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Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
Published on: February 12, 2011
Classification model based on strain measurements to identify patients with arrhythmogenic cardiomyopathy with left
Yolanda Vives-Gilabert1, Esther Zorio2, Jorge Sanz-Sánchez3
1Instituto ITACA, Universitat Politècnica de Valencia, Camino de Vera s/n,València 46022, Spain.
Insights
This study developed a new model using left ventricular (LV) strain analysis to identify arrhythmogenic cardiomyopathy (AC) patients with LV involvement. The model accurately classifies patients, aiding in early diagnosis and family screening for this heart condition.
Area of Science:
- Cardiology
- Medical Imaging
- Biomedical Engineering
Background:
- Arrhythmogenic cardiomyopathy (AC) diagnosis relies on qualitative assessments, lacking precise quantification of myocardial deformation.
- Current criteria for AC are limited to the right ventricle, neglecting left ventricular (LV) involvement.
- A strain-based approach offers potential for precise, quantitative assessment of myocardial function in both ventricles.
Purpose of the Study:
- To define and modelize the strain behavior of the LV in AC patients with LV involvement.
- To apply advanced algorithms like Principal Component Analysis (PCA), clustering, and Naïve Bayes (NB) classifiers for strain analysis.
- To develop a quantitative method for diagnosing AC with LV involvement.
Main Methods:
- Feature-tracking analysis of cardiac magnetic resonance imaging (cMRI) to assess 3D LV strain time series.
- Principal Component Analysis (PCA) applied to strain data for feature extraction.
- Two-Step clustering to categorize patients based on LV strain impairment, followed by Naïve Bayes classification.
Main Results:
- AC patients with LV involvement were classified into mildly (60%) and severely (40%) affected strain groups.
- Significant differences in LV strain were observed between AC subgroups and controls, particularly with severe impairment.
- The Naïve Bayes classifier achieved 82.76% overall accuracy, with high sensitivity and specificity for detecting severe LV strain abnormalities.
Conclusions:
- A validated LV strain classification model can aid in identifying AC patients with LV involvement.
- The model is particularly useful in high pretest probability settings, such as family screening.
- Quantitative strain analysis provides a valuable tool for diagnosing and monitoring AC with LV involvement.
Background And Objective:
A heterogenous expression characterizes arrhythmogenic cardiomyopathy (AC). The evaluation of regional wall movement included in the current Task Force Criteria is only qualitative and restricted to the right ventricle. However, a strain-based approach could precisely quantify myocardial deformation in both ventricles. We aim to define and modelize the strain behavior of the left ventricle in AC patients with left ventricular (LV) involvement by applying algorithms such as Principal Component Analysis (PCA), clustering and naïve Bayes (NB) classifiers.
Methods:
Thirty-six AC patients with LV involvement and twenty-three non-affected family members (controls) were enrolled. Feature-tracking analysis was applied to cine cardiac magnetic resonance imaging to assess strain time series from a 3D approach, to which PCA was applied. A Two-Step clustering algorithm separated the patients' group into clusters according to their level of LV strain impairment. A statistical characterization between controls and the new AC subgroups was done. Finally, a NB classifier was built and new data from a small evolutive dataset was predicted.
Results:
60% of AC-LV patients showed mildly affected strain and 40% severely affected strain. Both groups and controls exhibited statistically significant differences, especially when comparing controls and severely affected AC-LV patients. The classification accuracy of the strain NB classifier reached 82.76%. The model performance was as good as to classify the individuals with a 100% sensitivity and specificity for severely impaired strain patients, 85.7% and 81.1% for mildly impaired strain patients, and 69.9% and 91.4% for normal strain, respectively. Even when the severely affected LV-AC group was excluded, LV strain showed a good accuracy to differentiate patients and controls. The prediction of the evolutive dataset revealed a progressive alteration of strain in time.
Conclusions:
Our LV strain classification model may help to identify AC patients with LV involvement, at least in a setting of a high pretest probability, such as family screening.
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