Classification of histologically scored human knee osteochondral plugs by quantitative analysis of magnetic resonance images at 3T

Abstract

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 T1, T2, and T2* 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 T1- and T2-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.

Machine learning classification of OARSI-scored human articular cartilage using magnetic resonance imaging

“…This compares favorably with other analyses 9 , which also specifically addressed discrimination between these histologically-graded ostechondral plugs from human subjects with idiopathic non-advanced OA. The present approach relies upon the analysis of a large set of image features and provides compelling evidence that feature analysis yields information that may serve as a surrogate for histology.…”

“…Here, we extend the previous work of Lukas et al on univariate classification using individual MR parameters 9 by examining the ability of WND-CHRM to assign osteochondral plugs to either a normal or OA group based on analysis of MR images. Each plug was separately classified based on OARSI histological score 20 to provide the comparison gold standard with WND-CHRM.…”

“…Sample preparation for the present study has been previously described 9 . Briefly, human tissue was obtained from knee joints in an Institutional Review Board approved protocol from subjects undergoing elective arthroplasty.…”

“…The MRI protocol for the present study has been previously described 9 . Briefly, samples were inserted into a susceptibility-matched polyetherimide (ULTEM) sample holder containing Fluorinert® FC-77 (Sigma-Aldrich, St. Louis, MO) and heated to 37.0 ± 0.1 °C prior to data acquisition.…”

“…Therefore, a difference in group mean values of a given parameter between individuals with differing OA status does not necessarily indicate clinical utility of this parameter; classification according to even a statistically significantly varying parameter may result in a clinical test of limited sensitivity (SE) and specificity (SP) 8 . Examples of this were seen in the work of Lukas et al , who performed univariate classification of Osteoarthritis Research Society International (OARSI) graded human osteochondral plugs, using the Mahalanobis distance metric 9 . While statistically significant differences in the T 1 -weighted ( T 1 W) images and magnetization transfer ratios were seen with respect to degradation status, the corresponding classification accuracies were only 0.60 and 0.55.…”