期刊
CLINICAL ANATOMY
卷 -, 期 -, 页码 -出版社
WILEY
DOI: 10.1002/ca.24127
关键词
cartilage, articular; factor analysis, statistical; knee; osteoarthritis
This study challenges the traditional approach of evaluating articular cartilage loss based on anatomical regions and explores the impact of specific topographical locations on knee cartilage loss using a data-driven approach.
ObjectiveWe challenge the paradigm that a simplistic approach evaluating anatomic regions (e.g., medial femur or tibia) is ideal for assessing articular cartilage loss on magnetic resonance (MR) imaging. We used a data-driven approach to explore whether specific topographical locations of knee cartilage loss may identify novel patterns of cartilage loss over time that current assessment strategies miss.DesignWe assessed 60 location-specific measures of articular cartilage on a sample of 99 knees with baseline and 24-month MR images from the Osteoarthritis Initiative, selected as a group with a high likelihood to change. We performed factor analyses of the change in these measures in two ways: (1) summing the measures to create one measure for each of the six anatomically regional-based summary (anatomic regions; e.g., medial tibia) and (2) treating each location separately for a total of 60 measures (location-specific measures).ResultsThe first analysis produced three factors accounting for 66% of the variation in the articular cartilage changes that occur over 24 months of follow-up: (1) medial tibiofemoral, (2) medial and lateral patellar, and (3) lateral tibiofemoral. The second produced 20 factors accounting for 75% of the variance in cartilage changes. Twelve factors only involved one anatomic region. Five factors included locations from adjoining regions (defined by the first analysis; e.g., medial tibiofemoral). Three factors included articular cartilage loss from disparate locations.ConclusionsNovel patterns of cartilage loss occur within each anatomic region and across these regions, including in disparate regions. The traditional anatomic regional approach is simpler to implement and interpret but may obscure meaningful patterns of change.
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