MRI has been instrumental in exploring the development, maintenance, functional adaptation, and degeneration of articular cartilage as it has made it possible to extract the geometric dimensions of the tissue in vivo (1–3). In the study of osteoarthritis (OA) and other joint diseases, morphologic measures of cartilage are frequently obtained to assess disease status or progression. Metrics of cartilage morphology (volume, thickness, and others) based on MRI have been shown to be reproducible in single (1, 3) and multicenter studies (4) and hold promise for evaluating the treatment response of structure/disease-modifying drugs. Several studies have reported the rate and sensitivity to change of cartilage morphology measures in participants with OA (5–11) and healthy persons (12–15) using 1.5T (1, 3, 16) or 3T MRI (17–19). Some of these studies have compared measures of change based on MRI to joint-space narrowing from radiographs (7, 10, 11, 17, 20).

An international group of experts has recommended definitions and nomenclature for MRI-based measures of cartilage (21), and these involve, among others:

VC: Volume of the cartilage

tAB: Total area of the (subchondral) bone

AC: Area of the cartilage surface

cAB: tAB covered by the AC

dABp: Percentage of tAB not covered by the AC = 100 × (1 – cAB/tAB)

ThCtAB.Me: Mean cartilage thickness over the tAB

ThCcAB.Me: Mean cartilage thickness over the cAB

VCtAB: Volume normalized to the tAB

These measurements can be taken on several knee surfaces, e.g., medial and lateral tibial plates (MT, LT), medial and lateral weight-bearing femoral plates (cMF, cLF), and specific regions of these plates, thus creating a large (if not overwhelming) set of measurements available for examination and statistical testing in a given study. As many of these morphologic measures are strongly related, some may be redundant or contain minimal additional information.

The general goal of this analysis was to identify an efficient subset of core measures that comprises a comprehensive description of cartilage morphology and its longitudinal changes in healthy and diseased cartilage. A subset of measures will be considered efficient if it maximizes the information present in all measures in a given (minimal) number of measurements. Information is equivalent to the observed variation in the measures in the study sample. This exercise could be accomplished through the use of various statistical methods, e.g., principal components; however, maintaining the original measures as endpoints in clinical studies is highly desirable, so the search for a subset was undertaken with this constraint in mind. In practice, particularly if a number of measures are highly correlated, several equally valid efficient subsets may exist. The choice of the efficient subset will therefore also be based on the subjective interpretation of the measures selected.

The first specific objective of this study was to examine the relationship between commonly reported measures of knee cartilage in a relatively large cross-sectional and longitudinal study of participants with and without knee OA. The second specific objective was to identify an efficient subset of cartilage morphology measures that successfully explains most of the variation observed cross-sectionally and longitudinally in other measures not included in the efficient subset.

The following steps were taken to achieve the above objectives.

- 1The mathematical relationships between morphologic measurements under consideration were examined.
- 2The hypothesis that volume (VC) can be accurately predicted (both cross-sectionally and longitudinally, i.e., changes in VC) by a simple function of surface areas (tAB, AC, or cAB) and thickness (ThCcAB.Me or ThCtAB.Me) was examined.
- 3Regression models were constructed to determine whether simple additive models limited to surface area and thickness measures could explain most of the variation in volume, again both cross-sectionally and longitudinally (i.e., change in volume).

The overarching goal of these objectives was to identify an efficient subset of morphologic measures based on known (mathematical) and empirically constructed biologic and physical relationships that maximizes the information provided while minimizing the number of variables in the subset. The potential benefits of identifying a subset of measures include increased consistency and efficiency of reporting results, increased statistical power due to reduction in multiple comparisons made on a larger number of measures, and a better understanding of the relationships between the morphologic measures of articular knee cartilage.