Textural properties of microglial activation in Alzheimer's disease as measured by (R)-[11C]PK11195 PET

• Autor(a) principal: Lapo Pais, Marta
• Data de Publicação: 2023
• Outros Autores: Jorge, Lília, Martins, Ricardo, Canário, Nádia, Xavier, Ana Carolina, Bernardes, Rui, Abrunhosa, Antero, Santana, Isabel, Castelo-Branco, Miguel
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/107190; https://doi.org/10.1093/braincomms/fcad148

Alzheimer's disease is the most common form of dementia worldwide, accounting for 60-70% of diagnosed cases. According to the current understanding of molecular pathogenesis, the main hallmarks of this disease are the abnormal accumulation of amyloid plaques and neurofibrillary tangles. Therefore, biomarkers reflecting these underlying biological mechanisms are recognized as valid tools for an early diagnosis of Alzheimer's disease. Inflammatory mechanisms, such as microglial activation, are known to be involved in Alzheimer's disease onset and progression. This activated state of the microglia is associated with increased expression of the translocator protein 18 kDa. On that account, PET tracers capable of measuring this signature, such as (R)-[11C]PK11195, might be instrumental in assessing the state and evolution of Alzheimer's disease. This study aims to investigate the potential of Gray Level Co-occurrence Matrix-based textural parameters as an alternative to conventional quantification using kinetic models in (R)-[11C]PK11195 PET images. To achieve this goal, kinetic and textural parameters were computed on (R)-[11C]PK11195 PET images of 19 patients with an early diagnosis of Alzheimer's disease and 21 healthy controls and submitted separately to classification using a linear support vector machine. The classifier built using the textural parameters showed no inferior performance compared to the classical kinetic approach, yielding a slightly larger classification accuracy (accuracy of 0.7000, sensitivity of 0.6957, specificity of 0.7059 and balanced accuracy of 0.6967). In conclusion, our results support the notion that textural parameters may be an alternative to conventional quantification using kinetic models in (R)-[11C]PK11195 PET images. The proposed quantification method makes it possible to use simpler scanning procedures, which increase patient comfort and convenience. We further speculate that textural parameters may also provide an alternative to kinetic analysis in (R)-[11C]PK11195 PET neuroimaging studies involving other neurodegenerative disorders. Finally, we recognize that the potential role of this tracer is not in diagnosis but rather in the assessment and progression of the diffuse and dynamic distribution of inflammatory cell density in this disorder as a promising therapeutic target.