Evaluación de los sistemas de calificación radiológica de la estenosis foraminal lumbar: una revisión sistemática

Symptomatic lumbar foraminal stenosis (LFS) occurs when the neuroforamen narrows, compressing the exiting spinal nerve, leading to symptoms such as radicular pain, paresthesias, and potentially weakness. Although cross-sectional imaging studies are used for diagnostic purposes, there is no clear consensus as to which grading system best evaluates LFS, predisposing to inconsistencies in care. This systematic review aimed to evaluate and compare existing published grading systems for LFS to identify (1) systems most used within the literature and (2) the most effective and reliable method for classifying anatomic severity and clinical symptom correlation.

This study is a systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, analyzing available literature on grading systems for LFS, level of evidence IV. A comprehensive search of PubMed, Embase, and Cochrane Trials was conducted from inception through July 2024. Eligible studies were evaluated for methods, bias, sample size, patient demographics, imaging modalities, and grading systems. Bias was assessed using the Methodological Index for Non-Randomized Studies. Data were synthesized narratively and descriptively.

The review included 35 studies, most using magnetic resonance imaging (88.6%). Seven grading systems have been identified. The original Lee et al. grading system was the most frequently used LFS grading system (69%), followed by Wildermuth et al. (14.3%). Notably, artificial intelligence (AI) grading systems were included in two studies (5.7%). Findings regarding symptom correlation were mixed.

The Lee et al. grading system remains the most used grading system for LFS in the literature and is reliable. Several small studies found an association between the Lee et al. system and clinical symptoms/treatment outcomes; however, this was not universally found. Further investigation is needed to validate the newer grading. The introduction of AI may offer promise for refining the diagnostic and clinical utility of published LFS grading systems.