PCA and logistic regression in 2-[18F]FDG PET neuroimaging as an interpretable and diagnostic tool for Alzheimer’s disease

• Published in: Physics in medicine & biology Vol. 69; no. 2; pp. 25003 - 25016
• Main Authors: Gonçalves de Oliveira, Carlos Eduardo, de Araújo, Whemberton Martins, de Jesus Teixeira, Ana Beatriz Marinho, Gonçalves, Gustavo Lopes, Itikawa, Emerson Nobuyuki
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 21.01.2024
• Subjects: Alzheimer Disease - diagnostic imaging; artificial intelligence; Brain - diagnostic imaging; FDG PET; Fluorodeoxyglucose F18; Humans; Logistic Models; logistic regression; neurodegenerative disease; Neuroimaging; Positron-Emission Tomography - methods; principal component analysis; Radiopharmaceuticals; neurodegenerative disease; logistic regression; FDG PET; artificial intelligence; principal component analysis
• ISSN: 0031-9155; 1361-6560; 1361-6560
• DOI: 10.1088/1361-6560/ad0ddd

Objective. to develop an optimization and training pipeline for a classification model based on principal component analysis and logistic regression using neuroimages from PET with 2-[ 18 F]fluoro-2-deoxy-D-glucose (FDG PET) for the diagnosis of Alzheimer’s disease (AD). Approach. as training data, 200 FDG PET neuroimages were used, 100 from the group of patients with AD and 100 from the group of cognitively normal subjects (CN), downloaded from the repository of the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Regularization methods L1 and L2 were tested and their respective strength varied by the hyperparameter C. Once the best combination of hyperparameters was determined, it was used to train the final classification model, which was then applied to test data, consisting of 192 FDG PET neuroimages, 100 from subjects with no evidence of AD (nAD) and 92 from the AD group, obtained at the Centro de Diagnóstico por Imagem (CDI). Main results. the best combination of hyperparameters was L1 regularization and C ≈ 0.316. The final results on test data were accuracy = 88.54%, recall = 90.22%, precision = 86.46% and AUC = 94.75%, indicating that there was a good generalization to neuroimages outside the training set. Adjusting each principal component by its respective weight, an interpretable image was obtained that represents the regions of greater or lesser probability for AD given high voxel intensities. The resulting image matches what is expected by the pathophysiology of AD. Significance. our classification model was trained on publicly available and robust data and tested, with good results, on clinical routine data. Our study shows that it serves as a powerful and interpretable tool capable of assisting in the diagnosis of AD in the possession of FDG PET neuroimages. The relationship between classification model output scores and AD progression can and should be explored in future studies.