Abstract #0993

Quantitative MRI Interpretable 100D Feature Space of Knee Osteoarthritis

Gabrielle Hoyer¹, Kenneth Gao¹, Jinhee Lee¹, Johanna Luitjens², Felix Gassert², Sharmila Majumdar², and Valentina Pedoia²

¹Department of Radiology and Biomedical Imaging, UCSF; University of California Berkeley–University of California San Francisco Graduate Program in Bioengineering, University of California, San Francisco; University of California Berkeley, San Francisco, CA, United States, ²Department of Radiology and Biomedical Imaging, UCSF, University of California, San Francisco, San Francisco, CA, United States

Synopsis

Keywords: Osteoarthritis, MSK, statistical shape modeling, cartilage thickness, cartilage T2, meniscus

While the Osteoarthritis Initiative data has been explored in independent studies, to our knowledge, never has such a comprehensive analysis been completed, investigating morphology of femur, patella, tibia, menisci, for biomarkers: cartilage thickness, cartilage T2, bone shape, and meniscus shape. With 4,796 subjects from the OAI, this study utilizes an automatic statistical shape modeling technique to create a 100D interpretable space of biomarker-tissue PC modes, which can be disseminated using an innovative opensource app. In combination with Image Twin and Clinical Twin analyses, variations in morphology, as well as clinical metadata, were identified as being significantly associated with osteoarthritis incidence.

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