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- Development and validation of a radiomic model for the diagnosis of dopaminergic denervation on [18F]FDOPA PET/CT
Development and validation of a radiomic model for the diagnosis of dopaminergic denervation on [18F]FDOPA PET/CT
Authors
Victor Comte, Hugo Schmutz, David Chardin, Fanny Orlhac, Jacques Darcourt, Olivier Humbert
Abstract
Abstract
Purpose
FDOPA PET shows good performance for the diagnosis of striatal dopaminergic denervation, making it a valuable tool for the differential diagnosis of Parkinsonism. Textural features are image biomarkers that could potentially improve the early diagnosis and monitoring of neurodegenerative parkinsonian syndromes. We explored the performances of textural features for binary classification of FDOPA scans.
Methods
We used two FDOPA PET datasets: 443 scans for feature selection, and 100 scans from a different PET/CT system for model testing. Scans were labelled according to expert interpretation (dopaminergic denervation versus no dopaminergic denervation). We built LASSO logistic regression models using 43 biomarkers including 32 textural features. Clinical data were also collected using a shortened UPDRS scale.
Results
The model built from the clinical data alone had a mean area under the receiver operating characteristics (AUROC) of 63.91. Conventional imaging features reached a maximum score of 93.47 but the addition of textural features significantly improved the AUROC to 95.73 (
Conclusion
A simple model with three radiomic features can identify pathologic FDOPA PET scans with excellent sensitivity and specificity. Textural features show promise for the diagnosis of parkinsonian syndromes.