Combined blood Neurofilament light chain and third ventricle width to differentiate Progressive Supranuclear Palsy from Parkinson's Disease: a machine learning study

Abstract

Introduction

Differentiating Progressive Supranuclear Palsy (PSP) from Parkinson's Disease (PD) may be clinically challenging. In this study, we explored the performance of machine learning models based on MR imaging and blood molecular biomarkers in distinguishing between these two neurodegenerative diseases.

Methods

Twenty-eight PSP patients, 46 PD patients and 60 control subjects (HC) were consecutively enrolled in the study. Serum concentration of neurofilament light chain protein (Nf-L) was assessed by single molecule array (SIMOA), while an automatic segmentation algorithm was employed for T1-weighted measurements of third ventricle width/intracranial diameter ratio (3^rdV/ID). Machine learning (ML) models with Logistic Regression (LR), Random Forest (RF), and XGBoost algorithms based on 3^rdV/ID and blood Nf-L levels were tested in distinguishing among PSP, PD and HC.

Results

PSP patients showed higher serum Nf-L levels and larger 3^rdV/ID ratio in comparison with both PD and HC groups (p<0.005). All ML algorithms (LR, RF and XGBoost) showed that the combination of MRI and blood biomarkers had excellent classification performances in differentiating PSP from PD (AUC ≥0.92), outperforming each biomarker used alone (AUC: 0.85-0.90). Among the different algorithms, XGBoost was slightly more powerful than LR and RF in distinguishing PSP from PD patients, reaching AUC=0.94 ±0.04.

Conclusion

Our findings highlight the usefulness of combining serum blood and simple linear MRI biomarkers to accurately distinguish between PSP and PD. This multimodal approach may play a pivotal role in patient management and clinical decision-making, paving the way for more effective and timely interventions, in these neurodegenerative diseases.