Aditya Vedantam, Vedantam Rajshekhar


January 2013, Volume 22, Issue 1, pp 96 - 106 Review Article Read Full Article 10.1007/s00586-012-2483-9

First Online: 25 August 2012

Purpose

To review the literature on different classifications of T2-weighted (T2W) increased signal intensity (ISI) on preoperative magnetic resonance (MR) images of patients with cervical spondylotic myelopathy (CSM).

Methods

The authors searched the databases of PubMed and Cochrane for studies that used a categorization of T2W ISI to predict the functional outcome after decompressive surgery for CSM. Selected studies were analyzed for the type of ISI classification used, patient selection, methodology and results. The level of evidence provided by each study was determined.

Results

Twenty-two studies fulfilled our search criteria. There were 11 prospective studies and a total of 1,508 patients were studied. The majority of studies classified ISI based on either the longitudinal extent (12 studies) or the qualitative features of the ISI (10 studies). Three studies used both parameters to classify T2W ISI. Other classifications were based on the position of ISI (1 study), presence of snake-eye appearance on axial MR images (1 study) and signal intensity ratio (SIR) (1 study). Poorer functional outcomes correlated with sharp, intense ISI (6 studies) and multisegmental ISI (5 studies) (Class II evidence). Five of ten studies reported that the regression of ISI postoperatively was associated with better neurological outcomes (Class II evidence).

Conclusions

Methodological variations in previous studies made it difficult to compare studies and results. Both multisegmental T2W ISI and sharp, intense T2W ISI are associated with poorer surgical outcome (Class II evidence). The regression of T2W ISI postoperatively correlates with better functional outcomes (Class II). Future studies on the significance of ISI should ensure use of a uniform grading system, standardized outcome measures and multivariate analyses to control for other preoperative variables.


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