set was acceptable, indicating some generalizability. However, the present findings have not yet been validated at other centers. Future collaboration with other centers could advance this research and validate its clinical value. Second, as the current AI cannot identify high-risk plaques, the present study focused solely on plaque burden parameters. Further validation will be conducted once AI capabilities are upgraded.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eOverall, the present study provides risk cutoff values for plaque models. The combined model of plaque burden\u0026thinsp;+\u0026thinsp;CT-FFR demonstrates the best predictive performance for MACEs, offering early warning information to guide patient treatment strategies. Additionally, the LASSO-Cox model and Cox survival neural network model further provide the weights of three specific parameters, indicating that RCA plaque burden is the most important predictor of MACE risk. AI-assisted quantitative coronary plaque parameters combined with CT-FFR enhance risk assessment and early warning capabilities for MACEs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Area under the curve\u003c/p\u003e\n\u003cp\u003eACS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Acute coronary syndrome\u003c/p\u003e\n\u003cp\u003eAI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Artificial intelligence\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Body mass index\u003c/p\u003e\n\u003cp\u003eCAD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Coronary artery disease\u003c/p\u003e\n\u003cp\u003eCABG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Coronary artery bypass grafting\u003c/p\u003e\n\u003cp\u003eCCTA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Coronary computed tomography angiography\u003c/p\u003e\n\u003cp\u003eCT-FFR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Computed tomography fractional flow reserve\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCPV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Calcified plaque volume\u003c/p\u003e\n\u003cp\u003eCPAV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Calcified percent atheroma volume\u003c/p\u003e\n\u003cp\u003eFAI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Fractional attenuation index\u003c/p\u003e\n\u003cp\u003eHDL‑C \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;High-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eHR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Hazard ratio\u003c/p\u003e\n\u003cp\u003eHU \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Hounsfield unit\u003c/p\u003e\n\u003cp\u003eKM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Kaplan\u0026ndash;Meier\u003c/p\u003e\n\u003cp\u003eLAD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Left anterior descending\u003c/p\u003e\n\u003cp\u003eLCX \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Left circumflex\u003c/p\u003e\n\u003cp\u003eLDL-C \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Low-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eMACEs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Major adverse cardiovascular events\u003c/p\u003e\n\u003cp\u003eNCPV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Noncalcified plaque volume\u003c/p\u003e\n\u003cp\u003eNCPAV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Noncalcified percent atheroma volume\u003c/p\u003e\n\u003cp\u003eRCA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Right coronary artery\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eROI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Region of interest\u003c/p\u003e\n\u003cp\u003eTPV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Total plaque volume\u003c/p\u003e\n\u003cp\u003eTPAV \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Total percent atheroma volume\u003c/p\u003e\n\u003cp\u003eTG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Triglyceride\u003c/p\u003e\n\u003cp\u003eTC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Total cholesterol\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was approved by the Medical Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University (approval number: 20210226-134), and informed consent from the participants was waived.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll procedures involving human participants were conducted in accordance with the ethical standards of the institutional research committee and with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was supported by the National Natural Science Foundation of China (Grant No. 82460346), the Natural Science Foundation of Xinjiang Uygur Autonomous Region (Grant No. 2025D01D39) and the Xinjiang Medical University Smart Healthcare Innovation Center Construction Project (Grant No. ZHYL-001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the relevant staff for their guidance, assistance, support and collaboration. (I) Conception and design: Yan Xing and Mengyuan BAO; (II) Funding acquisition: Yan Xing; (III) Collection and assembly of data: Xinwei Zhang; (IV) Data analysis and interpretation: Mengyuan BAO, Haicheng Qi, and Xinwei Zhang; (V) Manuscript writing: Xinwei Zhang, Mengyuan BAO; (VI) Methodological support: Yongshun Wu. (VII) Final approval of manuscript: All authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXinwei Zhang\u003csup\u003e1*\u003c/sup\u003e, Mengyuan Bao\u003csup\u003e1*\u003c/sup\u003e, Yongshun Wu\u003csup\u003e1\u003c/sup\u003e, Haicheng Qi\u003csup\u003e1\u003c/sup\u003e,Yan Xing\u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003csup\u003e*\u003c/sup\u003eThese authors contributed equally to this work and share first authorship.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eImaging Center, The First Affiliated Hospital of Xinjiang Medical University, No. 137 South Liyushan Road, Xinshi District, Urumqi, Xinjiang, P.R.China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eCorresponding Author\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Corresponding Author\u003c/p\u003e\n\u003cp\u003eYan Xing\u003c/p\u003e\n\u003cp\u003eImaging Center, The First Affiliated Hospital of Xinjiang Medical University\u003c/p\u003e\n\u003cp\u003eNo. 137 South Liyushan Road, Xinshi District, Urumqi, Xinjiang, P.R.China\u003c/p\u003e\n\u003cp\u003eEmail:[email protected]\u003c/p\u003e\n\u003cp\u003eTelephone:13579973586\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAntoniou V, Kapreli E, Davos CH, et al. Safety and long-term outcomes of remote cardiac rehabilitation in coronary heart disease patients: a systematic review. Digit Health. 2024;10:20552076241237661. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/20552076241237661\u003c/span\u003e\u003cspan address=\"10.1177/20552076241237661\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Zhang W, Hu Y, et al. Pericoronary adipose tissue radiomics features as imaging markers for coronary artery disease risk assessment: insights from gene expression analysis. Cardiovasc Diabetol. 2024;23(1):444. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12933-024-02530-6\u003c/span\u003e\u003cspan address=\"10.1186/s12933-024-02530-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmail MAM, Singh RB, Fedacko J, et al. Evolving Role of Coronary Computed Tomography Angiography (CCTA) in Quantifying Atherosclerotic Coronary Artery Disease: A Narrative Review. Diseases. 2025;13(10):343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/diseases13100343\u003c/span\u003e\u003cspan address=\"10.3390/diseases13100343\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo H, Zhu W, Fan RJ, et al. Evaluation of the clinical value of CCTA as the preferred screening method in patients with chronic coronary syndrome. BMC Cardiovasc Disord. 2025;25(1):130. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12872-025-04587-x\u003c/span\u003e\u003cspan address=\"10.1186/s12872-025-04587-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBell JS, Weir-McCall J, Nicol E, et al. Plaque quantification from coronary computed tomography angiography in predicting cardiovascular events: A systematic review and meta-analysis. J Cardiovasc Comput Tomogr. 2025;19(4):423\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jcct.2025.05.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jcct.2025.05.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKj\u0026oslash;ller-Hansen L, Maehara A, Kelb\u0026aelig;k H, et al. Impact of Lipidic Plaque on In-Stent and Stent Edge\u0026ndash;Related Events After PCI in Myocardial Infarction: A PROSPECT II Substudy. Circ Cardiovasc Interv. 2024;17(10):e014215. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIRCINTERVENTIONS.124.014215\u003c/span\u003e\u003cspan address=\"10.1161/CIRCINTERVENTIONS.124.014215\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGall E, Pezel T, Toupin S, et al. Prognostic value of coronary plaque composition in symptomatic patients with obstructive coronary artery disease. Eur Radiol. 2025;35(7):3937\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00330-025-11353-2\u003c/span\u003e\u003cspan address=\"10.1007/s00330-025-11353-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDawson LP, Layland J. High-risk coronary plaque features: a narrative review. Cardiol Ther. 2022;11(3):319\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s40119-022-00271-9\u003c/span\u003e\u003cspan address=\"10.1007/s40119-022-00271-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakamoto A, Suwa K, Kawakami R, et al. Significance of Intra-plaque Hemorrhage for the Development of High-Risk Vulnerable Plaque: Current Understanding from Basic to Clinical Points of View. Int J Mol Sci. 2023;24(17):13298. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms241713298\u003c/span\u003e\u003cspan address=\"10.3390/ijms241713298\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMortensen MB, Dzaye O, Steffensen FH, et al. Impact of plaque burden versus stenosis on ischemic events in patients with coronary atherosclerosis. J Am Coll Cardiol. 2020;76(24):2803\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jacc.2020.10.021\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2020.10.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBudoff MJ, Kinninger A, Gransar H, et al. When Does a Calcium Score Equate to Secondary Prevention? Insights From the Multinational CONFIRM Registry. JACC Cardiovasc Imaging. 2023;16(9):1181\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jcmg.2023.03.008\u003c/span\u003e\u003cspan address=\"10.1016/j.jcmg.2023.03.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu W, Sun XX, Pan Y, et al. Predictive value of change in percent calcified plaque burden based on serial coronary computed tomographic angiography for cardiovascular events. Quant Imaging Med Surg. 2025;15(4):3401\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21037/qims-24-1846\u003c/span\u003e\u003cspan address=\"10.21037/qims-24-1846\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Y, Qi H, Zhang X, et al. Predictive value of CCTA-based pericoronary adipose tissue imaging for major adverse cardiovascular events. Acad Radiol. 2025;32(1):91\u0026ndash;101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.acra.2024.08.022\u003c/span\u003e\u003cspan address=\"10.1016/j.acra.2024.08.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDahdal J, Jukema RA, Maaniitty T, et al. CCTA-Derived coronary plaque burden offers enhanced prognostic value over CAC scoring in suspected CAD patients. Eur Heart J Cardiovasc Imaging. 2025;26(6):945\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ehjci/jeaf093\u003c/span\u003e\u003cspan address=\"10.1093/ehjci/jeaf093\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBar S, Maaniitty T, Nabeta T, et al. Artificial intelligence-based automated plaque quantification from CCTA to predict acute coronary syndrome on top of obstructive or ischemic coronary artery disease. Eur Heart J. 2024;45(Supplement1):ehae666\u0026ndash;172. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurheartj/ehae666.172\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehae666.172\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVatsa N, Faaborg-Andersen C, DONG T, et al. Coronary atherosclerotic plaque burden assessment by computed tomography and its clinical implications. Circ Cardiovasc Imaging. 2024;17(8):e016443. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIRCIMAGING.123.016443\u003c/span\u003e\u003cspan address=\"10.1161/CIRCIMAGING.123.016443\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNicholls SJ, Kataoka Y, Nissen SE, et al. Effect of Evolocumab on Coronary Plaque Phenotype and Burden in Statin-Treated Patients Following Myocardial Infarction. JACC Cardiovasc Imaging. 2022;15(7):1308\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jcmg.2022.03.002\u003c/span\u003e\u003cspan address=\"10.1016/j.jcmg.2022.03.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNurmohamed NS, Min JK, Anthopolos R, et al. Atherosclerosis quantification and cardiovascular risk: the ISCHEMIA trial. Eur Heart J. 2024;45(36):3735\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurheartj/ehae471\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehae471\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdelwahed YS, Nelles G, Frick C, et al. Coexistence of calcified- and lipid-containing plaque components and their association with incidental rupture points in acute coronary syndrome-causing culprit lesions: results from the prospective OPTICO-ACS study. Eur Heart J Cardiovasc Imaging. 2022;23(12):1598\u0026ndash;605. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ehjci/jeab247\u003c/span\u003e\u003cspan address=\"10.1093/ehjci/jeab247\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOnnis C, Virmani R, Kawai K, et al. Coronary Artery Calcification: Current Concepts and Clinical Implications. Circulation. 2024;149(3):251\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIRCULATIONAHA.123.065657\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.123.065657\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYong Y, Giovannucci J, Pang SN, et al. Coronary Artery Calcium Density and Risk of Cardiovascular Events: A Systematic Review and Meta-Analysis. JACC Cardiovasc Imaging. 2025;18(3):294\u0026ndash;304. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jcmg.2024.07.024\u003c/span\u003e\u003cspan address=\"10.1016/j.jcmg.2024.07.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu C, Yang F, Hu Y, et al. Relationship between coronary artery calcification and plaque vulnerability, a qualitative and quantitative optical coherence tomography study. Int J Cardiol. 2025;440:133707. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijcard.2025.133707\u003c/span\u003e\u003cspan address=\"10.1016/j.ijcard.2025.133707\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGallone G, Bellettini M, Gatti M, et al. Coronary plaque characteristics associated with major adverse cardiovascular events in atherosclerotic patients and lesions: a systematic review and meta-analysis. Cardiovasc Imaging. 2023;16(12):1584\u0026ndash;604. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jcmg.2023.08.006\u003c/span\u003e\u003cspan address=\"10.1016/j.jcmg.2023.08.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatti G, Grisafi L, Azzolina D, et al. Modifications of coronary plaque phenotype on lipid-lowering therapies and risk of cardiovascular events: a systematic review and meta-regression analysis. Atherosclerosis. 2025;120433. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.atherosclerosis.2025.120433\u003c/span\u003e\u003cspan address=\"10.1016/j.atherosclerosis.2025.120433\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark HB, Arsanjani R, Sung JM, et al. Impact of statins based on high-risk plaque features on coronary plaque progression in mild stenosis lesions: results from the PARADIGM study. Eur Heart J Cardiovasc Imaging. 2023;24(11):1536\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ehjci/jead110\u003c/span\u003e\u003cspan address=\"10.1093/ehjci/jead110\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB\u0026auml;r S, Knuuti J, Saraste A, et al. Derivation and validation of an artificial intelligence-based plaque burden safety cut-off for long-term acute coronary syndrome from coronary computed tomography angiography. Eur Heart J Cardiovasc Imaging. 2025;26(7):1163\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ehjci/jeaf121\u003c/span\u003e\u003cspan address=\"10.1093/ehjci/jeaf121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeuchtner GM, Lacaita PG, Bax JJ, et al. AI-Quantitative CT Coronary Plaque Features Associate With a Higher Relative Risk in Women: CONFIRM2 Registry. Circ Cardiovasc Imaging. 2025;18(6):e018235. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIRCIMAGING.125.018235\u003c/span\u003e\u003cspan address=\"10.1161/CIRCIMAGING.125.018235\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTummala R, Han D, Friedman J, et al. Association between plaque localization in proximal coronary segments and MACE outcomes in patients with mild CAC: results from the EISNER study. Am J Prev Cardiol. 2022;12:100423. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ajpc.2022.100423\u003c/span\u003e\u003cspan address=\"10.1016/j.ajpc.2022.100423\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBax AM, Yoon YE, Gianni U, et al. Vessel-specific plaque features on coronary computed tomography angiography among patients of varying atherosclerotic cardiovascular disease risk. Eur Heart J Cardiovasc Imaging. 2022;23(9):1171\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ehjci/jeac029\u003c/span\u003e\u003cspan address=\"10.1093/ehjci/jeac029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBax AM, Lin FY, van Rosendael AR, et al. Marked variation in atherosclerotic plaque progression between the major epicardial coronary arteries. Eur Heart J Cardiovasc Imaging. 2022;23(11):1482\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ehjci/jeac044\u003c/span\u003e\u003cspan address=\"10.1093/ehjci/jeac044\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiesen A, Mouselimis D, Weichsel L, et al. Pericoronary adipose tissue attenuation is associated with non-calcified plaque burden in patients with chronic coronary syndromes. J Cardiovasc Comput Tomogr. 2023;17(6):384\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jcct.2023.08.008\u003c/span\u003e\u003cspan address=\"10.1016/j.jcct.2023.08.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan N, Marwick TH, Dey D, et al. Association of Pericoronary Adipose Attenuation With Major Adverse Cardiovascular Events and High-Risk Plaque. JACC Cardiovasc Imaging. 2025;18(8):884\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jcmg.2025.04.008\u003c/span\u003e\u003cspan address=\"10.1016/j.jcmg.2025.04.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Coronary computed tomography angiography, Computed tomography fractional flow reserve, Major adverse cardiovascular events, Coronary artery plaque","lastPublishedDoi":"10.21203/rs.3.rs-9321407/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9321407/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eRationale and Objective:\u003c/h2\u003e \u003cp\u003eThe objectives of the present study were to determine the plaque parameters and computed tomography fractional flow reserve (CT-FFR) derived from artificial intelligence (AI)-assisted coronary computed tomography angiography, calculate the safety threshold of plaque burden, and investigate the efficacy of the combination of these two metrics for predicting major adverse cardiovascular events (MACEs).\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eA total of 381 patients with coronary heart disease were included in the study, and plaque parameters and CT-FFR data were collected. Plaque parameter risk cutoff values that predict MACEs were obtained through subject operating characteristic curves. Patients were followed up, and univariate and multivariate Cox regression analyses were performed. Kaplan‒Meier survival curves were plotted, and a plaque model, CT-FFR model, and plaque combined CT-FFR model were established to predict MACEs. A LASSO-Cox model and Cox survival neural network model were constructed for MACE prediction.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe calcified plaque volume, noncalcified plaque volume (NCPV), total plaque volume, calcified percent atheroma volume, noncalcified percent atheroma volume (NCPAV), and total percent atheroma volume correlated with the occurrence of MACEs. Multivariate Cox regression revealed that total-NCPV and total-NCPAV were the strongest predictors of MACEs [total-NCPV \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{H}\\text{R}:\\:4.752,\\:95\\text{\\%}\\:\\text{C}\\text{I}:2.708-8.340,\\text{P}\u0026lt;0.001\\)\u003c/span\u003e\u003c/span\u003e and total-NCPAV (HR 5.073, 95% CI: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:2.930-8.786\\)\u003c/span\u003e\u003c/span\u003e, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)]. The C indices of the LASSO-Cox model and Cox survival neural network model for predicting MACEs were 0.747 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:95\\text{\\%}\\:\\text{C}\\text{I}:0.674-0.816,\\text{P}\u0026lt;0.001\\)\u003c/span\u003e\u003c/span\u003e) and 0.730 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:95\\text{\\%}\\:\\text{C}\\text{I}:0.628-0.833\\)\u003c/span\u003e\u003c/span\u003e), respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe combination of AI-assisted measured quantitative plaque parameters and CT-FFR has high clinical value for the prediction of MACEs, among which the RCA plaque burden is the most critical factor for predicting MACEs.\u003c/p\u003e","manuscriptTitle":"Application Value of AI-Assisted Quantitative Plaque Parameters Combined with CT-FFR in Predicting Major Adverse Cardiovascular Events","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-26 17:17:25","doi":"10.21203/rs.3.rs-9321407/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-28T08:32:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T07:47:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T13:26:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T05:41:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189382740896950476102202316082848736086","date":"2026-04-20T09:05:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272993682560694384049929685142519778463","date":"2026-04-17T15:25:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5915082568055955087706993326174645844","date":"2026-04-17T15:01:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-17T14:36:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-07T12:46:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-07T09:17:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-07T09:16:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2026-04-04T15:01:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9ff0fe37-a57d-4177-aab3-59047eece196","owner":[],"postedDate":"April 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T08:40:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-26 17:17:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9321407","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9321407","identity":"rs-9321407","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}