One hundred and forty-seven consecutive patients with suspected CAD enrolled in the PERFECTION study [14, 15] were retrospectively analyzed for this study. Patients with a history of previous myocardial infarction, acute coronary syndrome, previous revascularization, impaired renal function or contraindication to administration of contrast agent, pregnancy, cardiac arrhythmias, inability to sustain breath hold, BMI> 35 and contraindication to administer B-blockers and nitrates were excluded from the study as previously described [14, 15]. The institutional ethical committee approved the protocol and all patients signed an informed consent.
Patients were asked to refrain from caffeine and smoking for 24 hours and fasting 6 hours prior the CTCA examination.
During rest CCTA, in patients with heart rate > 65 beats/min (bpm), metoprolol with a titration dose up to 15 mg was administered intravenously in order to obtain a HR ≤65 bpm.
Before the rest scan, all patients received sublingual nitrates to ensure coronary vasodilatation.
All examinations were performed using a Revolution CT scanner (GE Healthcare, Milwaukee, Wisconsin) following the guidelines of the Society of Cardiovascular Computed Tomography (SCCT). The Rest CCTA parameters have been described previously .
Based on HR, CCTA was acquired in 70-80% of the cardiac cycle in patients with HR ≤65 bpm, while in patients with > 65 bpm, CCTA was acquired between 40-80% of cardiac cycle. CCTA images were acquired after the injection of 70 ml of iodine contrast agent (Visipaque 320 mg/ml) at 6.2 ml/sec followed by 50 ml of saline at the same flow rate of contrast agent.
All scans were performed using the bolus tracking technique with visual assessment to determine the correct timing for acquisition.
All images were reconstructed using an adaptative statistical iterative reconstruction (ASIR-V, GE Healthcare, Milwaukee, Wisconsin). Datasets of CCTA were transferred to an image-processing workstation (Advantage Workstation 4.7, GE Healthcare, Milwaukee, Wisconsin) to perform quantitative coronary analysis according to SCCT guidelines for reporting by two certified expert readers with more than 8 years of experience in cardiovascular imaging following the European Association Cardiovascular Imaging (EACVI) guidelines for training and certification . In case of disagreement, a third cardiac radiologist evaluated the images.
The patient underwent intravenous administration of adenosine (0.14 mg/Kg/min) over 4 minutes. At the end of the third minute of adenosine administration, a CCTA was acquired using the same technical parameters previously described . Subsequently, images were transferred to an offline workstation (Advantage Workstation Version 4.7, GE Healthcare), reconstructed on short axis and long axis with an average slice thickness between 4-8mm using a narrow window width and level of 350 W and 150 L, respectively. Perfusion datasets were analyzed in consensus by two certified expert readers, blinded to clinical history and CCTA findings, with more than 8 years of experience in cardiovascular imaging following the EACVI guidelines for training and certification . In case of disagreement, a third reader evaluated the images.
Combined CCTA+CTPStress interpretation
Coronary arteries at CCTA were segmented according to the American Heart Association (AHA) model . Obstructive CAD was defined as coronary stenosis > 50%.
CTPStress was evaluated according to the AHA myocardial segmentation  model. Stress positivity was defined as any subendocardial hypo-enhancement extending more than 25% of transmurality in a specific territory that was not present at rest CCTA.
Matching between CCTA and CTPStress findings was performed according to the algorithm previously described [14, 20, 21].
Briefly, the adjudication process was applied each time there was a coronary arterial lesion with >50% diameter stenosis and at least 1 myocardial perfusion defect in the matched myocardial segment.
Time of analysis of image reporting for CTPStress was recorded.
ICA and invasive FFR performance and interpretation
All patients underwent ICA following the guidelines of the American College of Cardiology/AHA Task Force on Practice Guidelines and the Society for Cardiac Angiography and Interventions . The coronary arteries were classified according to the AHA Classification system . An interventional cardiologist blinded to results of CCTA analyzed ICA images with quantitative coronary angiography (QCA) (QantCor QCA; Pie Medical Imaging, Maastricht, the Netherlands).
Coronary stenosis was evaluated calculating the percentage of narrowing using the minimum diameter and reference diameter. In the presence of stenosis ranging between 30% and 80%, invasive FFR was calculated . In order to calculate FFR, the pressure wire (Certus Pressure Wire, St. Jude Medical Systems, St. Paul, MI) was equalized with the aortic pressure and subsequently was placed distal to the stenosis in the distal third of the coronary artery with stenosis. An injection of 100 mg of glyceryl trinitrate was injected intracoronary in order to prevent vasospasm while 140 mg/Kg/min of adenosine was administered intravenously in order to create an inducible stress. FFR was assessed at the peak of hyperemia using the RadiAnalyzer Xpress (Radi Medical Systems, Uppsala, Sweden) by dividing the mean coronary pressure, measured with the pressure sensor placed distal to the stenosis, by the mean aortic pressure measured through the guide catheter. Intermediate stenoses showing values of invasive FFR ≤0.8 or anatomical stenoses showing >80% diameter reduction or total occlusions were considered functionally significant.
Deep Learning analysis
We selected 112 patients from the 147 studied; 58 with invasive FFR ≤ 0.8 or stenosis > 80% (Group 0) and 54 with invasive FFR > 0.8 (Group 1). Thirty five patients were excluded due to poor image quality or an inconsistent dataset. First, we randomly split the dataset in two subsets: an "independent test set", generated selecting 18 and 14 samples from Group 0 and Group 1, respectively, and a "learning set" composed of the remaining 80. To limit the effect of overfitting during the training step, a data augmentation step was applied. At the end, the learning set consisted of 80 group 0 and 80 group 1 samples. The learning set was used to train the algorithms and to identify its best configuration of parameters and hyper-parameters; conversely, the independent test set was used to assess the performance.
Each dataset (rest and stress) scan was composed of 256 or 224 slices (512 x 512 pixels) and stored as a DICOM. To focus our attention on the left ventricle, we manually defined a region of interest (a 250-by-250 px window) around the left ventricle, allowing a decrease of computational burden and improving the overall network performance. Finally, the resulting slices were joined in a 10-by-10 squared image, resized to 1280 x 1280 pixels.
Convolutional neural network (CNN) architecture
The keras package has been used to build and test the CNN. The convolutional section of the network consisted of four consecutive layer blocks, each one containing a single convolutional layer and a max-pooling layer. A densely connected network with 4 hidden layers (512, 256, 64 and 32 neurons) and the output layer followed the convolutional section. To handle overfitting a dropout strategy was implemented before the first hidden layer (dropout rate: 0.2) . The ReLU activation function was used for each neuron (densely connected and convolutional ones), except for the output node (activation function: ‘sigmoid’). Finally, we set the ‘Stochastic Gradient Descend’ optimizer to minimize the ‘binary cross entropy’ loss function, the number of max epochs = 500 and the batch size = 20.
The CNN was trained in order to predict the ischemic myocardium by using both rest (CTP-DLRest) and stress (CTP-DLStress) datasets based on the values of invasive FFR. The time of analysis for the evaluation of algorithms for both CTP-DLRest and CTP-DLStress were recorded.
Statistical analysis was performed using SPSS version 21.0 software (SPSS, Chicago, Illinois) and R version 4.0.2 (R Foundation for statistical Computing, Vienna, Austria).
Continuous variables are presented as means ± SD, while categorical data are reported as frequencies and percentages and compared with T-test Student or Chi-squared test, respectively. Association between variables were assessed by Pearson's correlation coefficient (R).
Patient-based sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), diagnostic accuracy and area under the curve (AUC) were measured for CCTA, CCTA+CTPStress, CCTA+CTP-DLRest and CCTA+CTP-DLStress on the independent test set and DeLong method used to compare the differences in terms of AUC between all approaches.
Time of analysis between human and DL algorithm for both CTP-DLrest and CTP-DLStress were compared.
Differences were deemed significant if the p-value was < 0.05.