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Classification of histologically scored human knee osteochondral plugs by quantitative analysis of magnetic resonance images at 3T
Author(s) -
Lukas Vanessa A.,
Fishbein Kenneth W.,
Lin PingChang,
Schär Michael,
Schneider Erika,
Neu Corey P.,
Spencer Richard G.,
Reiter David A.
Publication year - 2015
Publication title -
journal of orthopaedic research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.041
H-Index - 155
eISSN - 1554-527X
pISSN - 0736-0266
DOI - 10.1002/jor.22810
Subject(s) - magnetic resonance imaging , osteoarthritis , medicine , cartilage , mahalanobis distance , nuclear medicine , radiology , artificial intelligence , pathology , computer science , anatomy , alternative medicine
This work evaluates the ability of quantitative MRI to discriminate between normal and pathological human osteochondral plugs characterized by the Osteoarthritis Research Society International (OARSI) histological system. Normal and osteoarthritic human osteochondral plugs were scored using the OARSI histological system and imaged at 3 T using MRI sequences producing T1 and T2 contrast and measuring T 1 , T 2 , and T 2 * relaxation times, magnetization transfer, and diffusion. The classification accuracies of quantitative MRI parameters and corresponding weighted image intensities were evaluated. Classification models based on the Mahalanobis distance metric for each MRI measurement were trained and validated using leave‐one‐out cross‐validation with plugs grouped according to OARSI histological grade and score. MRI measurements used for classification were performed using a region‐of‐interest analysis which included superficial, deep, and full‐thickness cartilage. The best classifiers based on OARSI grade and score were T 1 ‐ and T 2 ‐weighted image intensities, which yielded accuracies of 0.68 and 0.75, respectively. Classification accuracies using OARSI score‐based group membership were generally higher when compared with grade‐based group membership. MRI‐based classification—either using quantitative MRI parameters or weighted image intensities—is able to detect early osteoarthritic tissue changes as classified by the OARSI histological system. These findings suggest the benefit of incorporating quantitative MRI acquisitions in a comprehensive clinical evaluation of OA. © 2015 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 33:640–650, 2015.