Radiomics for the detection of diffusely impaired myocardial perfusion: A proof-of-concept study using 13N-ammonia positron emission tomography

AIM The current proof-of-concept study investigates the value of radiomic features from normal 13N-ammonia positron emission tomography (PET) myocardial retention images to identify patients with reduced global myocardial flow reserve (MFR). METHODS Data from 100 patients with normal retention 13N-ammonia PET scans were divided into two groups, according to global MFR (i.e., < 2 and ≥ 2), as derived from quantitative PET analysis. We extracted radiomic features from retention images at each of five different gray-level (GL) discretization (8, 16, 32, 64, and 128 bins). Outcome independent and dependent feature selection and subsequent univariate and multivariate analyses was performed to identify image features predicting reduced global MFR. RESULTS A total of 475 radiomic features were extracted per patient. Outcome independent and dependent feature selection resulted in a remainder of 35 features. Discretization at 16 bins (GL16) yielded the highest number of significant predictors of reduced MFR and was chosen for the final analysis. GLRLM_GLNU was the most robust parameter and at a cut-off of 948 yielded an accuracy, sensitivity, specificity, negative and positive predictive value of 67%, 74%, 58%, 64%, and 69%, respectively, to detect diffusely impaired myocardial perfusion. CONCLUSION A single radiomic feature (GLRLM_GLNU) extracted from visually normal 13N-ammonia PET retention images independently predicts reduced global MFR with moderate accuracy. This concept could potentially be applied to other myocardial perfusion imaging modalities based purely on relative distribution patterns to allow for better detection of diffuse disease

High-Speed Onsite Deep-Learning Based FFR-CT Algorithm: Evaluation Using Invasive Angiography as Reference Standard

Background: Estimation of fractional flow reserve (FFR) from coronary CTA (FFR-CT) is an established method to assess coronary lesions' hemodynamic significance. However, clinical implementation has progressed slowly, partly related to offsite data transfer with long turnaround times while awaiting results. Objectives: We aimed to evaluate the diagnostic performance of FFR-CT computed onsite with a high-speed deep-learning based algorithm, using invasive hemodynamic indices as reference standard. Methods: This retrospective study included 59 patients (46 men, 13 women; mean age 66.5±10.2 years) who underwent coronary CTA (including calcium scoring) followed within 90 days by invasive angiography with invasive FFR and/or instantaneous wave-free ratio (iwFR) measurements from December 2014 to October 2021. Coronary artery lesions were considered to show hemodynamically significant stenosis in presence of invasive FFR ≤0.80 and/or iwFR ≤0.89. A single cardiologist evaluated CTA images using an onsite deep-learning based semiautomated algorithm employing a 3D computational flow dynamics model to determine FFR-CT for coronary artery lesions detected by invasive angiography. Time for FFR-CT analysis was recorded. FFR-CT analysis was repeated by the same cardiologist in 26 randomly selected examinations, and by a different cardiologist in 45 randomly selected examinations. Diagnostic performance and agreement were assessed. Results: Invasive angiography identified 74 lesions. FFR-CT and invasive FFR showed strong correlation (r=0.81), and, in Bland-Altman analysis, showed bias of 0.01 and 95% limits of agreement of -0.13 to +0.15. FFR-CT had AUC for hemodynamically significant stenosis of 0.975. At cutoff of ≤0.80, FFR-CT had accuracy of 95.9%, sensitivity of 93.5%, and specificity of 97.7%. In 39 lesions with severe calcifications (≥400 Agatston units), FFR-CT had AUC of 0.991, with cutoff of ≤0.80 yielding sensitivity of 94.7%, specificity of 95.0%, and accuracy of 94.9%. Mean analysis time per patient was 7 minutes 54 seconds. Interobserver and intraobserver agreement were good-to-excellent (intraclass correlation coefficient, 0.944 and 0.854; bias -0.01 and -0.01; 95% limits of agreement, -0.08 to +0.07, and -0.12 and +0.10, respectively). Conclusion: A high-speed onsite deep-learning based FFR-CT algorithm showed excellent diagnostic performance for hemodynamically significant stenosis, with high reproducibility. Clinical Impact: The algorithm should facilitate the FFR-CT technology's implementation into routine clinical practice