Scholarlink

<p>Freezing of gait (FOG) is a common and debilitating symptom in Parkinson’s disease (PD) that requires early detection for timely intervention. In this study, we developed an explainable SHAP-XGBoost model integrating clinical assessments and dopamine transporter (DAT) imaging to identify L-dopa responsive FOG. The internal cohort included 516 participants, with the model trained on a subset and validated on both internal and external test sets (Parkinson’s Progression Markers Initiative, PPMI). The model demonstrated strong predictive performance, achieving AUCs of 0.90, 0.89, and 0.75 on the internal training, internal test, and external PPMI sets, respectively. SHAP analysis revealed that Hoehn &amp; Yahr (H&amp;Y) staging and DAT availability in the contralateral anterior putamen were the most influential features. Threshold analyses identified key cutoffs: around 5 years for disease duration, 64 years for age, and 35.7 for MDS-UPDRS Part III score. Notably, among patients with milder motor symptoms (H&amp;Y ≤ 2.5), a contralateral anterior putamen SBR below 1.25 was associated with a higher FOG risk compared to those with H&amp;Y &gt; 2.5. In summary, our explainable model effectively detects L-dopa responsive FOG by leveraging clinical and DAT imaging data, emphasizing the contralateral anterior putamen as a critical region in FOG pathophysiology.</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

Explainable SHAP- XGBoost with DAT and clinical data for freezing of gait detection in Parkinson disease

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月8日

查看全文
  • Shuxian Jin,
  • Yumeng Qi,
  • Yayun Yan,
  • Wenhua Ren,
  • Xue Wang,
  • Ying Chang

摘要

Freezing of gait (FOG) is a common and debilitating symptom in Parkinson’s disease (PD) that requires early detection for timely intervention. In this study, we developed an explainable SHAP-XGBoost model integrating clinical assessments and dopamine transporter (DAT) imaging to identify L-dopa responsive FOG. The internal cohort included 516 participants, with the model trained on a subset and validated on both internal and external test sets (Parkinson’s Progression Markers Initiative, PPMI). The model demonstrated strong predictive performance, achieving AUCs of 0.90, 0.89, and 0.75 on the internal training, internal test, and external PPMI sets, respectively. SHAP analysis revealed that Hoehn & Yahr (H&Y) staging and DAT availability in the contralateral anterior putamen were the most influential features. Threshold analyses identified key cutoffs: around 5 years for disease duration, 64 years for age, and 35.7 for MDS-UPDRS Part III score. Notably, among patients with milder motor symptoms (H&Y ≤ 2.5), a contralateral anterior putamen SBR below 1.25 was associated with a higher FOG risk compared to those with H&Y > 2.5. In summary, our explainable model effectively detects L-dopa responsive FOG by leveraging clinical and DAT imaging data, emphasizing the contralateral anterior putamen as a critical region in FOG pathophysiology.