The use of random forests to classify amyloid brain PET
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Date
2019-10
Authors
Zukotynski, Katherine
Gaudet, Vincent C.
Kuo, Phillip H.
Adamo, Sabrina
Goubran, Maged
Scott, Christopher
Bocti, Christian
Borrie, Michael
Chertkow, Howard
Frayne, Richard
Journal Title
Journal ISSN
Volume Title
Publisher
Wolters Kluwer Health
Abstract
Purpose: To evaluate random forests (RFs) as a supervised machine learning algorithm to classify amyloid brain PET as positive or negative for amyloid deposition and identify key regions of interest for stratification.
Methods: The data set included 57 baseline 18F-florbetapir (Amyvid; Lilly, Indianapolis, IN) brain PET scans in participants with severe white matter disease, presenting with either transient ischemic attack/lacunar stroke or mild cognitive impairment from early Alzheimer disease, enrolled in a multicenter prospective observational trial. Scans were processed using the MINC toolkit to generate SUV ratios, normalized to cerebellar gray matter, and clinically read by 2 nuclear medicine physicians with interpretation based on consensus (35 negative, 22 positive). SUV ratio data and clinical reads were used for super- vised training of an RF classifier programmed in MATLAB.
Results: A 10,000-tree RF, each tree using 15 randomly selected cases and 20 randomly selected features (SUV ratio per region of interest), with 37 cases for training and 20 cases for testing, had sensitivity = 86% (95% confidence in- terval [CI], 42%–100%), specificity = 92% (CI, 64%–100%), and classification accuracy = 90% (CI, 68%–99%). The most common features at the root node (key regions for stratification) were (1) left posterior cingulate (1039 trees), (2) left middle frontal gyrus (1038 trees), (3) left precuneus (857 trees), (4) right an- terior cingulate gyrus (655 trees), and (5) right posterior cingulate (588 trees). Conclusions: Random forests can classify brain PET as positive or negative for amyloid deposition and suggest key clinically relevant, regional features for classification.
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Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.
Keywords
amyloid, brain PET, dementia, machine learning, random forest