Habitat-based MRI radiomics for enhanced parkinson’s diagnosis

TL;DR

This study shows that using habitat-based MRI radiomics can improve early and accurate diagnosis of Parkinson's disease.

Contribution

The novelty lies in using habitat-based radiomics to capture subtle tissue heterogeneity for Parkinson's detection.

Findings

Habitat-based radiomics achieved over 94% diagnostic accuracy for Parkinson's disease.

This approach outperforms traditional radiomics in capturing tissue heterogeneity linked to PD.

The method uses routine MRI scans, making it clinically applicable for early diagnosis.

Abstract

Early and accurate diagnosis of Parkinson’s disease (PD) remains a clinical challenge. This study explores the potential of habitat-based radiomics as a novel approach to improve PD detection using routine clinical MRI scans. We analyzed MRI data from 308 participants (173 PD patients and 135 healthy controls) to extract detailed features from segmented habitats in the caudate nucleus and putamen. Machine learning models, trained on habitat-based radiomic features, achieved a diagnostic accuracy exceeding 94%. This superior performance, compared to traditional radiomics, highlights the ability of habitat-based radiomics to capture subtle tissue heterogeneity associated with PD. Our findings suggest that habitat-based radiomics could be a valuable tool for early and accurate PD diagnosis, enabling timely intervention and improved patient outcomes.

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