  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
\footnote{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.
