Quantitative assessment of myositis in thigh muscles using magnetic resonance imaging.

Magnetic resonance (MR) imaging has been suggested as a technique for diagnosing and monitoring myositis, an inflammatory muscle disease. To date, the assessment of disease from MR images has been by subjective visual analysis. We describe here an objective, semi-automatic, computer-based method for quantifying the degree of disease from MR images, without the need for a radiologist or physician trained in the visual assessment of the MR images. The method is based on analysis of the histogram of intensity values produced from the MR images. The analysis yielded measures of the intensity and extent of disease. These two measures were combined to produce a calculated myositis index (CMI) which described the degree of disease evident from the MR images. This index was compared with a clinical assessment of the patient's condition, based on currently accepted, invasive and non-invasive, non-imaging criteria. Receiver operating characteristic (ROC) curve analysis showed that calculated myositis index agreed at least as well with clinical assessment as did visual analysis (receiver operating characteristic area = 0.93 and 0.94, p = not significant (NS), respectively, for separating remission from disease). Even using only two central MR slices for each patient, the receiver operating characteristic area for calculated myositis index was 0.92, implying that very short acquisition times are possible. We conclude that quantitative histogram analysis of MR images can be successfully performed with minimal operator input and using few MR slices. Agreement with more invasive clinical assessment is good and the method has the advantages of repeatability, objectivity, and decreased scan and analysis time.

Familial autoimmunity in pedigrees of idiopathic inflammatory myopathy patients suggests common genetic risk factors for many autoimmune diseases.

OBJECTIVE: To test the hypothesis that many autoimmune diseases share common genetic risk factors and to define the frequency and distribution of autoimmune diseases in relatives of patients with very rare disorders, the idiopathic inflammatory myopathies (IIM). METHODS: We evaluated, in a prospective case-control study, consecutive patients with IIM who were referred to our center and ascertained without regard to family history or known risk factors for autoimmunity, and all available family members. We used a standardized assessment to determine the presence and type of autoimmune disease in each subject. A matched comparison group of control subjects without autoimmune disease who were referred to our center and their families were similarly assessed. RESULTS: Autoimmune diseases were significantly increased in prevalence (21.9%) in the 151 first-degree relatives of the 21 IIM probands compared with the prevalence (4.9%) in the 143 relatives of the 21 control probands (odds ratio [OR] by regression analysis 7.9, 95% confidence interval [95% CI] 2.9-21.9, P < 0.001). Women had more autoimmune disease than men (OR by regression analysis 4.6, 95% CI 2.3-9.0) and the odds ratio for autoimmune disease increased 0.02 per year of age. These disorders tended to follow the frequency distribution of autoimmune diseases in the general population. Genetic modeling studies showed that a non-Mendelian polygenic inheritance pattern for autoimmune disease was most consistent with these data. CONCLUSION: Autoimmune diseases are significantly increased in frequency in first-degree relatives of IIM patients, affect more women than men, increase with age, and are distributed in a pattern similar to that in the general population. Many autoimmune disorders share genes that together act as polygenic risk factors for autoimmunity.