Radiomics Analysis of Clinical Myocardial Perfusion Stress SPECT Images to Identify Coronary Artery Calcification

Ashrafinia, Saeed; Dalaie, Pejman; Ms, Sadaghiani; Schindler, Thomas H.; Mg, Pomper; Rahmim, Arman

doi:10.1101/2021.01.29.21250803

Cited by 8 publications

(5 citation statements)

References 55 publications

(78 reference statements)

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“…More recently we demonstrated that deep learning cardiac chamber volumes (from CTAC) provided incremental and complementary value to CAC and SPECT variables. 31 Ashrafinia et al used radiomic features from SPECT MPI to predict CAC score derived from CT scans 32 , whereas Amini et al applied a quantitative image analysis approach not only to diagnose CAD, but also for risk classification. 33 The proposed AI approach integrates simultaneous assessment of multiple structures on CTAC by leveraging strengths of deep learning and quantitative image analyses.…”

Section: Discussionmentioning

confidence: 99%

Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction

Michalowska,

Zhang,

Shanbhag

et al. 2024

Preprint

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BackgroundWhile 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.MethodsPatients 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).ResultsOf 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).ConclusionIn 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.

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Section: Discussionmentioning

confidence: 99%

Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction

Michalowska,

Zhang,

Shanbhag

et al. 2024

Preprint

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“…Several studies have been conducted in attempts to address this issue. Ashrafinia et al (28,29), applied texture and radiomics analysis to clinical myocardial perfusion SPECT imaging to predict coronary artery calcification (CAC) from CT imaging. A study by Kolossváry et al (30), showed that radiomics features are superior to conventional quantitative computed tomographic metrics in identifying coronary plaques with napkin-ring signs.…”

Section: Discussionmentioning

confidence: 99%

Cardiac SPECT radiomic features repeatability and reproducibility: A multi-scanner phantom study

Edalat-Javid

Shiri

Hajianfar

et al. 2021

Journal of Nuclear Cardiology

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Background:The aim of this study was to assess the robustness of cardiac SPECT radiomics features against changes in imaging settings including acquisition and reconstruction settings. Methods: Four scanners were used to acquire SPECT scans of a cardiac phantom with 5mCi of 99m Tc. The effects of different image acquisition and reconstruction settings including the Number of View, View Matrix Size, attenuation correction, image reconstruction algorithm, number of iterations, number of subsets, type of filter, full width at half maximum (FWHM) of Gaussian filter, Butterworth filter order, and Butterworth filter cut-off were studied. In total 5263 different images were reconstructed. Eighty-seven radiomic features including first, second, and high order textures were extracted from images. To assess reproducibility and repeatability the coefficient of variation (COV) was used for each image feature over the different imaging settings. Result: IDMN and IDN features from GLCM, RP from GLRLM, ZE from GLSZM, and DE from GLDM feature sets were the only features that were the most reproducible (COV ≤ 5%) against changes in all imaging settings. In addition, the IDMN feature from GLCM, LALGLE, SALGLE and LGLZE from GLSZM, and SDLGLE from GLDM feature sets were the features that were less reproducible (COV>20 %) against changes in all imaging settings. Matrix size has the greatest impact on feature variability as most of features are not repeatable and 82.76% of them had (COV>20 %). Conclusion: Repeatability and reproducibility of SPECT/CT radiomics texture features in different imaging settings is feature-dependent, and different image acquisitions and reconstructions have different effects on radiomics texture features. Low COV radiomics features could be consider for further clinical studies.

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“…We subsequently provide discussions on our findings and a conclusion. This article was previously posted to the medRxiv preprint server on February 01, 2021 [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Standardized Radiomics Analysis of Clinical Myocardial Perfusion Stress SPECT Images to Identify Coronary Artery Calcification

Ashrafinia,

Dalaie,

Schindler

et al. 2023

Cureus

Self Cite

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Purpose: Myocardial perfusion (MP) stress single-photon emission computed tomography (SPECT) is an established diagnostic test for patients suspected of coronary artery disease (CAD). Meanwhile, coronary artery calcification (CAC) scoring obtained from diagnostic CT is a highly sensitive test, offering incremental diagnostic information in identifying patients with significant CAD yet normal MP stress SPECT (MPSS) scans. However, after decades of wide utilization of MPSS, CAC is not commonly reimbursed (e.g. by the CMS), nor widely deployed in community settings. We studied the potential of complementary information deduced from the radiomics analysis of normal MPSS scans in predicting the CAC score.Methods: We collected data from 428 patients with normal (non-ischemic) MPSS ( 99m Tc-sestamibi; consensus reading). A nuclear medicine physician verified iteratively reconstructed images (attenuationcorrected) to be free from fixed perfusion defects and artifactual attenuation. Three-dimensional images were automatically segmented into four regions of interest (ROIs), including myocardium and three vascular segments (left anterior descending [LAD]-left circumference [LCX]-right coronary artery [RCA]). We used our software package, standardized environment for radiomics analysis (SERA), to extract 487 radiomic features in compliance with the image biomarker standardization initiative (IBSI). Isotropic cubic voxels were discretized using fixed bin-number discretization (eight schemes). We first performed blind-tooutcome feature selection focusing on a priori usefulness, dynamic range, and redundancy of features. Subsequently, we performed univariate and multivariate machine learning analyses to predict CAC scores from i) selected radiomic features, ii) 10 clinical features, and iii) combined radiomics + clinical features. Univariate analysis invoked Spearman correlation with Benjamini-Hotchberg false-discovery correction. The multivariate analysis incorporated stepwise linear regression, where we randomly selected a 15% test set and divided the other 85% of data into 70% training and 30% validation sets. Training started from a constant (intercept) model, iteratively adding/removing features (stepwise regression), invoking the Akaike information criterion (AIC) to discourage overfitting. Validation was run similarly, except that the training output model was used as the initial model. We randomized training/validation sets 20 times, selecting the best model using log-likelihood for evaluation in the test set. Assessment in the test set was performed thoroughly by running the entire operation 50 times, subsequently employing Fisher's method to verify the significance of independent tests. Results: Unsupervised feature selection significantly reduced 8×487 features to 56. In univariate analysis, no feature survived the false-discovery rate (FDR) to directly correlate with CAC scores. Applying Fisher's method to the multivariate regression results demonstrated combining radiomics with the clinical features to enhance the ...

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Radiomics Analysis of Clinical Myocardial Perfusion Stress SPECT Images to Identify Coronary Artery Calcification

Cited by 8 publications

References 55 publications

Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction

Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction

Cardiac SPECT radiomic features repeatability and reproducibility: A multi-scanner phantom study

Standardized Radiomics Analysis of Clinical Myocardial Perfusion Stress SPECT Images to Identify Coronary Artery Calcification

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