FDG-PET Image Classification in Alzheimer's Disease: from Traditional Visual Analysis to Advanced Transfer Learning.

PURPOSE

Alzheimer's disease (AD) often coexists with other brain pathologies, and we aimed to classify people with AD using 18 F- Flouro-Deoxy-Glucose-Positron Emission Tomography (FDG-PET).

METHOD

Baseline FDG-PET data were collected as part of two large scale Phase III clinical trials of a novel tau aggregation inhibitor drug, Leuco-Methylthioninium (LMTX®). A total of 794, well-characterised probable AD subjects were included in the study and the images were classified into "typical AD"(temporoparietal hypometabolism) and "mixed" (patchy hypo-metabolism in other vascular territories of brain such as frontal and cerebellar regions along with temporo-parietal hypo-metabolism) patterns based on visual interpretation. The differences in the two groups were further assessed with region-of-interest based analysis of Standardized Uptake Value Ratio (SUVR) and automated classification using transfer learning with visual classification as the gold standard.

RESULTS

Of the total of 794 (438 female) participants, 533 (284 female) were classified as typical AD and 261 (154 female) participants classified as mixed. A subset of 50 images each from typical and mixed subtypes were used for transfer learning and sensitivity, specificity and accuracy for one of the cross-validation loops was 94.73%, 95.23% and 95% respectively. The average accuracy to distinguish the two subtypes after 5-fold cross validation was found to be 97.5%.

CONCLUSIONS

This study is first of its kind to distinguish two subtypes of AD through visual interpretation of FDG-PET images and exploring the findings with a semi-quantitative method followed by transfer learning, which has been used to predict the two subtypes with high accuracy, sensitivity and specificity.