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Purpose <p>Accurate differentiation of Parkinsonism subtypes—including Parkinson’s disease (PD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP)—is essential for clinical prognosis and treatment planning. However, this remains a major challenge due to overlapping symptomatology and high inter-individual variability in cerebral glucose metabolism patterns observed on fluorodeoxyglucose positron emission tomography (FDG-PET).</p> Methods <p>To address these challenges, we propose PETFormer-SCL, a clinically informed deep learning framework that integrates convolutional neural networks (CNNs) with a channel-wise Transformer module, guided by supervised contrastive learning (SCL). This architecture is designed to enhance disease-specific feature learning while mitigating individual variability.</p> Results <p>Trained on 945 patients and evaluated on an independent test cohort of 330 patients (1275 in total), PETFormer-SCL achieved AUCs of 0.9830, 0.9702, and 0.9565 for MSA, PD, and PSP, respectively. In addition, class activation maps (CAMs) highlighted key disease-related brain regions—including the cerebellum, midbrain, and basal ganglia—demonstrating strong alignment with known pathophysiological findings.</p> Conclusions <p>PETFormer-SCL not only achieves high diagnostic accuracy, particularly for subtypes with overlapping phenotypes, but also enhances interpretability. These results support its potential as a reliable clinical decision-support tool for the early and differential diagnosis of Parkinsonism.</p>

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PETFormer-SCL: a supervised contrastive learning-guided CNN–transformer hybrid network for Parkinsonism classification from FDG-PET

  • 期刊论文

  • 发表日期：

    2025年7月14日

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  • Shaoyou Wu,
  • Chenyang Li,
  • Jiaying Lu,
  • Jingjie Ge,
  • Jing Wang,
  • Chuantao Zuo,
  • Zhilin Zhang,
  • Jiehui Jiang

摘要

Purpose

Accurate differentiation of Parkinsonism subtypes—including Parkinson’s disease (PD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP)—is essential for clinical prognosis and treatment planning. However, this remains a major challenge due to overlapping symptomatology and high inter-individual variability in cerebral glucose metabolism patterns observed on fluorodeoxyglucose positron emission tomography (FDG-PET).

Methods

To address these challenges, we propose PETFormer-SCL, a clinically informed deep learning framework that integrates convolutional neural networks (CNNs) with a channel-wise Transformer module, guided by supervised contrastive learning (SCL). This architecture is designed to enhance disease-specific feature learning while mitigating individual variability.

Results

Trained on 945 patients and evaluated on an independent test cohort of 330 patients (1275 in total), PETFormer-SCL achieved AUCs of 0.9830, 0.9702, and 0.9565 for MSA, PD, and PSP, respectively. In addition, class activation maps (CAMs) highlighted key disease-related brain regions—including the cerebellum, midbrain, and basal ganglia—demonstrating strong alignment with known pathophysiological findings.

Conclusions

PETFormer-SCL not only achieves high diagnostic accuracy, particularly for subtypes with overlapping phenotypes, but also enhances interpretability. These results support its potential as a reliable clinical decision-support tool for the early and differential diagnosis of Parkinsonism.