Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction.

Background: While low-dose computed tomography scans are traditionally used for attenuation correction in hybrid myocardial perfusion imaging (MPI), they also contain additional anatomic and pathologic information not utilized in clinical assessment. We seek to uncover the full potential of these scans utilizing a holistic artificial intelligence (AI)-driven image framework for image assessment. Methods: Patients with SPECT/CT MPI from 4 REFINE SPECT registry sites were studied. A multi-structure model segmented 33 structures and quantified 15 radiomics features for each on CT attenuation correction (CTAC) scans. Coronary artery calcium and epicardial adipose tissue scores were obtained from separate deep-learning models. Normal standard quantitative MPI features were derived by clinical software. Extreme Gradient Boosting derived all-cause mortality risk scores from SPECT, CT, stress test, and clinical features utilizing a 10-fold cross-validation regimen to separate training from testing data. The performance of the models for the prediction of all-cause mortality was evaluated using area under the receiver-operating characteristic curves (AUCs). Results: Of 10,480 patients, 5,745 (54.8%) were male, and median age was 65 (interquartile range [IQR] 57-73) years. During the median follow-up of 2.9 years (1.6-4.0), 651 (6.2%) patients died. The AUC for mortality prediction of the model (combining CTAC, MPI, and clinical data) was 0.80 (95% confidence interval [0.74-0.87]), which was higher than that of an AI CTAC model (0.78 [0.71-0.85]), and AI hybrid model (0.79 [0.72-0.86]) incorporating CTAC and MPI data (p<0.001 for all). Conclusion: In patients with normal perfusion, the comprehensive model (0.76 [0.65-0.86]) had significantly better performance than the AI CTAC (0.72 [0.61-0.83]) and AI hybrid (0.73 [0.62-0.84]) models (p<0.001, for all).CTAC significantly enhances AI risk stratification with MPI SPECT/CT beyond its primary role - attenuation correction. A comprehensive multimodality approach can significantly improve mortality prediction compared to MPI information alone in patients undergoing cardiac SPECT/CT.

AI-Defined Cardiac Anatomy Improves Risk Stratification of Hybrid Perfusion Imaging.

BACKGROUND: Computed tomography attenuation correction (CTAC) improves perfusion quantification of hybrid myocardial perfusion imaging by correcting for attenuation artifacts. Artificial intelligence (AI) can automatically measure coronary artery calcium (CAC) from CTAC to improve risk prediction but could potentially derive additional anatomic features. OBJECTIVES: The authors evaluated AI-based derivation of cardiac anatomy from CTAC and assessed its added prognostic utility. METHODS: The authors considered consecutive patients without known coronary artery disease who underwent single-photon emission computed tomography/computed tomography (CT) myocardial perfusion imaging at 3 separate centers. Previously validated AI models were used to segment CAC and cardiac structures (left atrium, left ventricle, right atrium, right ventricular volume, and left ventricular [LV] mass) from CTAC. They evaluated associations with major adverse cardiovascular events (MACEs), which included death, myocardial infarction, unstable angina, or revascularization. RESULTS: In total, 7,613 patients were included with a median age of 64 years. During a median follow-up of 2.4 years (IQR: 1.3-3.4 years), MACEs occurred in 1,045 (13.7%) patients. Fully automated AI processing took an average of 6.2 ± 0.2 seconds for CAC and 15.8 ± 3.2 seconds for cardiac volumes and LV mass. Patients in the highest quartile of LV mass and left atrium, LV, right atrium, and right ventricular volume were at significantly increased risk of MACEs compared to patients in the lowest quartile, with HR ranging from 1.46 to 3.31. The addition of all CT-based volumes and CT-based LV mass improved the continuous net reclassification index by 23.1%. CONCLUSIONS: AI can automatically derive LV mass and cardiac chamber volumes from CT attenuation imaging, significantly improving cardiovascular risk assessment for hybrid perfusion imaging.

Artificial Intelligence Predicts Hospitalization for Acute Heart Failure Exacerbation in Patients Undergoing Myocardial Perfusion Imaging.

Heart failure (HF) is a leading cause of morbidity and mortality in the United States and worldwide, with a high associated economic burden. This study aimed to assess whether artificial intelligence models incorporating clinical, stress test, and imaging parameters could predict hospitalization for acute HF exacerbation in patients undergoing SPECT/CT myocardial perfusion imaging. Methods: The HF risk prediction model was developed using data from 4,766 patients who underwent SPECT/CT at a single center (internal cohort). The algorithm used clinical risk factors, stress variables, SPECT imaging parameters, and fully automated deep learning-generated calcium scores from attenuation CT scans. The model was trained and validated using repeated hold-out (10-fold cross-validation). External validation was conducted on a separate cohort of 2,912 patients. During a median follow-up of 1.9 y, 297 patients (6%) in the internal cohort were admitted for HF exacerbation. Results: The final model demonstrated a higher area under the receiver-operating-characteristic curve (0.87 ± 0.03) for predicting HF admissions than did stress left ventricular ejection fraction (0.73 ± 0.05, P < 0.0001) or a model developed using only clinical parameters (0.81 ± 0.04, P < 0.0001). These findings were confirmed in the external validation cohort (area under the receiver-operating-characteristic curve: 0.80 ± 0.04 for final model, 0.70 ± 0.06 for stress left ventricular ejection fraction, 0.72 ± 0.05 for clinical model; P < 0.001 for all). Conclusion: Integrating SPECT myocardial perfusion imaging into an artificial intelligence-based risk assessment algorithm improves the prediction of HF hospitalization. The proposed method could enable early interventions to prevent HF hospitalizations, leading to improved patient care and better outcomes.

Automated vessel specific coronary artery calcification quantification with deep learning in a large multi-center registry.

AIMS: Vessel specific coronary artery calcification (CAC) is additive to global CAC for prognostic assessment. We assessed accuracy and prognostic implications of vessel-specific automated deep learning (DL) CAC analysis on electrocardiogram gated and attenuation correction computed tomography (CT) in a large multicenter registry. METHODS AND RESULTS: Vessel-specific CAC was assessed in the left main/left anterior descending (LM/LAD), left circumflex (LCX) and right coronary artery (RCA) using a DL model trained on 3000 gated CT and tested on 2094 gated CT and 5969 non-gated attenuation correction CT. Vessel-specific agreement was assessed with linear weighted Cohen's Kappa for CAC zero, 1-100, 101-400 and >400 Agatston units (AU). Risk of major adverse cardiovascular events (MACE) was assessed during 2.4±1.4 years follow-up, with hazard ratios (HR) and 95% confidence intervals (CI). There was strong to excellent agreement between DL and expert ground truth for CAC in LM/LAD, LCX and RCA on gated CT [0.90 (95% CI 0.89 to 0.92); 0.70 (0.68 to 0.73); 0.79 (0.77 to 0.81)] and attenuation correction CT [(0.78 (0.77 to 0.80); 0.60 (0.58 to 0.62); 0.70 (0.68 to 0.71)]. MACE occurred in 242 (12%) undergoing gated CT and 841(14%) of undergoing attenuation correction CT. LM/LAD CAC >400 AU was associated with the highest risk of MACE on gated (HR 12.0, 95% CI 7.96, 18.0, p<0.001) and attenuation correction CT (HR 4.21, 95% CI 3.48, 5.08, p<0.001). CONCLUSION: Vessel-specific CAC assessment with DL can be performed accurately and rapidly on gated CT and attenuation correction CT and provides important prognostic information.

AI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging.

Epicardial adipose tissue (EAT) volume and attenuation are associated with cardiovascular risk, but manual annotation is time-consuming. We evaluated whether automated deep learning-based EAT measurements from ungated computed tomography (CT) are associated with death or myocardial infarction (MI). We included 8781 patients from 4 sites without known coronary artery disease who underwent hybrid myocardial perfusion imaging. Of those, 500 patients from one site were used for model training and validation, with the remaining patients held out for testing (n = 3511 internal testing, n = 4770 external testing). We modified an existing deep learning model to first identify the cardiac silhouette, then automatically segment EAT based on attenuation thresholds. Deep learning EAT measurements were obtained in <2 s compared to 15 min for expert annotations. There was excellent agreement between EAT attenuation (Spearman correlation 0.90 internal, 0.82 external) and volume (Spearman correlation 0.90 internal, 0.91 external) by deep learning and expert segmentation in all 3 sites (Spearman correlation 0.90-0.98). During median follow-up of 2.7 years (IQR 1.6-4.9), 565 patients experienced death or MI. Elevated EAT volume and attenuation were independently associated with an increased risk of death or MI after adjustment for relevant confounders. Deep learning can automatically measure EAT volume and attenuation from low-dose, ungated CT with excellent correlation with expert annotations, but in a fraction of the time. EAT measurements offer additional prognostic insights within the context of hybrid perfusion imaging.

Comparison of the prognostic value between quantification and visual estimation of coronary calcification from attenuation CT in patients undergoing SPECT myocardial perfusion imaging.

We investigated the prognostic utility of visually estimated coronary artery calcification (VECAC) from low dose computed tomography attenuation correction (CTAC) scans obtained during SPECT/CT myocardial perfusion imaging (MPI), and assessed how it compares to coronary artery calcifications (CAC) quantified by calcium score on CTACs (QCAC). From the REFINE SPECT Registry 4,236 patients without prior coronary stenting with SPECT/CT performed at a single center were included (age: 64 ± 12 years, 47% female). VECAC in each coronary artery (left main, left anterior descending, circumflex, and right) were scored separately as 0 (absent), 1 (mild), 2 (moderate), or 3 (severe), yielding a possible score of 0-12 for each patient (overall VECAC grade zero:0, mild:1-2, moderate: 3-5, severe: >5). CAC scoring of CTACs was performed at the REFINE SPECT core lab with dedicated software. VECAC was correlated with categorized QCAC (zero: 0, mild: 1-99, moderate: 100-399, severe: ≥400). A high degree of correlation was observed between VECAC and QCAC, with 73% of VECACs in the same category as QCAC and 98% within one category. There was substantial agreement between VECAC and QCAC (weighted kappa: 0.78 with 95% confidence interval: 0.76-0.79, p < 0.001). During a median follow-up of 25 months, 372 patients (9%) experienced major adverse cardiovascular events (MACE). In survival analysis, both VECAC and QCAC were associated with MACE. The area under the receiver operating characteristic curve for 2-year-MACE was similar for VECAC when compared to QCAC (0.694 versus 0.691, p = 0.70). In conclusion, visual assessment of CAC on low-dose CTAC scans provides good estimation of QCAC in patients undergoing SPECT/CT MPI. Visually assessed CAC has similar prognostic value for MACE in comparison to QCAC.

CT attenuation correction improves quantitative risk prediction by cardiac SPECT in obese patients.

PURPOSE: This study aimed to compare the predictive value of CT attenuation-corrected stress total perfusion deficit (AC-sTPD) and non-corrected stress TPD (NC-sTPD) for major adverse cardiac events (MACE) in obese patients undergoing cadmium zinc telluride (CZT) SPECT myocardial perfusion imaging (MPI). METHODS: The study included 4,585 patients who underwent CZT SPECT/CT MPI for clinical indications (chest pain: 56%, shortness of breath: 13%, other: 32%) at Yale New Haven Hospital (age: 64 ± 12 years, 45% female, body mass index [BMI]: 30.0 ± 6.3 kg/m2, prior coronary artery disease: 18%). The association between AC-sTPD or NC-sTPD and MACE defined as the composite end point of mortality, nonfatal myocardial infarction or late coronary revascularization (> 90 days after SPECT) was evaluated with survival analysis. RESULTS: During a median follow-up of 25 months, 453 patients (10%) experienced MACE. In patients with BMI ≥ 35 kg/m2 (n = 931), those with AC-sTPD ≥ 3% had worse MACE-free survival than those with AC-sTPD < 3% (HR: 2.23, 95% CI: 1.40 - 3.55, p = 0.002) with no difference in MACE-free survival between patients with NC-sTPD ≥ 3% and NC-sTPD < 3% (HR:1.06, 95% CI:0.67 - 1.68, p = 0.78). AC-sTPD had higher AUC than NC-sTPD for the detection of 2-year MACE in patients with BMI ≥ 35 kg/m2 (0.631 versus 0.541, p = 0.01). In the overall cohort AC-sTPD had a higher ROC area under the curve (AUC, 0.641) than NC-sTPD (0.608; P = 0.01) for detection of 2-year MACE. In patients with BMI ≥ 35 kg/m2 AC sTPD provided significant incremental prognostic value beyond NC sTPD (net reclassification index: 0.14 [95% CI: 0.20 - 0.28]). CONCLUSIONS: AC sTPD outperformed NC sTPD in predicting MACE in patients undergoing SPECT MPI with BMI ≥ 35 kg/m2. These findings highlight the superior prognostic value of AC-sTPD in this patient population and underscore the importance of CT attenuation correction.

Subfunctionalization of Parental Polyamine Oxidase (PAO) Genes in the Allopolyploid Tobacco Nicotiana tabacum (L.).

Polyamines play an important role in developmental and environmental stress responses in plants. Polyamine oxidases (PAOs) are flavin-adenine-dinucleotide-dependent enzymes associated with polyamine catabolism. In this study, 14 genes were identified in the tobacco genome that code for PAO proteins being named based on their sequence homology with Arabidopsis PAOs (AtPAO1-5): NtPAO1A-B; NtPAO2A-C, NtPAO4A-D, and NtPAO5A-E. Sequence analysis confirmed that the PAO gene family of the allopolyploid hybrid Nicotiana tabacum is not an exact combination of the PAO genes of the maternal Nicotiana sylvestris and paternal Nicotiana tomentosiformis ones. The loss of the N. sylvestris homeolog of NtPAO5E and the gain of an extra NtPAO2 copy, likely of Nicotiana othophora origin, was revealed. The latter adds to the few pieces of evidence suggesting that the paternal parent of N. tabacum was an introgressed hybrid of N. tomentosiformis and N. othophora. Gene expression analysis indicated that all 14 PAO genes kept their expression following the formation of the hybrid species. The homeologous gene pairs showed similar or opposite regulation depending on the investigated organ, applied stress, or hormone treatment. The data indicate that the expression pattern of the homeologous genes is diversifying in a process of subfunctionalization.

A Common Molecular Signature Indicates the Pre-Meristematic State of Plant Calli.

In response to different degrees of mechanical injury, certain plant cells re-enter the division cycle to provide cells for tissue replenishment, tissue rejoining, de novo organ formation, and/or wound healing. The intermediate tissue formed by the dividing cells is called a callus. Callus formation can also be induced artificially in vitro by wounding and/or hormone (auxin and cytokinin) treatments. The callus tissue can be maintained in culture, providing starting material for de novo organ or embryo regeneration and thus serving as the basis for many plant biotechnology applications. Due to the biotechnological importance of callus cultures and the scientific interest in the developmental flexibility of somatic plant cells, the initial molecular steps of callus formation have been studied in detail. It was revealed that callus initiation can follow various ways, depending on the organ from which it develops and the inducer, but they converge on a seemingly identical tissue. It is not known, however, if callus is indeed a special tissue with a defined gene expression signature, whether it is a malformed meristem, or a mass of so-called "undifferentiated" cells, as is mostly believed. In this paper, I review the various mechanisms of plant regeneration that may converge on callus initiation. I discuss the role of plant hormones in the detour of callus formation from normal development. Finally, I compare various Arabidopsis gene expression datasets obtained a few days, two weeks, or several years after callus induction and identify 21 genes, including genes of key transcription factors controlling cell division and differentiation in meristematic regions, which were upregulated in all investigated callus samples. I summarize the information available on all 21 genes that point to the pre-meristematic nature of callus tissues underlying their wide regeneration potential.

L-Aminoguanidine Induces Imbalance of ROS/RNS Homeostasis and Polyamine Catabolism of Tomato Roots after Short-Term Salt Exposure.

Polyamine (PA) catabolism mediated by amine oxidases is an important process involved in fine-tuning PA homeostasis and related mechanisms during salt stress. The significance of these amine oxidases in short-term responses to salt stress is, however, not well understood. In the present study, the effects of L-aminoguanidine (AG) on tomato roots treated with short-term salt stress induced by NaCl were studied. AG is usually used as a copper amine oxidase (CuAO or DAO) inhibitor. In our study, other alterations of PA catabolism, such as reduced polyamine oxidase (PAO), were also observed in AG-treated plants. Salt stress led to an increase in the reactive oxygen and nitrogen species in tomato root apices, evidenced by in situ fluorescent staining and an increase in free PA levels. Such alterations were alleviated by AG treatment, showing the possible antioxidant effect of AG in tomato roots exposed to salt stress. PA catabolic enzyme activities decreased, while the imbalance of hydrogen peroxide (H2O2), nitric oxide (NO), and hydrogen sulfide (H2S) concentrations displayed a dependence on stress intensity. These changes suggest that AG-mediated inhibition could dramatically rearrange PA catabolism and related reactive species backgrounds, especially the NO-related mechanisms. More studies are, however, needed to decipher the precise mode of action of AG in plants exposed to stress treatments.

Impaired left-ventricular global longitudinal strain by feature-tracking cardiac MRI predicts mortality in systemic sclerosis.

Impaired left-ventricular (LV) and right-ventricular (RV) cardiac magnetic resonance (CMR) strain has been documented in systemic sclerosis (SSc). However, it is unknown whether the CMR strain is predictive of adverse outcomes in SSc. Therefore, we set out to investigate the prognostic value of CMR strain in SSc. Patients with SSc who underwent CMR for clinical indications between 11/2010 and 07/2020 were retrospectively studied. LV and RV strain was evaluated by feature tracking. The association between strain, late gadolinium enhancement (LGE), and survival was evaluated with time to event and Cox-regression analyses. During the study period, 42 patients with SSc (age: 57 ± 14 years, 83% female, 57% limited cutaneous SSc, SSc duration: 7 ± 8 years) underwent CMR. During the median follow-up of 3.6 years, 11 patients died (26%). Compared to surviving patients, patients who died had significantly worse LV GLS (- 8.2 ± 6.2% versus - 12.1 ± 2.9%, p = 0.03), but no difference in LV global radial, circumferential, or RV strain values. Patients within the quartile of most impaired LV GLS (≥ - 12.8%, n = 10) had worse survival when compared to patients with preserved LV GLS (< - 12.8%, n = 32, log-rank p = 0.02), which persisted after controlling for LV cardiac output, LV cardiac index, reduced LV ejection fraction, or presence of LGE. In addition, patients who had both impaired LV GLS and LGE (n = 5) had worse survival than patients with LGE or impaired GLS alone (n = 14) and compared to those without any of these features (n = 17, p = 0.003). In our retrospective cohort of patients with SSc undergoing CMR for clinical indications, LV GLS and LGE were found to be predictive of overall survival.

The ROP2 GTPase Participates in Nitric Oxide (NO)-Induced Root Shortening in Arabidopsis.

Nitric oxide (NO) is a versatile signal molecule that mediates environmental and hormonal signals orchestrating plant development. NO may act via reversible S-nitrosation of proteins during which an NO moiety is added to a cysteine thiol to form an S-nitrosothiol. In plants, several proteins implicated in hormonal signaling have been reported to undergo S-nitrosation. Here, we report that the Arabidopsis ROP2 GTPase is a further potential target of NO-mediated regulation. The ROP2 GTPase was found to be required for the root shortening effect of NO. NO inhibits primary root growth by altering the abundance and distribution of the PIN1 auxin efflux carrier protein and lowering the accumulation of auxin in the root meristem. In rop2-1 insertion mutants, however, wild-type-like root size of the NO-treated roots were maintained in agreement with wild-type-like PIN1 abundance in the meristem. The ROP2 GTPase was shown to be S-nitrosated in vitro, suggesting that NO might directly regulate the GTPase. The potential mechanisms of NO-mediated ROP2 GTPase regulation and ROP2-mediated NO signaling in the primary root meristem are discussed.

Absence of Arabidopsis Polyamine Oxidase 5 Influences the Cytokinin-Induced Shoot Meristem Formation from Lateral Root Primordia.

Lateral root primordia (LRPs) of Arabidopsis can be directly converted to shoot meristems (SMs) by the application of exogenous cytokinin. Here, we report that Arabidopsis POLYAMINE OXIDASE 5 (AtPAO5) contributes to this process, since the rate of SM formation from LRPs was significantly lower in the pao5-2 knockout mutant. Furthermore, the presented experiments showed that AtPAO5 influences SM formation via controlling the thermospermine (T-Spm) level. Gene expression analyses supported the view that the pao5-2 mutation as well as exogenous T-Spm downregulate the expression of the class 3 haemoglobin coding genes AtGLB1 and AtGLB2. AtGLB1 and 2 have been reported to augment cytokinin sensitivity, indirectly inhibiting the expression of type-A ARABIDOPSIS RESPONSE REGULATORs (ARRs). In agreement, the same ARR-coding genes were found to be upregulated in the pao5-2 mutant. Although GLB proteins might also control cytokinin-induced nitric oxide (NO) accumulation, we could not find experimental evidence for it. Rather, the negative effect of NO-donor treatment on AtPAO5 gene expression and SM formation was seen. Nevertheless, a hypothetical pathway is set up explaining how AtPAO5 may affect direct shoot meristem formation, controlling cytokinin sensitivity through T-Spm and GLBs.

Segmentation-Free PVC for Cardiac SPECT Using a Densely-Connected Multi-Dimensional Dynamic Network.

In nuclear imaging, limited resolution causes partial volume effects (PVEs) that affect image sharpness and quantitative accuracy. Partial volume correction (PVC) methods incorporating high-resolution anatomical information from CT or MRI have been demonstrated to be effective. However, such anatomical-guided methods typically require tedious image registration and segmentation steps. Accurately segmented organ templates are also hard to obtain, particularly in cardiac SPECT imaging, due to the lack of hybrid SPECT/CT scanners with high-end CT and associated motion artifacts. Slight mis-registration/mis-segmentation would result in severe degradation in image quality after PVC. In this work, we develop a deep-learning-based method for fast cardiac SPECT PVC without anatomical information and associated organ segmentation. The proposed network involves a densely-connected multi-dimensional dynamic mechanism, allowing the convolutional kernels to be adapted based on the input images, even after the network is fully trained. Intramyocardial blood volume (IMBV) is introduced as an additional clinical-relevant loss function for network optimization. The proposed network demonstrated promising performance on 28 canine studies acquired on a GE Discovery NM/CT 570c dedicated cardiac SPECT scanner with a 64-slice CT using Technetium-99m-labeled red blood cells. This work showed that the proposed network with densely-connected dynamic mechanism produced superior results compared with the same network without such mechanism. Results also showed that the proposed network without anatomical information could produce images with statistically comparable IMBV measurements to the images generated by anatomical-guided PVC methods, which could be helpful in clinical translation.

Deep Learning-Based Attenuation Correction Improves Diagnostic Accuracy of Cardiac SPECT.

To improve diagnostic accuracy, myocardial perfusion imaging (MPI) SPECT studies can use CT-based attenuation correction (AC). However, CT-based AC is not available for most SPECT systems in clinical use, increases radiation exposure, and is impacted by misregistration. We developed and externally validated a deep-learning model to generate simulated AC images directly from non-AC (NC) SPECT, without the need for CT. Methods: SPECT myocardial perfusion imaging was performed using 99mTc-sestamibi or 99mTc-tetrofosmin on contemporary scanners with solid-state detectors. We developed a conditional generative adversarial neural network that applies a deep learning model (DeepAC) to generate simulated AC SPECT images. The model was trained with short-axis NC and AC images performed at 1 site (n = 4,886) and was tested on patients from 2 separate external sites (n = 604). We assessed the diagnostic accuracy of the stress total perfusion deficit (TPD) obtained from NC, AC, and DeepAC images for obstructive coronary artery disease (CAD) with area under the receiver-operating-characteristic curve. We also quantified the direct count change among AC, NC, and DeepAC images on a per-voxel basis. Results: DeepAC could be obtained in less than 1 s from NC images; area under the receiver-operating-characteristic curve for obstructive CAD was higher for DeepAC TPD (0.79; 95% CI, 0.72-0.85) than for NC TPD (0.70; 95% CI, 0.63-0.78; P < 0.001) and similar to AC TPD (0.81; 95% CI, 0.75-0.87; P = 0.196). The normalcy rate in the low-likelihood-of-coronary-disease population was higher for DeepAC TPD (70.4%) and AC TPD (75.0%) than for NC TPD (54.6%, P < 0.001 for both). The positive count change (increase in counts) was significantly higher for AC versus NC (median, 9.4; interquartile range, 6.0-14.2; P < 0.001) than for AC versus DeepAC (median, 2.4; interquartile range, 1.3-4.2). Conclusion: In an independent external dataset, DeepAC provided improved diagnostic accuracy for obstructive CAD, as compared with NC images, and this accuracy was similar to that of actual AC. DeepAC simplifies the task of artifact identification for physicians, avoids misregistration artifacts, and can be performed rapidly without the need for CT hardware and additional acquisitions.

Integration of coronary artery calcium scoring from CT attenuation scans by machine learning improves prediction of adverse cardiovascular events in patients undergoing SPECT/CT myocardial perfusion imaging.

BACKGROUND: Machine learning (ML) has been previously applied for prognostication in patients undergoing SPECT myocardial perfusion imaging (MPI). We evaluated whether including attenuation CT coronary artery calcification (CAC) scoring improves ML prediction of major adverse cardiovascular events (MACE) in patients undergoing SPECT/CT MPI. METHODS: From the REFINE SPECT Registry 4770 patients with SPECT/CT performed at a single center were included (age: 64 ± 12 years, 45% female). ML algorithm (XGBoost) inputs were clinical risk factors, stress variables, SPECT imaging parameters, and expert-observer CAC scoring using CT attenuation correction scans performed to obtain CT attenuation maps. The ML model was trained and validated using tenfold hold-out validation. Receiver Operator Characteristics (ROC) curves were analyzed for prediction of MACE. MACE-free survival was evaluated with standard survival analyses. RESULTS: During a median follow-up of 24.1 months, 475 patients (10%) experienced MACE. Higher area under the ROC curve for MACE was observed with ML when CAC scoring was included (CAC-ML score, 0.77, 95% confidence interval [CI] 0.75-0.79) compared to ML without CAC (ML score, 0.75, 95% CI 0.73-0.77, P = .005) and when compared to CAC score alone (0.71, 95% CI 0.68-0.73, P < .001). Among clinical, imaging, and stress parameters, CAC score had highest variable importance for ML. On survival analysis patients with high CAC-ML score (> 0.091) had higher event rate when compared to patients with low CAC-ML score (hazard ratio 5.3, 95% CI 4.3-6.5, P < .001). CONCLUSION: Integration of attenuation CT CAC scoring improves the predictive value of ML risk score for MACE prediction in patients undergoing SPECT MPI.