A Multi-Criteria Decision Aid Tool for Radiopharmaceutical Selection in Tau PET Imaging

Abstract

The accumulation of pathologically misfolded tau is a feature shared by a group of neurodegenerative disorders collectively referred to as tauopathies. Alzheimer’s disease (AD) is the most prevalent of these tauopathies. Immunohistochemical evaluation allows neuropathologists to visualize paired-helical filaments (PHFs)—tau pathological lesions, but this is possible only after death and only shows tau in the portion of brain sampled. Positron emission tomography (PET) imaging allows both the quantitative and qualitative analysis of pathology over the whole brain of a living subject. The ability to detect and quantify tau pathology in vivo using PET can aid in the early diagnosis of AD, provide a way to monitor disease progression, and determine the effectiveness of therapeutic interventions aimed at reducing tau pathology. Several tau-specific PET radiotracers are now available for research purposes, and one is approved for clinical use. This study aims to analyze, compare, and rank currently available tau PET radiotracers using the fuzzy preference ranking organization method for enrichment of evaluations (PROMETHEE), which is a multi-criteria decision-making (MCDM) tool. The evaluation is based on relatively weighted criteria, such as specificity, target binding affinity, brain uptake, brain penetration, and rates of adverse reactions. Based on the selected criteria and assigned weights, this study shows that a second-generation tau tracer, [18F]RO-948, may be the most favorable. This flexible method can be extended and updated to include new tracers, additional criteria, and modified weights to help researchers and clinicians select the optimal tau PET tracer for specific purposes. Additional work is needed to confirm these results, including a systematic approach to defining and weighting criteria and clinical validation of tracers in different diseases and patient populations.

Assessment of UTI Diagnostic Techniques Using the Fuzzy–PROMETHEE Model

“…By assessing the variations in how the choices are ranked, the F-PROMETHEE is an MCDM method utilized to clarify the ambiguity parameters of choice problems and compare alternative pairs' choices for each criterion. One of the most widely utilized methods for making decisions in various fields [26], the F-PROMETHEE approach has recently been applied by numerous researchers in the medical and health fields [26,31,32]. This technique combines the PROMETHEE method with fuzzy logic and aims to simplify the issue into a manageable form, which enables decision makers to incorporate non-crisp parameters into the system, examine them, and rank alternatives based on their criteria [32].…”