Evidence map›Paper›PMID 40660058›Full record

ArticleAnnals of nuclear medicine2025

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

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

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Article in Annals of nuclear medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Shaoyou Wu *Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, 200444, China.
Chenyang Li *Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, 200444, China.
Jiaying LuDepartment of Nuclear Medicine & PET Center, National Clinical Research Center for Aging and Medicine, & National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, 200437, China.
Jingjie GeDepartment of Nuclear Medicine & PET Center, National Clinical Research Center for Aging and Medicine, & National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, 200437, China.
Jing WangDepartment of Nuclear Medicine & PET Center, National Clinical Research Center for Aging and Medicine, & National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, 200437, China.
Chuantao ZuoDepartment of Nuclear Medicine & PET Center, National Clinical Research Center for Aging and Medicine, & National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, 200437, China. zuochuantao@fudan.edu.cn.
Zhilin ZhangResearch Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China. zhangzhilin@siat.ac.cn.
Jiehui JiangInstitute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, 200444, China. jiangjiehui@shu.edu.cn.ORCID http://orcid.org/0000-0003-4948-3683

Funding

National Natural Science Foundation of China 62376150National Natural Science Foundation of China 82020108013Science and Technology Innovation 2030 - Major Projects 2022ZD0211600Science and Technology Innovation 2030 - Major Projects 2022ZD0211606Shanghai Industrial Collaborative Innovation Project XTCX-KJ-2023-37
6 · The paper itself

Abstract

purposeAccurate 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).

methodsTo 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.

resultsTrained 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.

conclusionsPETFormer-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.

Indexed as

Deep LearningFluorodeoxyglucose F18Image Processing, Computer-AssistedNeural Networks, ComputerParkinsonian DisordersPositron-Emission TomographyAgedDiagnosis, DifferentialFemaleHumansMaleMiddle AgedParkinson DiseaseFluorodeoxyglucose F18Clinical interpretabilityCNN–Transformer hybridDeep learningFDG-PETMetabolic brain imagingNeurodegenerative disordersParkinsonism classificationSupervised contrastive learning

Identifiers

PMID40660058

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