Assessment of tumor buds in colorectal cancer. A large-scale international digital observer study
J. Bokhorst, H. Dawson, A. Blank, I. Zlobec, A. Lugli, M. Vieth, R. Kirsch, M. Urbanowicz, S. Brockmoeller, J. Flejou, L. Rijstenberg, J. van der Laak, F. Ciompi and I. Nagtegaal
European Congress of Pathology (2019)
Tumor budding (TB) is a promising and cost-effective biomarker with strong prognostic value in colorectal cancer. We previously found moderate agreement between two pathologists scoring TB using the International Tumor Budding Consensus Conference (ITBCC) guidelines, while considerable discrepancy in identifying individual tumor buds was observed. To explore this issue further, we performed a large-scale international digital observer study on the assessment of individual tumor buds. We extracted 3000 tumor bud candidates by application of digital image analysis algorithms. For every candidate, an image patch (size 256x256um) was extracted from pan-cytokeratinstained whole-slide images of 36 patients with reported TB. Members of a tumor budding consortium were invited to categorize each individual object as (1) tumor bud, (2) poorly differentiated cluster, or (3) none of the previous, based on best practice and current definitions. Agreement was assessed with Cohen’s and Fleiss Kappa. Cohen’s and Fleiss Kappa showed a fair to moderate overall agreement between observers (range 0.24-0.65 and 0.37 respectively) when asked to score 3000 individual objects. Despite adequate agreement between observers in the assessment of TB on patient level, the agreement on individual tumor bud level using immunohistochemistry is only fair. To better understand the causes of this disagreement, more research is needed involving H&E stained images. A machine learning approach may prove especially useful for a more robust assessment of individual tumor buds.