A radiomics approach to analyze cardiac alterations in hypertension
This work addresses the challenge of early detection of hypertension-related cardiac changes for medical professionals, but it is incremental as it applies existing radiomics and machine learning techniques to a specific domain.
The paper tackled the problem of detecting subtle cardiac alterations in hypertension at the subclinical stage, where conventional imaging indices fail, by developing a radiomics approach that identified intermediate imaging phenotypes, with validation showing it detects intensity and textural changes beyond current methods.
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.