Quantitative Image Analysis in the Assessment of Diffuse Large B-cell Lymphoma

Devon S Chabot-Richards; David R Martin; Orrin B Myers; David R Czuchlewski; Kristin E Hunt

Disclosures

Mod Pathol. 2011;24(12):1598-1605. 

In This Article

Abstract and Introduction

Abstract

Proliferation rates in diffuse large B-cell lymphoma have been associated with conflicting outcomes in the literature, more often with high proliferation associated with poor prognosis. In most studies, the proliferation rate was estimated by a pathologist using an immunohistochemical stain for the monoclonal antibody Ki-67. We hypothesized that a quantitative image analysis algorithm would give a more accurate estimate of the proliferation rate, leading to better associations with survival. In all, 84 cases of diffuse large B-cell lymphoma were selected according to the World Health Organization criteria. Ki-67 percentage positivity estimated by the pathologist was recorded from the original report. The same slides were then scanned using an Aperio ImageScope, and Ki-67 percentage positivity was calculated using a computer-based quantitative immunohistochemistry nuclear algorithm. In addition, chart review was performed and survival time was recorded. The Ki-67 percentage estimated by the pathologist from the original report versus quantitative image analysis was significantly correlated (P<0.001), but pathologist Ki-67 percentages were significantly higher than quantitative image analysis (P=0.021). There was less agreement at lower Ki-67 percentages. Comparison of Ki-67 percentage positivity versus survival did not show significant association either with pathologist estimate or quantitative image analysis. However, although not significant, there was a trend of worse survival at higher proliferation rates detected by the pathologist but not by quantitative image analysis. Interestingly, our data suggest that the Ki-67 percentage positivity as assessed by the pathologist may be more closely associated with survival outcome than that identified by quantitative image analysis. This may indicate that pathologists are better at selecting appropriate areas of the slide. More cases are needed to assess whether this finding would be statistically significant. Due to the good correlation between pathologist estimate and quantitative image analysis, there is no substantial benefit to using quantitative image analysis at this point of time.

Introduction

Diffuse large B-cell lymphoma is the most frequent type of non-Hodgkin lymphoma in adults in the United States.[1] Due to its heterogeneity in appearance and clinical behavior, numerous studies have attempted to further subclassify this entity into meaningfully distinct groups. Although gene expression profiling and surrogate immunohistochemical studies have elicited significant differences in pathogenesis and overall survival,[2–9] with groups such as those of germinal center B-cell origin and activated B-cell types identified, the majority of these studies took place before the routine addition of rituximab to the chemotherapeutic regimen. The relatively recent use of chemotherapy regimens incorporating rituximab (R-CHOP) has shown in some studies an eradication of these differences in prognostic markers,[10] although some recent studies have suggested no significant difference between CHOP and R-CHOP groups.[11] Although many practicing pathologists have avoided the routine classification of diffuse large B-cell lymphoma into germinal center B-cell and activated B-cell phenotypes, for reasons including those mentioned above, most pathologists still continue to report the proliferation rate of the tumor. The Ki-67 monoclonal antibody is commonly used to measure the proliferation index, and a higher proliferation rate is commonly associated with worse prognosis. Many of the studies, however, that report an association between high proliferation rate and poor prognosis[12–14] were performed before the routine use of rituximab in chemotherapy regimens. Examination of the medical literature shows some conflicting data with some studies reporting no effect of proliferation rates on prognosis,[15,16] and other studies even showing correlation between low proliferation index and poor outcome.[17] Despite the variable data, the most recent World Health Organization criteria on diffuse large B-cell lymphoma does list high proliferation index as a likely indicator of poorer prognosis.[1]

Traditionally in the clinical setting, pathologists have estimated this proliferation rate using a Ki-67 immunohistochemical stain, selecting areas of tumor involvement and estimating the percentage of tumor cells with positive nuclei. The presence of nuclear staining with Ki-67 indicates an actively cycling cell while quiescent cells should be negative.[18] Although some research studies have used more complex and time-consuming methods involving counting cells in grids, in practical application one would generally study multiple areas of involvement and give a rough estimate without actually counting all of the cells. There is some debate as to which method is best and whether a semi-quantitative estimation is sufficient. No specific cutoff has been established at which prognosis changes, but several studies have used cutoffs in the range of 60–80% Ki-67 positivity to achieve statistical significance.[12,14,19]

In recent years, virtual microscopy and quantitative image analysis have become viable options in the clinical laboratory and have been used both in clinical and research applications, including areas of high-volume study such as routine gynecological cytology, where their cost savings is more obvious.[20–24] In areas of pathology such as the microscopical evaluation of lymph nodes for malignancy, quantitative image analysis is not widely used and less literature exists as to its potential benefits. In the study of large cell lymphomas, proliferation indices are frequently evaluated to assess for potentially aggressive tumors, although as stated above, it is typical for only a semiquantitative estimate to be given in clinical practice.

In this study, we sought to address whether the use of computer-driven quantitative image analysis would add clinical value. We hypothesized that a quantitative image analysis system may be able to provide a more accurate proliferation index than that obtained by routine pathologist estimation. As previous reports in the literature show conflicting conclusions as to the prognostic significance of the proliferation rate, we further hypothesized that quantitative image analysis of the proliferation index would reveal a stronger correlation with prognosis, with higher proliferation conferring a worse prognosis. Lastly, we suspected that the number of background T-cells might confound the computer's ability to assess the Ki-67 percentage and to identify which cells were tumor and which were background T-cells. Thus, we also became interested in quantifying the number of background T-cells, as we thought an algorithm might be necessary to 'subtract' the non-tumor T-cells from the computer-generated Ki-67 proliferation index, thereby providing the 'true' proliferation index of the tumor. The ultimate question that this study addresses is whether the current pathologist assessment of proliferation rate is adequate or whether a computer-driven quantitative analysis would add prognostic value, and should be implemented in routine clinical laboratory evaluation of diffuse large B-cell lymphoma.

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