Annals of nuclear medicine · 2025
PETFormer-SCL: a supervised contrastive learning-guided CNN-transformer hybrid network for Parkinsonism classification from FDG-PET.
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Wu Shaoyou, Li Chenyang, Lu Jiaying, Ge Jingjie, Wang Jing, Zuo Chuantao, Zhang Zhilin, Jiang Jiehui
Аннотация
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.
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.