Lapo Pais Marta, Jorge Lília, Martins Ricardo, Canário Nádia, Xavier Ana Carolina, Bernardes Rui, Abrunhosa Antero, Santana Isabel, Castelo-Branco Miguel
Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, 3000-548 Coimbra, Portugal.
Clinical Academic Centre of Coimbra (CACC), Faculty of Medicine (FMUC), University of Coimbra, 3000-548 Coimbra, Portugal.
Brain Commun. 2023 May 6;5(3):fcad148. doi: 10.1093/braincomms/fcad148. eCollection 2023.
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)-[C]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)-[C]PK11195 PET images. To achieve this goal, kinetic and textural parameters were computed on (R)-[C]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)-[C]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)-[C]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.
阿尔茨海默病是全球最常见的痴呆形式，占已确诊病例的60 - 70%。根据目前对分子发病机制的理解，该疾病的主要特征是淀粉样斑块和神经原纤维缠结的异常积累。因此，反映这些潜在生物学机制的生物标志物被认为是早期诊断阿尔茨海默病的有效工具。炎症机制，如小胶质细胞激活，已知与阿尔茨海默病的发病和进展有关。小胶质细胞的这种激活状态与18 kDa转位蛋白的表达增加有关。因此，能够测量这种特征的PET示踪剂，如(R)-[C]PK11195，可能有助于评估阿尔茨海默病的状态和进展。本研究旨在探讨基于灰度共生矩阵的纹理参数在(R)-[C]PK11195 PET图像中作为使用动力学模型进行传统量化的替代方法的潜力。为实现这一目标，计算了19例早期诊断为阿尔茨海默病的患者和21名健康对照的(R)-[C]PK11195 PET图像的动力学和纹理参数，并分别使用线性支持向量机进行分类。使用纹理参数构建的分类器与经典动力学方法相比表现并不逊色，分类准确率略高（准确率为0.7000，灵敏度为0.6957，特异性为0.7059，平衡准确率为0.6967）。总之，我们的结果支持这样一种观点，即纹理参数可能是(R)-[C]PK11195 PET图像中使用动力学模型进行传统量化的替代方法。所提出的量化方法使得可以使用更简单的扫描程序，这增加了患者的舒适度和便利性。我们进一步推测，在涉及其他神经退行性疾病的(R)-[C]PK11195 PET神经影像学研究中，纹理参数也可能为动力学分析提供一种替代方法。最后，我们认识到这种示踪剂的潜在作用不在于诊断，而在于作为一个有前景的治疗靶点评估该疾病中炎症细胞密度的弥漫性和动态分布及其进展。
