Combined quantitative amyloid-ß PET and structural MRI features improve Alzheimer's Disease classification in random forest model - A multicenter study.

ABSTRACT

RATIONALE AND OBJECTIVES: Prior to clinical presentations of Alzheimer's Disease (AD), neuropathological changes, such as amyloid-ß and brain atrophy, have accumulated at the earlier stages of the disease. The combination of such biomarkers assessed by multiple modalities commonly improves the likelihood of AD etiology. We aimed to explore the discriminative ability of Aß PET features and whether combining Aß PET and structural MRI features can improve the classification performance of the machine learning model in older healthy control (OHC) and mild cognitive impairment (MCI) from AD. MATERIAL AND METHODS: We collected 94 AD patients, 82 MCI patients, and 85 OHC from three different cohorts. 17 global/regional Aß features in Centiloid, 122 regional volume, and 68 regional cortical thickness were extracted as imaging features. Single or combined modality features were used to train the random forest model on the testing set. The top 10 features were sorted based on the Gini index in each binary classification. RESULTS: The results showed that AUC scores were 0.81/0.86 and 0.69/0.68 using sMRI/Aß PET features on the testing set in differentiating OHC and MCI from AD. The performance was improved while combining two-modality features with an AUC of 0.89 and an AUC of 0.71 in two classifications. Compared to sMRI features, particular Aß PET features contributed more to differentiating AD from others. CONCLUSION: Our study demonstrated the discriminative ability of Aß PET features in differentiating AD from OHC and MCI. A combination of Aß PET and structural MRI features can improve the RF model performance.