  Hypertension is a medical condition that is well-established as a risk factor
for many major diseases. For example, it can cause alterations in the cardiac
structure and function over time that can lead to heart related morbidity and
mortality. However, at the subclinical stage, these changes are subtle and
cannot be easily captured using conventional cardiovascular indices calculated
from clinical cardiac imaging. In this paper, we describe a radiomics approach
for identifying intermediate imaging phenotypes associated with hypertension.
The method combines feature selection and machine learning techniques to
identify the most subtle as well as complex structural and tissue changes in
hypertensive subgroups as compared to healthy individuals. Validation based on
a sample of asymptomatic hearts that include both hypertensive and
non-hypertensive cases demonstrate that the proposed radiomics model is capable
of detecting intensity and textural changes well beyond the capabilities of
conventional imaging phenotypes, indicating its potential for improved
understanding of the longitudinal effects of hypertension on cardiovascular
health and disease.
