Investigation of cardiomyopathy using cardiac magnetic resonance imaging part 2: Rare phenotypes

Rare Metabolic and Endocrine Diseases with Cardiovascular Involvement: Insights from Cardiovascular Magnetic Resonance - A Review

The identification of rare diseases with cardiovascular involvement poses significant diagnostic challenges due to the rarity of the diseases, but also due to the lack of knowledge and expertise. Most of them remain underrecognized and undiagnosed, leading to clinical mismanagement and affecting the patients' prognosis, as these diseases are per definition life-threatening or chronic debilitating. This article reviews the cardiovascular involvement of the most well-known rare metabolic and endocrine diseases and their diagnostic approach through the lens of cardiovascular magnetic resonance (CMR) imaging and its prognostic role, highlighting its fundamental value compared to other imaging modalities.

cMRI-BED: A novel informatics framework for cardiac MRI biomarker extraction and discovery applied to pediatric cardiomyopathy classification

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.

[Cardiac magnetic resonance tomography in the diagnostics of restrictive and unclassified cardiopathies]

CLINICAL/METHODICAL ISSUE

Besides ischemic heart disease cardiomyopathies are common causes of heart failure and sudden cardiac death.

STANDARD RADIOLOGICAL METHODS

The diagnostic spectrum in cardiomyopathies comprises non-invasive and invasive examination techniques.

METHODICAL INNOVATIONS

The exact verification of certain cardiomyopathies necessitates knowledge of the latest classification of cardiomyopathies as well as dedicated examination protocols.

PERFORMANCE

Modern imaging modalities, such as echocardiography and cardiac magnetic resonance imaging (MRI) have emerged as useful imaging tools in the investigation of patients suspected of having many different types of cardiomyopathies.

ACHIEVEMENTS

Based on a better understanding of the underlying pathophysiology several diagnostic criteria have been defined using cardiac MRI. In particular there is an increasing importance of cardiac MRI in the description of patients with restrictive and unclassified cardiomyopathies.

PRACTICAL RECOMMENDATIONS

Echocardiography still remains the modality of choice in the diagnostics of unclear left ventricular heart failure. Further diagnostic work-up should include cardiac MRI in case of any lack of clarity.