Dataset Information

Automated SPECT analysis compared with expert visual scoring for the detection of FFR-defined coronary artery disease.

ABSTRACT: PURPOSE:Traditionally, interpretation of myocardial perfusion imaging (MPI) is based on visual assessment. Computer-based automated analysis might be a simple alternative obviating the need for extensive reading experience. Therefore, the aim of the present study was to compare the diagnostic performance of automated analysis with that of expert visual reading for the detection of obstructive coronary artery disease (CAD). METHODS:206 Patients (64% men, age 58.2?±?8.7 years) with suspected CAD were included prospectively. All patients underwent 99mTc-tetrofosmin single-photon emission computed tomography (SPECT) and invasive coronary angiography with fractional flow reserve (FFR) measurements. Non-corrected (NC) and attenuation-corrected (AC) SPECT images were analyzed both visually as well as automatically by commercially available SPECT software. Automated analysis comprised a segmental summed stress score (SSS), summed difference score (SDS), stress total perfusion deficit (S-TPD), and ischemic total perfusion deficit (I-TPD), representing the extent and severity of hypoperfused myocardium. Subsequently, software was optimized with an institutional normal database and thresholds. Diagnostic performances of automated and visual analysis were compared taking FFR as a reference. RESULTS:Sensitivity did not differ significantly between visual reading and most automated scoring parameters, except for SDS, which was significantly higher than visual assessment (p?

SUBMITTER: Driessen RS

PROVIDER: S-EPMC5954003 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Automated SPECT analysis compared with expert visual scoring for the detection of FFR-defined coronary artery disease.

Driessen R S RS Raijmakers P G PG Danad I I Stuijfzand W J WJ Schumacher S P SP Leipsic J A JA Min J K JK Knuuti J J Lammertsma A A AA van Rossum A C AC van Royen N N Underwood S R SR Knaapen P P

European journal of nuclear medicine and molecular imaging 20180222 7

<h4>Purpose</h4>Traditionally, interpretation of myocardial perfusion imaging (MPI) is based on visual assessment. Computer-based automated analysis might be a simple alternative obviating the need for extensive reading experience. Therefore, the aim of the present study was to compare the diagnostic performance of automated analysis with that of expert visual reading for the detection of obstructive coronary artery disease (CAD).<h4>Methods</h4>206 Patients (64% men, age 58.2 ± 8.7 years) with ...[more]

PMID: 29470616

Similar Datasets

Project description:Coronary artery disease (CAD), the most common manifestation of cardiovascular disease, remains the most common cause of mortality in the United States. Risk assessment is key for primary prevention of coronary events and coronary artery calcium (CAC) scoring using computed tomography (CT) is one such non-invasive tool. Despite the proven clinical value of CAC, the current clinical practice implementation for CAC has limitations such as the lack of insurance coverage for the test, need for capital-intensive CT machines, specialized imaging protocols, and accredited 3D imaging labs for analysis (including personnel and software). Perhaps the greatest gap is the millions of patients who undergo routine chest CT exams and demonstrate coronary artery calcification, but their presence is not often reported or quantitation is not feasible. We present two deep learning models that automate CAC scoring demonstrating advantages in automated scoring for both dedicated gated coronary CT exams and routine non-gated chest CTs performed for other reasons to allow opportunistic screening. First, we trained a gated coronary CT model for CAC scoring that showed near perfect agreement (mean difference in scores = -2.86; Cohen's Kappa = 0.89, P < 0.0001) with current conventional manual scoring on a retrospective dataset of 79 patients and was found to perform the task faster (average time for automated CAC scoring using a graphics processing unit (GPU) was 3.5 ± 2.1 s vs. 261 s for manual scoring) in a prospective trial of 55 patients with little difference in scores compared to three technologists (mean difference in scores = 3.24, 5.12, and 5.48, respectively). Then using CAC scores from paired gated coronary CT as a reference standard, we trained a deep learning model on our internal data and a cohort from the Multi-Ethnic Study of Atherosclerosis (MESA) study (total training n = 341, Stanford test n = 42, MESA test n = 46) to perform CAC scoring on routine non-gated chest CT exams with validation on external datasets (total n = 303) obtained from four geographically disparate health systems. On identifying patients with any CAC (i.e., CAC ≥ 1), sensitivity and PPV was high across all datasets (ranges: 80-100% and 87-100%, respectively). For CAC ≥ 100 on routine non-gated chest CTs, which is the latest recommended threshold to initiate statin therapy, our model showed sensitivities of 71-94% and positive predictive values in the range of 88-100% across all the sites. Adoption of this model could allow more patients to be screened with CAC scoring, potentially allowing opportunistic early preventive interventions.

Project description:PurposeMyocardial perfusion imaging (MPI) has limitations in the presence of balanced multivessel disease (MVD) and left main (LM) coronary artery disease, occasionally resulting in false-normal results despite the high cardiovascular risk associated with this condition. The purpose of this study was to assess the incidence of severe coronary artery disease (CAD) in the presence of a very high Agatston coronary artery calcium (CAC) score (>1,000) in stable symptomatic patients without known CAD but with normal MPI results.MethodsA total of 2,659 prospectively acquired consecutive patients were referred for MPI and evaluation of CAC score by CT. Of this patient population, 8 % (222/2,659) had ischemia without myocardial infarction (MI) on MPIand 11 % (298/2,659) had abnormal MPI (MI and/or ischemia). On presentation 1 % of the patients (26/2,659) were symptomatic, had a CAC score >1,000 and normal MPI results. The definition of normal MPI was strict and included a normal hemodynamic response without ischemic ECG changes and normal imaging, particularly absence of transient ischemic dilation. All of these 26 patients with a CAC score >1,000 and normal MPI findings underwent cardiac catheterization.ResultsOf these 26 patients, 58 % (15/26) had severe disease (≥70 % stenosis) leading to revascularization. Of this group, 47 % (7/15) underwent percutaneous intervention, and 53 % (8/15) underwent coronary artery bypass grafting. All of these 15 patients had either MVD (14/15) or LM coronary artery disease (1/15), and represented 0.6 % (15/2,659) of all referred patients (95 % CI 0.3 - 0.9 %). The majority, 90 % (8/9), had severe CAD with typical chest pain.ConclusionA very high CAC score (>1,000) with normal MPI in a small subset of symptomatically stable patients was associated with a moderate incidence of severe CAD (95 % CI 37 - 77 %). Larger studies and/or a meta-analysis of small studies are needed to more precisely estimate the incidence of CAD in this population. This study also supports the concept that a normal MPI result in patients with severe CAD may be due to balanced MVD.

Dataset Information

Automated SPECT analysis compared with expert visual scoring for the detection of FFR-defined coronary artery disease.

Publications

Automated SPECT analysis compared with expert visual scoring for the detection of FFR-defined coronary artery disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets