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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».