Ling, Ronghua; Wang, Min; Lu, Jiaying; Wu, Shaoyou; Wu, Ping; Ge, Jingjie; Wang, Luyao; Liu, Yingqian; Jiang, Juanjuan; Shi, Kuangyu; Yan, Zhuangzhi; Zuo, Chuantao; Jiang, Jiehui (2024). Radiomics-Guided Deep Learning Networks Classify Differential Diagnosis of Parkinsonism. Brain Sciences, 14(7) MDPI 10.3390/brainsci14070680
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The differential diagnosis between atypical Parkinsonian syndromes may be challenging and critical. We aimed to proposed a radiomics-guided deep learning (DL) model to discover interpretable DL features and further verify the proposed model through the differential diagnosis of Parkinsonian syndromes. We recruited 1495 subjects for 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) scanning, including 220 healthy controls and 1275 patients diagnosed with idiopathic Parkinson's disease (IPD), multiple system atrophy (MSA), or progressive supranuclear palsy (PSP). Baseline radiomics and two DL models were developed and tested for the Parkinsonian diagnosis. The DL latent features were extracted from the last layer and subsequently guided by radiomics. The radiomics-guided DL model outperformed the baseline radiomics approach, suggesting the effectiveness of the DL approach. DenseNet showed the best diagnosis ability (sensitivity: 95.7%, 90.1%, and 91.2% for IPD, MSA, and PSP, respectively) using retained DL features in the test dataset. The retained DL latent features were significantly associated with radiomics features and could be interpreted through biological explanations of handcrafted radiomics features. The radiomics-guided DL model offers interpretable high-level abstract information for differential diagnosis of Parkinsonian disorders and holds considerable promise for personalized disease monitoring.
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Clinic of Nuclear Medicine |
UniBE Contributor: |
Shi, Kuangyu |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
2076-3425 |
Publisher: |
MDPI |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
29 Jul 2024 10:08 |
Last Modified: |
29 Jul 2024 10:16 |
Publisher DOI: |
10.3390/brainsci14070680 |
PubMed ID: |
39061420 |
Uncontrolled Keywords: |
Parkinsonian brain PET imaging deep learning glucose metabolism radiomics |
BORIS DOI: |
10.48350/199329 |
URI: |
https://boris.unibe.ch/id/eprint/199329 |
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