RESUMO
Quantitative measures of rheumatoid arthritis (RA) disease progression can provide valuable tools for evaluation of new treatments during clinical trials. In this study, a novel multispectral (MS) MRI analysis method is presented to quantify changes in bone lesion volume (DeltaBLV) in the hands of RA patients. Image registration and MS analysis were employed to identify MS tissue class transitions between two serial MRI exams. DeltaBLV was determined from MS class transitions between two time points. The following three classifiers were investigated: (a) multivariate Gaussian (MVG), (b) k-nearest neighbor (k-NN), and (c) K-means (KM). Unlike supervised classifiers (MVG, k-NN), KM, an unsupervised classifier, does not require labeled training data, resulting in potentially greater clinical utility. All MS estimates of DeltaBLV were linearly correlated (r(p)) with manual estimates. KM and k-NN estimates also exhibited a significant rank-order correlation (r(s)) with manual estimates. For KM, r(p) = 0.94 p < 0.0001, r(s) = 0.76 p = 0.002; for k-NN, r(p) = 0.86 p = 0.0001, r(s) = 0.69 p = 0.009; and for MVG, r(p) = 0.84 p = 0.0003, r(s) = 0.49 p = 0.09. Temporal classification rates were as follows: for KM, 90.1%; for MVG, 89.5%; and for k-NN, 86.7%. KM matched the performance of k-NN, offering strong potential for use in multicenter clinical trials. This study demonstrates that MS tissue class transitions provide a quantitative measure of DeltaBLV.