Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Habitat-based MRI radiomics for enhanced parkinson’s diagnosis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月3日

查看全文
  • Yuan-Zhe Li,
  • Yi Wang,
  • Chi Cai,
  • Si-Qing Cai,
  • Yi-Fan Guo,
  • Shu-Fen Liu,
  • Chun-Nuan Chen,
  • Tie-Qiang Li

摘要

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.