Increasing the confidence of 18F-Florbetaben PET interpretations: Machine learning quantitative approximation
| Autor: | Santiago Navarro Muñoz, Ana María García Vicente, Mariano Amo-Salas, María Jesús Tello Galán, Francisco José Pena Pardo, Carlos Marsal Alonso, Ángel Soriano Castrejón, Beatriz Mondejar Marín, Ignacio Rueda Medina, Víctor Manuel Poblete García |
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| Rok vydání: | 2022 |
| Předmět: |
Amyloid β
business.industry General Engineering Standardized uptake value Machine learning computer.software_genre 18F-florbetaben Multicenter study Posterior cingulate Retrospective analysis General Earth and Planetary Sciences Medicine In patient Artificial intelligence business Cognitive impairment computer General Environmental Science |
| Zdroj: | Revista Española de Medicina Nuclear e Imagen Molecular (English Edition). 41:153-163 |
| ISSN: | 2253-8089 |
| DOI: | 10.1016/j.remnie.2021.03.014 |
| Popis: | Aim To assess the added value of semiquantitative parameters on the visual assessment and to study the patterns of 18F-Florbetaben brain deposition. Materials and methods Retrospective analysis of multicenter study performed in patients with mild cognitive impairment or dementia of uncertain origin. 18F-Florbetaben PET scans were visually interpreted by two experienced observers, analyzing target regions in order to calculate the interobserver agreement. Semiquantification of all cortical regions with respect to three reference regions was performed to obtain standardized uptake value ratios (SUVRs). The ability of SUVRs to predict the visual evaluation, the possibility of preferential radiotracer deposition in some target regions and interhemisphere differences were analyzed. Results 135 patients were evaluated. In the visual assessment, 72 were classified as positive. Interobserver agreement was excellent. All SUVRs were significantly higher in positive PET scans than in negative ones. Prefrontal area and posterior cingulate were the cortical regions with the best correlations with the visual evaluation, followed by the composite region. Using ROC analysis, the SUVRs obtained in same target locations showed the best diagnostic performance. Conclusions The derived information from target regions seems to help the visual classification, based on a preferential amyloid β deposit, allowing machine learning. The amyloid β deposit, although diffuse in all cortical regions, seems not to be uniform and symmetric. |
| Databáze: | OpenAIRE |
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