Plasma cell cytogenetic abnormalities and labeling index have also shown to be of prognostic value, but, these techniques require fresh unfixed material, are sophisticated and expensive, and can be performed only in few laboratories. Therefore it would be interesting to look for additional prognostic factors, which are not cost-expensive,Vemurafenib can be examined retrospectively and be assessed independently of a specialized laboratory. Cell morphology, evaluated subjectively by a trained observer, has also been considered a prognostic variable in multiple myelomas but the drawback of this method is the considerable inter-observer variability. Goasguen et al. developed a protocol for morphologic analysis of myeloma cells based on more objective morphologic criteria in routinely stained slides. These criteria included the presence of a nucleolus, blast-like chromatin and a nuclear-cytoplasmatic ratio.RWJ 64809 thus creating 8 possible subtypes. This procedure was able to identify an intermediate prognostic subgroup of patients, but the method was still dependent on a trained human observer. Leleu et al described in detail the nuclear shape changes in myeloma cells and created the variable ‘‘percentage of plasma cells with irregular nuclear shape’’. This variable was a prognostic factor in the univariate analysis and significantly associated with other prognostic parameters such as Ki67 labeling index, hemoglobin values and hypodiploidy, but not with beta-2-microglobulin. In both studies, however, morphologic variables were not independent risk factors in multivariate regressions. Computerized analysis of microscopic images overcomes the necessity of morphologic expertise and expert opinion and has shown to be an objective and reproducible method for diagnostic and prognostic purposes. Image analysis is able to detect subtle morphologic changes which cannot be recognized even by a trained observer. Among these techniques, the examination of the fractal characteristics of nuclear chromatin has shown to be of increasing importance. The use of the fractal concept for image analysis has several advantages. The fractal dimension has shown to be robust against the segmentation process.