Key result
The Bayesian Rule Learning decision tree model achieved 80.72% accuracy and 79.6% AUC in classifying positive versus negative findings of pediatric cardiomyopathy from cMRI-derived biomarkers.
Why the study?
Does the cMRI-BED informatics framework accurately classify positive versus negative findings of cardiomyopathy in pediatric patients using cMRI-derived biomarkers?
Observational (n=83)
No
Does the cMRI-BED informatics framework accurately classify positive versus negative findings of cardiomyopathy in pediatric patients using cMRI-derived biomarkers?
The novel cMRI-BED informatics framework demonstrates feasibility in processing pediatric cMRI data to yield predictive classification rules for cardiomyopathy with approximately 80% accuracy.
May support ML classification of pediatric cardiomyopathy on cMRI; hypothesis-generating and requires prospective validation.
BACKGROUND: Pediatric cardiomyopathies are a rare, yet heterogeneous group of pathologies of the myocardium that are routinely examined clinically using Cardiovascular Magnetic Resonance Imaging (cMRI). This gold standard powerful non-invasive tool yields high resolution temporal images that characterize myocardial tissue. The complexities associated with the annotation of images and extraction of markers, necessitate the development of efficient workflows to acquire, manage and transform this data into actionable knowledge for patient care to reduce mortality and morbidity. METHODS: We develop and test a novel informatics framework called cMRI-BED for biomarker extraction and discovery from such complex pediatric cMRI data that includes the use of a suite of tools for image processing, marker extraction and predictive modeling. We applied our workflow to obtain and analyze a dataset of 83 de-identified cases and controls containing cMRI-derived biomarkers for classifying positive versus negative findings of cardiomyopathy in children. Bayesian rule learning (BRL) methods were applied to derive understandable models in the form of propositional rules with posterior probabilities pertaining to their validity. Popular machine learning methods in the WEKA data mining toolkit were applied using default parameters to assess cross-validation performance of this dataset using accuracy and percentage area under ROC curve (AUC) measures. RESULTS: The best 10-fold cross validation predictive performance obtained on this cMRI-derived biomarker dataset was 80.72% accuracy and 79.6% AUC by a BRL decision tree model, which is promising from this type of rare data. Moreover, we were able to verify that mycocardial delayed enhancement (MDE) status, which is known to be an important qualitative factor in the classification of cardiomyopathies, is picked up by our rule models as an important variable for prediction. CONCLUSIONS: Preliminary results show the feasibility of our framework for processing such data while also yielding actionable predictive classification rules that can augment knowledge conveyed in cardiac radiology outcome reports. Interactions between MDE status and other cMRI parameters that are depicted in our rules warrant further investigation and validation. Predictive rules learned from cMRI data to classify positive and negative findings of cardiomyopathy can enhance scientific understanding of the underlying interactions among imaging-derived parameters.
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Gopalakrishnan et al. (2015) conducted an observational in Pediatric cardiomyopathy and myocarditis (n=83). Bayesian Rule Learning (BRL) decision tree model vs. Other machine learning methods (SVM, C4.5, Random Forests) was evaluated on 10-fold cross-validation accuracy. The Bayesian Rule Learning decision tree model achieved 80.72% accuracy and 79.6% AUC in classifying positive versus negative findings of pediatric cardiomyopathy from cMRI-derived biomarkers.
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