The utility of a fully automated cardiac magnetic resonance post-processing tool and radiomics algorithm to non-invasively classify patients with or without significant coronary artery stenosis
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): Research Institute of the McGill University Health Centre. Background Oxygenation-Sensitive Cardiac Magnetic Resonance (OS-CMR) has emerged as a powerful tool to investigate the underlying physiology of a number of disease states through the assessment of tissue oxygenation status with myocardial oxygenation reserve and functional kinetics of the myocardium with strain. Recently, the analysis of CMR scans with radiomics algorithms has demonstrated to have superior diagnostic accuracy over standard analysis and reporting methods. As up to half of patients undergoing coronary angiography are found to have ischemia with no significant coronary artery obstruction, a non-invasive diagnostic test that can help to more accurately stratify patients presenting with symptoms of ischemia as having significant or no significant coronary artery disease (CAD) would be of great clinical use. Methods We analyzed 49 patients (38 with significant and 15 without significant obstructive CAD) with a positive stress test and coronary angiography. All participants underwent a non-contrast CMR exam on a clinical 3T MRI system (Magnetom Skyra™, Siemens Healthineers, Erlangen, Germany) within one week of the coronary angiography. Long axis cine CMR for ventricular morphology, volumes, function including strain, and short axis OS-CMR images were acquired (total image acquisition time less than 15min). The images were imported and analyzed with a fully automated analysis package including an advanced machine learning algorithm (cvi42™ Cardiom prototype (Circle Cardiovascular Imaging, Alberta, Canada). Per participant, 602 discrete data points per participant are extracted. A 75% or higher degree of coronary artery stenosis on Quantitative Coronary Angiography (QCA) was used as the ground truth and classified as either 1 vessel disease (VD), 2VD, 3VD, or no significant coronary artery obstruction. Results Fig. 1 shows the top discriminative features as identified by the algorithm: OS-CMR derived marker: 1) myocardial oxygen saturation (LV SVO2), 2) myocardial oxygenation in response to hyperventilation stress (MORS), and 3) epicardial myocardial oxygenation reserve (MORE). Other predictive markers were: Peak Systolic Radial Strain, treatment with calcium channel blockers, presence of cerebrovascular disease, and hypertension. The algorithm showed a 73% classification accuracy of identifying patients with or without obstructive coronary artery stenosis. Conclusion In this proof-of-concept analysis, a fully automated post-processing tool and radiomics algorithm has demonstrated the potential to accurately predict clinical classification in patients with and without significant CAD with a non-invasive, contrast-free CMR protocol. Further training and refinement of analysis algorithms are likely to further enhance the predictive value.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".