Abstract
PURPOSE: We evaluate the ability of Artificial Intelligence with automatic classification methods applied to semi-quantitative data from brain (18)F-FDG PET/CT to improve the differential diagnosis between Alzheimer Disease (AD) and Mild Cognitive Impairment (MCI). PROCEDURES: We retrospectively analyzed a total of 150 consecutive patients who underwent diagnostic evaluation for suspected AD (n = 67) or MCI (n = 83). All patients received brain 18F-FDG PET/CT according to the international guidelines, and images were analyzed both Qualitatively (QL) and Quantitatively (QN), the latter by a fully automated post-processing software that produced a z score metabolic map of 25 anatomically different cortical regions. A subset of n = 122 cases with a confirmed diagnosis of AD (n = 53) or MDI (n = 69) by 18-24-month clinical follow-up was finally included in the study. Univariate analysis and three automated classification models (classification tree -ClT-, ridge classifier -RC- and linear Support Vector Machine -lSVM-) were considered to estimate the ability of the z scores to discriminate between AD and MCI cases in. RESULTS: The univariate analysis returned 14 areas where the z scores were significantly different between AD and MCI groups, and the classification accuracy ranged between 74.59% and 76.23%, with ClT and RC providing the best results. The best classification strategy consisted of one single split with a cut-off value of ≈ -2.0 on the z score from temporal lateral left area: cases below this threshold were classified as AD and those above the threshold as MCI. CONCLUSIONS: Our findings confirm the usefulness of brain 18F-FDG PET/CT QL and QN analyses in differentiating AD from MCI. Moreover, the combined use of automated classifications models can improve the diagnostic process since its use allows identification of a specific hypometabolic area involved in AD cases in respect to MCI. This data improves the traditional 18F-FDG PET/CT image interpretation and the diagnostic assessment of cognitive disorders.