A Radiomics Approach to Computer-Aided Diagnosis with Cardiac Cine-MRI

Computer-aided diagnosis of cardiovascular diseases (CVDs) with cine-MRI is an important research topic to enable improved stratification of CVD patients. However, current approaches that use expert visualization or conventional clinical indices can lack accuracy for borderline classications. Advanced statistical approaches based on eigen-decomposition have been mostly concerned with shape and motion indices. In this paper, we present a new approach to identify CVDs from cine-MRI by estimating large pools of radiomic features (statistical, shape and textural features) encoding relevant changes in anatomical and image characteristics due to CVDs. The calculated cine-MRI radiomic features are assessed using sequential forward feature selection to identify the most relevant ones for given CVD classes (e.g. myocardial infarction, cardiomyopathy, abnormal right ventricle). Finally, advanced machine learning is applied to suitably integrate the selected radiomics for final multi-feature classification based on Support Vector Machines (SVMs). The proposed technique was trained and cross-validated using 100 cine-MRI cases corresponding to five different cardiac classes from the ACDC MICCAI 2017 challenge (https://www.creatis.insa-lyon.fr/Challenge/acdc/index.html). All cases were correctly classified in this preliminary study, indicating potential of using large-scale radiomics for MRI-based diagnosis of CVDs.

[1]  Milan Sonka,et al.  Computer-aided diagnosis via model-based shape analysis: automated classification of wall motion abnormalities in echocardiograms. , 2005, Academic radiology.

[2]  Dudley J. Pennell,et al.  Cardiovascular Magnetic Resonance , 2010, Circulation.

[3]  Alejandro F. Frangi,et al.  Statistical Personalization of Ventricular Fiber Orientation Using Shape Predictors , 2014, IEEE Transactions on Medical Imaging.

[4]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[5]  Gaetano Santulli,et al.  Epidemiology of cardiovascular disease in the 21st century: Updated updated numbers and updated facts , 2013 .

[6]  Ling Shao,et al.  A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging , 2016, Magnetic Resonance Materials in Physics, Biology and Medicine.

[7]  Daniel Rueckert,et al.  Classification of Myocardial Infarcted Patients by Combining Shape and Motion Features , 2015, STACOM@MICCAI.

[8]  Alejandro F. Frangi,et al.  Statistical Shape Modeling Using Partial Least Squares: Application to the Assessment of Myocardial Infarction , 2015, STACOM@MICCAI.

[9]  Alejandro F. Frangi,et al.  Automated Detection of Regional Wall Motion Abnormalities Based on a Statistical Model Applied to Multislice Short-Axis Cardiac MR Images , 2009, IEEE Transactions on Medical Imaging.

[10]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[11]  Milan Sonka,et al.  Congenital aortic disease: 4D magnetic resonance segmentation and quantitative analysis , 2009, Medical Image Anal..

[12]  Andriy Fedorov,et al.  Computational Radiomics System to Decode the Radiographic Phenotype. , 2017, Cancer research.

[13]  Alejandro F. Frangi,et al.  Statistical Shape Modeling of the Left Ventricle: Myocardial Infarct Classification Challenge , 2018, IEEE Journal of Biomedical and Health Informatics.

[14]  Daniel Rueckert,et al.  A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion , 2015, Medical Image Anal..

[15]  Peter Schmid,et al.  Abstract 104: miRNAs in the 14q32 cluster are involved in lapatinib resistance , 2017 .

[16]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[17]  Olivier Gevaert,et al.  Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. , 2012, Radiology.

[18]  Olivier Gevaert,et al.  non – small cell lung cancer : Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data — Methods and Preliminary Results 1 , 2012 .