A radiomics approach to analyze cardiac alterations in hypertension

TL;DR

This study introduces a radiomics method that uses advanced feature analysis and machine learning to detect subtle cardiac changes caused by hypertension, surpassing traditional imaging techniques.

Contribution

It presents a novel radiomics approach combining feature selection and machine learning to identify complex cardiac alterations in hypertensive patients.

Findings

Radiomics model detects subtle tissue and structural changes.

Model outperforms conventional imaging indices.

Potential for early detection of hypertensive cardiac effects.

Abstract

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.