Deep Learning for Echo Analysis, Tracking, and Evaluation of Mitral Regurgitation (DELINEATE-MR).

BACKGROUND: Artificial intelligence, particularly deep learning (DL), has immense potential to improve the interpretation of transthoracic echocardiography (TTE). Mitral regurgitation (MR) is the most common valvular heart disease and presents unique challenges for DL, including the integration of multiple video-level assessments into a final study-level classification. METHODS: A novel DL system was developed to intake complete TTEs, identify color MR Doppler videos, and determine MR severity on a 4-step ordinal scale (none/trace, mild, moderate, and severe) using the reading cardiologist as a reference standard. This DL system was tested in internal and external test sets with performance assessed by agreement with the reading cardiologist, weighted κ, and area under the receiver-operating characteristic curve for binary classification of both moderate or greater and severe MR. In addition to the primary 4-step model, a 6-step MR assessment model was studied with the addition of the intermediate MR classes of mild-moderate and moderate-severe with performance assessed by both exact agreement and ±1 step agreement with the clinical MR interpretation. RESULTS: A total of 61 689 TTEs were split into train (n=43 811), validation (n=8891), and internal test (n=8987) sets with an additional external test set of 8208 TTEs. The model had high performance in MR classification in internal (exact accuracy, 82%; κ=0.84; area under the receiver-operating characteristic curve, 0.98 for moderate/severe MR) and external test sets (exact accuracy, 79%; κ=0.80; area under the receiver-operating characteristic curve, 0.98 for moderate or greater MR). Most (63% internal and 66% external) misclassification disagreements were between none/trace and mild MR. MR classification accuracy was slightly higher using multiple TTE views (accuracy, 82%) than with only apical 4-chamber views (accuracy, 80%). In subset analyses, the model was accurate in the classification of both primary and secondary MR with slightly lower performance in cases of eccentric MR. In the analysis of the 6-step classification system, the exact accuracy was 80% and 76% with a ±1 step agreement of 99% and 98% in the internal and external test set, respectively. CONCLUSIONS: This end-to-end DL system can intake entire echocardiogram studies to accurately classify MR severity and may be useful in helping clinicians refine MR assessments.

Deep learning to detect left ventricular structural abnormalities in chest X-rays.

BACKGROUND AND AIMS: Early identification of cardiac structural abnormalities indicative of heart failure is crucial to improving patient outcomes. Chest X-rays (CXRs) are routinely conducted on a broad population of patients, presenting an opportunity to build scalable screening tools for structural abnormalities indicative of Stage B or worse heart failure with deep learning methods. In this study, a model was developed to identify severe left ventricular hypertrophy (SLVH) and dilated left ventricle (DLV) using CXRs. METHODS: A total of 71 589 unique CXRs from 24 689 different patients completed within 1 year of echocardiograms were identified. Labels for SLVH, DLV, and a composite label indicating the presence of either were extracted from echocardiograms. A deep learning model was developed and evaluated using area under the receiver operating characteristic curve (AUROC). Performance was additionally validated on 8003 CXRs from an external site and compared against visual assessment by 15 board-certified radiologists. RESULTS: The model yielded an AUROC of 0.79 (0.76-0.81) for SLVH, 0.80 (0.77-0.84) for DLV, and 0.80 (0.78-0.83) for the composite label, with similar performance on an external data set. The model outperformed all 15 individual radiologists for predicting the composite label and achieved a sensitivity of 71% vs. 66% against the consensus vote across all radiologists at a fixed specificity of 73%. CONCLUSIONS: Deep learning analysis of CXRs can accurately detect the presence of certain structural abnormalities and may be useful in early identification of patients with LV hypertrophy and dilation. As a resource to promote further innovation, 71 589 CXRs with adjoining echocardiographic labels have been made publicly available.

Effects of confounding and effect-modifying lifestyle, environmental and medical factors on risk of radiation-associated cardiovascular disease.

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death worldwide. It has been known for some considerable time that radiation is associated with excess risk of CVD. A recent systematic review of radiation and CVD highlighted substantial inter-study heterogeneity in effect, possibly a result of confounding or modifications of radiation effect by non-radiation factors, in particular by the major lifestyle/environmental/medical risk factors and latent period. METHODS: We assessed effects of confounding by lifestyle/environmental/medical risk factors on radiation-associated CVD and investigated evidence for modifying effects of these variables on CVD radiation dose-response, using data assembled for a recent systematic review. RESULTS: There are 43 epidemiologic studies which are informative on effects of adjustment for confounding or risk modifying factors on radiation-associated CVD. Of these 22 were studies of groups exposed to substantial doses of medical radiation for therapy or diagnosis. The remaining 21 studies were of groups exposed at much lower levels of dose and/or dose rate. Only four studies suggest substantial effects of adjustment for lifestyle/environmental/medical risk factors on radiation risk of CVD; however, there were also substantial uncertainties in the estimates in all of these studies. There are fewer suggestions of effects that modify the radiation dose response; only two studies, both at lower levels of dose, report the most serious level of modifying effect. CONCLUSIONS: There are still large uncertainties about confounding factors or lifestyle/environmental/medical variables that may influence radiation-associated CVD, although indications are that there are not many studies in which there are substantial confounding effects of these risk factors.

Recovery Rates of Diagnostic Cardiac Procedural Volume in Oceania 1 Year Into COVID-19: The IAEA Non-Invasive Cardiology Protocol Survey on COVID-19 (INCAPS COVID 2).

AIM: The aim of this study was to assess the recovery rates of diagnostic cardiac procedure volumes in the Oceania Region, midway through the coronavirus disease 2019 (COVID-19) pandemic. METHODS: A survey was performed comparing procedure volumes between March 2019 (pre-pandemic), April 2020 (during first wave of COVID-19 pandemic), and April 2021 (1 year into the COVID-19 pandemic). A total of 31 health care facilities within Oceania that perform cardiac diagnostic procedures were surveyed, including a mixture of metropolitan and regional, hospital and outpatient, public and private sites, as well as teaching and non-teaching hospitals. A comparison was made with 549 centres in 96 countries in the rest of the world (RoW) outside of Oceania. The total number and median percentage change in procedure volume were measured between the three timepoints, compared by test type and by facility. RESULTS: A total of 11,902 cardiac diagnostic procedures were performed in Oceania in April 2021 as compared with 11,835 pre-pandemic in March 2019 and 5,986 in April 2020; whereas, in the RoW, 499,079 procedures were performed in April 2021 compared with 497,615 pre-pandemic in March 2019 and 179,014 in April 2020. There was no significant difference in the median recovery rates for total procedure volumes between Oceania (-6%) and the RoW (-3%) (p=0.81). While there was no statistically significant difference in percentage recovery been functional ischaemia testing and anatomical coronary testing in Oceania as compared with the RoW, there was, however, a suggestion of poorer recovery in anatomical coronary testing in Oceania as compared with the RoW (CT coronary angiography -16% in Oceania vs -1% in RoW, and invasive coronary angiography -20% in Oceania vs -9% in RoW). There was no statistically significant difference in recovery rates in procedure volume between metropolitan vs regional (p=0.44), public vs private (p=0.92), hospital vs outpatient (p=0.79), or teaching vs non-teaching centres (p=0.73). CONCLUSIONS: Total cardiology procedure volumes in Oceania normalised 1 year post-pandemic compared to pre-pandemic levels, with no significant difference compared with the RoW and between the different types of health care facilities.

Evaluating Radiation Exposure in Patients with Stable Chest Pain in the SCOT-HEART Trial.

Background In the Scottish Computed Tomography of the Heart (SCOT-HEART) trial in individuals with stable chest pain, a treatment strategy based on coronary CT angiography (CTA) led to improved outcomes. Purpose To assess 5-year cumulative radiation doses of participants undergoing investigation for suspected angina due to coronary artery disease with or without coronary CTA. Materials and Methods This secondary analysis of the SCOT-HEART trial included data from six of 12 recruiting sites and two of three imaging sites. Participants were recruited between November 18, 2010, and September 24, 2014, with follow-up through January 31, 2018. Study participants had been randomized (at a one-to-one ratio) to standard care with CT (n = 1466) or standard care alone (n = 1428). Imaging was performed on a 64-detector (n = 223) or 320-detector row scanner (n = 1466). Radiation dose from CT (dose-length product), SPECT (injected activity), and invasive coronary angiography (ICA; kerma-area product) was assessed for 5 years after enrollment. Effective dose was calculated using conversion factors appropriate for the imaging modality and body region imaged (using 0.026 mSv/mGy · cm for cardiac CT). Results Cumulative radiation dose was assessed in 2894 participants. Median effective dose was 3.0 mSv (IQR, 2.6-3.3 mSv) for coronary calcium scoring, 4.1 mSv (IQR, 2.6-6.1 mSv) for coronary CTA, 7.4 mSv (IQR, 6.2-8.5 mSv) for SPECT, and 4.1 mSv (IQR, 2.5-6.8 mSv) for ICA. After 5 years, total per-participant cumulative dose was higher in the CT group (median, 8.1 mSv; IQR, 5.5-12.4 mSv) compared with standard-care group (median, 0 mSv; IQR, 0-4.5 mSv; P < .001). In participants who underwent any imaging, cumulative radiation exposure was higher in the CT group (n = 1345; median, 8.6 mSv; IQR, 6.1-13.3 mSv) compared with standard-care group (n = 549; median, 6.4 mSv; IQR, 3.4-9.2 mSv; P < .001). Conclusion In the SCOT-HEART trial, the 5-year cumulative radiation dose from cardiac imaging was higher in the coronary CT angiography group compared with the standard-care group, largely because of the radiation exposure from CT. Clinical trial registration no. NCT01149590 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Dodd and Bosserdt in this issue.

Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images.

PURPOSE: Artificial intelligence (AI) has high diagnostic accuracy for coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, when trained using high-risk populations (such as patients with correlating invasive testing), the disease probability can be overestimated due to selection bias. We evaluated different strategies for training AI models to improve the calibration (accurate estimate of disease probability), using external testing. METHODS: Deep learning was trained using 828 patients from 3 sites, with MPI and invasive angiography within 6 months. Perfusion was assessed using upright (U-TPD) and supine total perfusion deficit (S-TPD). AI training without data augmentation (model 1) was compared to training with augmentation (increased sampling) of patients without obstructive CAD (model 2), and patients without CAD and TPD < 2% (model 3). All models were tested in an external population of patients with invasive angiography within 6 months (n = 332) or low likelihood of CAD (n = 179). RESULTS: Model 3 achieved the best calibration (Brier score 0.104 vs 0.121, p < 0.01). Improvement in calibration was particularly evident in women (Brier score 0.084 vs 0.124, p < 0.01). In external testing (n = 511), the area under the receiver operating characteristic curve (AUC) was higher for model 3 (0.930), compared to U-TPD (AUC 0.897) and S-TPD (AUC 0.900, p < 0.01 for both). CONCLUSION: Training AI models with augmentation of low-risk patients can improve calibration of AI models developed to identify patients with CAD, allowing more accurate assignment of disease probability. This is particularly important in lower-risk populations and in women, where overestimation of disease probability could significantly influence down-stream patient management.

Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging.

PURPOSE: Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). METHODS: From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%). RESULTS: Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference). CONCLUSIONS: Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone.

Diagnostic performance characteristics of planar quantitative and semi-quantitative parameters of Tc99m pyrophosphate (PYP) imaging for diagnosis of transthyretin (ATTR) cardiac amyloidosis: the SCAN-MP study.

BACKGROUND: The optimal heart-to-contralateral chest (H/CL) ratio threshold for non-invasive diagnosis of transthyretin cardiac amyloidosis (ATTR-CA) using Tc99m pyrophosphate (PYP) imaging in a population with low pretest probability is not known. METHODS: Using myocardial PYP retention by SPECT as the reference standard, we evaluated the diagnostic performance of different semi-quantitative and quantitative (H/CL chest ratio) planar parameters obtained from 3-hour PYP imaging in a prospectively recruited cohort of minority older adults with heart failure and increased LV wall thickness. RESULTS: Of 229 patients, 14 were found to have ATTR-CA (6.1%). No PYP uptake (grade 0) was observed in 77% of scans, all grade 3 scans were ATTR-CA, and only 4 of 11 (36%) grade 2 scans were ATTR-CA. An H/CL threshold of ≥ 1.4 maximized specificity (99%) and positive predictive value (93%) but resulted in decreased sensitivity (93%), compared to the ≥ 1.3 threshold which had 100% sensitivity. CONCLUSION: Among patients with a low pretest likelihood of ATTR-CA, planar interpretation, while useful to exclude disease, must be interpreted with caution. H/CL ratio threshold of ≥ 1.3 resulted in clinically important misclassifications. These data suggest that quantitative planar imaging thresholds may not be appropriate to apply in low pretest likelihood populations being evaluated for ATTR-CA.

Prevalence and significance of extracardiac uptake on pyrophosphate imaging in the SCAN-MP study: the first 379 cases.

INTRODUCTION: Technetium-labeled bone-avid radiotracers can be used to diagnose transthyretin cardiac amyloidosis (ATTR-CA). Extracardiac uptake of technetium pyrophosphate (Tc-99m PYP) in this context has not been extensively explored and its significance is not well characterized. We assessed extracardiac Tc-99m PYP uptake in individuals undergoing nuclear scintigraphy and the extent of clinically actionable findings. METHODS: The Screening for Cardiac Amyloidosis with Nuclear Imaging in Minority Populations (SCAN-MP) study utilizes Tc-99m PYP imaging to identify ATTR-CA in self-identified Black and Caribbean Hispanic participants ≥ 60 years old with heart failure. We characterized the distribution of extracardiac uptake, including stratification of findings by timing of scan (1 hour vs 3 hours after Tc-99m PYP administration) and noted any additional testing in these subjects. RESULTS: Of 379 participants, 195 (51%) were male, 306 (81%) Black race, and 120 (32%) Hispanic ethnicity; mean age was 73 years. Extracardiac Tc-99m PYP uptake was found in 42 subjects (11.1%): 21 with renal uptake only, 14 with bone uptake only, 4 with both renal and bone uptake, 2 with breast uptake, and 1 with thyroid uptake. Extracardiac uptake was more common in subjects with Tc-99m PYP scans at 1 hour (23.8%) than at 3 hours (6.2%). Overall, four individuals (1.1%) had clinically actionable findings. CONCLUSION: Extracardiac Tc-99m PYP uptake manifested in about 1 in 9 SCAN-MP subjects but was clinically actionable in only 1.1% of cases.

Mining of EHR for interface terminology concepts for annotating EHRs of COVID patients.

BACKGROUND: Two years into the COVID-19 pandemic and with more than five million deaths worldwide, the healthcare establishment continues to struggle with every new wave of the pandemic resulting from a new coronavirus variant. Research has demonstrated that there are variations in the symptoms, and even in the order of symptom presentations, in COVID-19 patients infected by different SARS-CoV-2 variants (e.g., Alpha and Omicron). Textual data in the form of admission notes and physician notes in the Electronic Health Records (EHRs) is rich in information regarding the symptoms and their orders of presentation. Unstructured EHR data is often underutilized in research due to the lack of annotations that enable automatic extraction of useful information from the available extensive volumes of textual data. METHODS: We present the design of a COVID Interface Terminology (CIT), not just a generic COVID-19 terminology, but one serving a specific purpose of enabling automatic annotation of EHRs of COVID-19 patients. CIT was constructed by integrating existing COVID-related ontologies and mining additional fine granularity concepts from clinical notes. The iterative mining approach utilized the techniques of 'anchoring' and 'concatenation' to identify potential fine granularity concepts to be added to the CIT. We also tested the generalizability of our approach on a hold-out dataset and compared the annotation coverage to the coverage obtained for the dataset used to build the CIT. RESULTS: Our experiments demonstrate that this approach results in higher annotation coverage compared to existing ontologies such as SNOMED CT and Coronavirus Infectious Disease Ontology (CIDO). The final version of CIT achieved about 20% more coverage than SNOMED CT and 50% more coverage than CIDO. In the future, the concepts mined and added into CIT could be used as training data for machine learning models for mining even more concepts into CIT and further increasing the annotation coverage. CONCLUSION: In this paper, we demonstrated the construction of a COVID interface terminology that can be utilized for automatically annotating EHRs of COVID-19 patients. The techniques presented can identify frequently documented fine granularity concepts that are missing in other ontologies thereby increasing the annotation coverage.

An Image Quality-informed Framework for CT Characterization.

Background Lack of standardization in CT protocol choice contributes to radiation dose variation. Purpose To create a framework to assess radiation doses within broad CT categories defined according to body region and clinical imaging indication and to cluster indications according to the dose required for sufficient image quality. Materials and Methods This was a retrospective study using Digital Imaging and Communications in Medicine metadata. CT examinations in adults from January 1, 2016 to December 31, 2019 from the University of California San Francisco International CT Dose Registry were grouped into 19 categories according to body region and required radiation dose levels. Five body regions had a single dose range (ie, extremities, neck, thoracolumbar spine, combined chest and abdomen, and combined thoracolumbar spine). Five additional regions were subdivided according to dose. Head, chest, cardiac, and abdomen each had low, routine, and high dose categories; combined head and neck had routine and high dose categories. For each category, the median and 75th percentile (ie, diagnostic reference level [DRL]) were determined for dose-length product, and the variation in dose within categories versus across categories was calculated and compared using an analysis of variance. Relative median and DRL (95% CI) doses comparing high dose versus low dose categories were calculated. Results Among 4.5 million examinations, the median and DRL doses varied approximately 10 times between categories compared with between indications within categories. For head, chest, abdomen, and cardiac (3 266 546 examinations [72%]), the relative median doses were higher in examinations assigned to the high dose categories than in examinations assigned to the low dose categories, suggesting the assignment of indications to the broad categories is valid (head, 3.4-fold higher [95% CI: 3.4, 3.5]; chest, 9.6 [95% CI: 9.3, 10.0]; abdomen, 2.4 [95% CI: 2.4, 2.5]; and cardiac, 18.1 [95% CI: 17.7, 18.6]). Results were similar for DRL doses (all P < .001). Conclusion Broad categories based on image quality requirements are a suitable framework for simplifying radiation dose assessment, according to expected variation between and within categories. © RSNA, 2021 See also the editorial by Mahesh in this issue.

The Impact of Surveillance Imaging Frequency on the Detection of Distant Disease for Patients with Resected Stage III Melanoma.

BACKGROUND: It is not known whether there is a survival benefit associated with more frequent surveillance imaging in patients with resected American Joint Committee on Cancer stage III melanoma. OBJECTIVE: The aim of this study was to investigate distant disease-free survival (DDFS), melanoma-specific survival (MSS), post distant recurrence MSS (dMSS), and overall survival for patients with resected stage III melanoma undergoing regular computed tomography (CT) or positron emission tomography (PET)/CT surveillance imaging at different intervals. PATIENTS AND METHODS: A closely followed longitudinal cohort of patients with resected stage IIIA-D disease treated at a tertiary referral center underwent 3- to 4-monthly, 6-monthly, or 12-monthly surveillance imaging between 2000 and 2017. Survival outcomes were estimated using the Kaplan-Meier method, and log-rank tests assessed the significance of survival differences between imaging frequency groups. RESULTS: Of 473 patients (IIIA, 19%; IIIB, 31%; IIIC, 49%; IIID, 1%) 30% underwent 3- to 4-monthly imaging, 10% underwent 6-monthly imaging, and 60% underwent 12-monthly imaging. After a median follow-up of 6.2 years, distant recurrence was recorded in 252 patients (53%), with 40% detected by surveillance CT or PET/CT, 43% detected clinically, and 17% with another imaging modality. Median DDFS was 5.1 years (95% confidence interval 3.9-6.6). Among 139 IIIC patients who developed distant disease, the median dMSS was 4.4 months shorter in those who underwent 3- to 4-monthly imaging than those who underwent 12-monthly imaging. CONCLUSION: Selecting patients at higher risk of distant recurrence for more frequent surveillance imaging yields a higher proportion of imaging-detected distant recurrences but is not associated with improved survival. A randomized comparison of low versus high frequency imaging is needed.

Diagnostic reference levels and median doses for common clinical indications of CT: findings from an international registry.

OB JECTIVES: The European Society of Radiology identified 10 common indications for computed tomography (CT) as part of the European Study on Clinical Diagnostic Reference Levels (DRLs, EUCLID), to help standardize radiation doses. The objective of this study is to generate DRLs and median doses for these indications using data from the UCSF CT International Dose Registry. METHODS: Standardized data on 3.7 million CTs in adults were collected between 2016 and 2019 from 161 institutions across seven countries (United States of America (US), Switzerland, Netherlands, Germany, UK, Israel, Japan). DRLs (75th percentile) and median doses for volumetric CT-dose index (CTDIvol) and dose-length product (DLP) were assessed for each EUCLID category (chronic sinusitis, stroke, cervical spine trauma, coronary calcium scoring, lung cancer, pulmonary embolism, coronary CT angiography, hepatocellular carcinoma (HCC), colic/abdominal pain, appendicitis), and US radiation doses were compared with European. RESULTS: The number of CT scans within EUCLID categories ranged from 8,933 (HCC) to over 1.2 million (stroke). There was greater variation in dose between categories than within categories (p < .001), and doses were significantly different between categories within anatomic areas. DRLs and median doses were assessed for all categories. DRLs were higher in the US for 9 of the 10 indications (except chronic sinusitis) than in Europe but with a significantly higher sample size in the US. CONCLUSIONS: DRLs for CTDIvol and DLP for EUCLID clinical indications from diverse organizations were established and can contribute to dose optimization. These values were usually significantly higher in the US than in Europe. KEY POINTS: • Registry data were used to create benchmarks for 10 common indications for CT identified by the European Society of Radiology. • Observed US radiation doses were higher than European for 9 of 10 indications (except chronic sinusitis). • The presented diagnostic reference levels and median doses highlight potentially unnecessary variation in radiation dose.

ASNC Imaging Indications (ASNC-I2): Multisocietal indications for radionuclide imaging in the multimodality context-Series rationale and methodology.

BACKGROUND: Appropriate use criteria (AUC) enhance application of cardiovascular imaging techniques but have been applied in limited settings, primarily in common cardiovascular disease processes. There are several complex systemic diseases with cardiovascular implications and special populations with unique cardiovascular considerations that could benefit from appropriateness analysis. Moreover, the high medical complexity of these topics indicate they would benefit from high-yield expert consensus recommendations of the available imaging options. The ASNC Imaging Indications (ASNC-I2) Series will provide a concise overview of relevant disease processes and their multimodality evaluation and will provide consensus clinical indications, diagnostic criteria, and clinical algorithms with representative clinical cases. METHODS: For each ASNC-I2 document, a diverse writing group and rating panel will be composed of experts from societies pertinent to the topic, including relevant imaging societies and clinical societies that manage the disease under evaluation. The rating panel will follow robust modified Delphi methodology and commonly-accepted appropriateness methods to create consensus diagnostic criteria, clinical algorithms, and clinical indications that they will then rate with level of agreement recorded. The clinical and imaging experts will provide concise, high-yield clinical summaries of the disease process, the non-imaging evaluation, and multimodality imaging. Relevant cases will be provided highlighting application of the diagnostic criteria and clinical algorithms. CONCLUSION: The ASNC Imaging Indications (ASNC-I2) Series will complement the diverse portfolio of documents from ASNC. It will use a multisocietal approach with robust appropriateness methodology to guide use of radionuclide imaging in the multimodality imaging context for the cardiovascular care of patients.

Machine learning to predict abnormal myocardial perfusion from pre-test features.

BACKGROUND: Accurately predicting which patients will have abnormal perfusion on MPI based on pre-test clinical information may help physicians make test selection decisions. We developed and validated a machine learning (ML) model for predicting abnormal perfusion using pre-test features. METHODS: We included consecutive patients who underwent SPECT MPI, with 20,418 patients from a multi-center (5 sites) international registry in the training population and 9019 patients (from 2 separate sites) in the external testing population. The ML (extreme gradient boosting) model utilized 30 pre-test features to predict the presence of abnormal myocardial perfusion by expert visual interpretation. RESULTS: In external testing, the ML model had higher prediction performance for abnormal perfusion (area under receiver-operating characteristic curve [AUC] 0.762, 95% CI 0.750-0.774) compared to the clinical CAD consortium (AUC 0.689) basic CAD consortium (AUC 0.657), and updated Diamond-Forrester models (AUC 0.658, p < 0.001 for all). Calibration (validation of the continuous risk prediction) was superior for the ML model (Brier score 0.149) compared to the other models (Brier score 0.165 to 0.198, all p < 0.001). CONCLUSION: ML can predict abnormal myocardial perfusion using readily available pre-test information. This model could be used to help guide physician decisions regarding non-invasive test selection.

Prevalence and predictors of automatically quantified myocardial ischemia within a multicenter international registry.

BACKGROUND: The utility of cardiac stress testing depends on the prevalence of myocardial ischemia within candidate populations. However, a comprehensive assessment of the factors influencing frequency of myocardial ischemia within contemporary populations referred for stress testing has not been performed. METHODS: We assessed 19,690 patients undergoing nuclear stress testing from a multicenter registry. The chi-square test was used to assess the relative importance of features for predicting myocardial ischemia. RESULTS: In the overall cohort, LVEF, male gender, and rest total perfusion deficit (TPD) were the top three predictors of ischemia, followed by CAD status, age, typical angina, and CAD risk factors. Myocardial ischemia was observed in 13.6 % of patients with LVEF > 55 %, in 26.2 % of patients with LVEF 45 %-54 %, and in 48.3% among patients with LVEF < 45 % (P < 0.001). A similar pattern was noted for rest TPD (P < 0.001). Men had a threefold higher frequency of ischemia versus women (25.8 % vs. 8.4%, P < 0.001). Although the relative ranking of ischemia predictors varied among centers, LVEF and/or rest TPD were among the two most potent predictors of myocardial ischemia within each center. CONCLUSION: The prevalence of myocardial ischemia varied markedly according to clinical and imaging characteristics. LVEF and rest TPD are robust predictors of myocardial ischemia.

Automated quantitative analysis of CZT SPECT stratifies cardiovascular risk in the obese population: Analysis of the REFINE SPECT registry.

BACKGROUND: Obese patients constitute a substantial proportion of patients referred for SPECT myocardial perfusion imaging (MPI), presenting a challenge of increased soft tissue attenuation. We investigated whether automated quantitative perfusion analysis can stratify risk among different obesity categories and whether two-view acquisition adds to prognostic assessment. METHODS: Participants were categorized according to body mass index (BMI). SPECT MPI was assessed visually and quantified automatically; combined total perfusion deficit (TPD) was evaluated. Kaplan-Meier and Cox proportional hazard analyses were used to assess major adverse cardiac event (MACE) risk. Prognostic accuracy for MACE was also compared. RESULTS: Patients were classified according to BMI: BMI < 30, 30 ≤ BMI < 35, BMI ≥ 35. In adjusted analysis, each category of increasing stress TPD was associated with increased MACE risk, except for 1% ≤ TPD < 5% and 5% ≤ TPD < 10% in patients with BMI ≥ 35. Compared to visual analysis, single-position stress TPD had higher prognostic accuracy in patients with BMI < 30 (AUC .652 vs .631, P < .001) and 30 ≤ BMI < 35 (AUC .660 vs .636, P = .027). Combined TPD had better discrimination than visual analysis in patients with BMI ≥ 35 (AUC .662 vs .615, P = .003). CONCLUSIONS: Automated quantitative methods for SPECT MPI interpretation provide robust risk stratification in the obese population. Combined stress TPD provides additional prognostic accuracy in patients with more significant obesity.

Diagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT.

BACKGROUND: Stress-only myocardial perfusion imaging (MPI) markedly reduces radiation dose, scanning time, and cost. We developed an automated clinical algorithm to safely cancel unnecessary rest imaging with high sensitivity for obstructive coronary artery disease (CAD). METHODS AND RESULTS: Patients without known CAD undergoing both MPI and invasive coronary angiography from REFINE SPECT were studied. A machine learning score (MLS) for prediction of obstructive CAD was generated using stress-only MPI and pre-test clinical variables. An MLS threshold with a pre-defined sensitivity of 95% was applied to the automated patient selection algorithm. Obstructive CAD was present in 1309/2079 (63%) patients. MLS had higher area under the receiver operator characteristic curve (AUC) for prediction of CAD than reader diagnosis and TPD (0.84 vs 0.70 vs 0.78, P < .01). An MLS threshold of 0.29 had superior sensitivity than reader diagnosis and TPD for obstructive CAD (95% vs 87% vs 87%, P < .01) and high-risk CAD, defined as stenosis of the left main, proximal left anterior descending, or triple-vessel CAD (sensitivity 96% vs 89% vs 90%, P < .01). CONCLUSIONS: The MLS is highly sensitive for prediction of both obstructive and high-risk CAD from stress-only MPI and can be applied to a stress-first protocol for automatic cancellation of unnecessary rest imaging.

Comparison of diabetes to other prognostic predictors among patients referred for cardiac stress testing: A contemporary analysis from the REFINE SPECT Registry.

BACKGROUND: Diabetes mellitus (DM) is increasingly prevalent among contemporary populations referred for cardiac stress testing, but its potency as a predictor for major adverse cardiovascular events (MACE) vs other clinical variables is not well delineated. METHODS AND RESULTS: From 19,658 patients who underwent SPECT-MPI, we identified 3122 patients with DM without known coronary artery disease (CAD) (DM+/CAD-) and 3564 without DM with known CAD (DM-/CAD+). Propensity score matching was used to control for the differences in characteristics between DM+/CAD- and DM-/CAD+ groups. There was comparable MACE in the matched DM+/CAD- and DM-/CAD+ groups (HR 1.15, 95% CI 0.97-1.37). By Chi-square analysis, type of stress (exercise or pharmacologic), total perfusion deficit (TPD), and left ventricular function were the most potent predictors of MACE, followed by CAD and DM status. The combined consideration of mode of stress, TPD, and DM provided synergistic stratification, an 8.87-fold (HR 8.87, 95% CI 7.27-10.82) increase in MACE among pharmacologically stressed patients with DM and TPD > 10% (vs non-ischemic, exercised stressed patients without DM). CONCLUSIONS: Propensity-matched patients with DM and no known CAD have similar MACE risk compared to patients with known CAD and no DM. DM is synergistic with mode of stress testing and TPD in predicting the risk of cardiac stress test patients.

An Analytic Method for Calculating Scanner-, Kilovoltage Peak-, and Patient Size-Specific Hounsfield Unit Scale Thresholds for Agatston Score.

OBJECTIVE: This study aimed to calculate scanner-, kilovoltage peak (kVp)-, and patient size-specific computed tomography (CT) number thresholds for determining Agatston score (AgSc). METHODS: The proposed method was validated using calcium measurements in an anthropomorphic phantom for 4 CT scanners made by 4 vendors. The derived mass concentration (γ) thresholds were used to calculate kVp- and patient size-specific CT number thresholds. Two models were applied to reduce intrascanner and interscanner AgSc variation, respectively. RESULTS: The mean error of the modeled CT numbers is 1.8% (0.1%-4.4%). Model 1 has comparable results to the published phantom calibration method for an average-size patient (error, 1.5%; 0.1%-5.1%). The size- and the kVp-dependent fitting of modeled results have R2 greater than 0.965. CONCLUSIONS: Our results show a potential to enable accurate determination of AgSc under diverse conditions (eg, reduced tube potential) and are more easily applicable to different patient sizes than the phantom calibration method.