Reproducibility of tumor budding assessment in pancreatic cancer based on a multicenter interobserver study

Author:

Karamitopoulou EvaORCID,Esposito Irene,Zlobec Inti,Insilla Andrea Cacciato,Wartenberg Martin,Schaeffer David F.,Kalloger Steve,La Rosa Stefano,Sempoux Christine,Ramos Centeno Irene,Lohneis Philipp

Abstract

AbstractTumor budding has been reported to be an independent prognostic factor in pancreatic ductal adenocarcinoma (PDAC). Its use in daily diagnostics would improve the prognostic stratification of patients. We performed a multicenter interobserver study to test various budding assessment methods for their reproducibility. Two serial sections of 50 resected, treatment-naïve PDACs were stained for Hematoxylin and Eosin (H&E) and pancytokeratin. Tumor budding was scored by independent observers at five participating centers in Switzerland, Germany, and Canada. Pathologists assessed tumor budding on a digital platform comparing H&E with pancytokeratin staining in 10 high-power fields (10HPF) and one HPF hotspot (1HPF). Additionally, tumor budding was assessed in one H&E hotspot at × 20 magnification, as suggested by the International Tumor Budding Consensus Conference (ITBCC). Correlation coefficients for bud counts between centers ranged from r = 0.58648 to r = 0.78641 for H&E and from r = 0.69288 to r = 0.81764 for pancytokeratin. The highest interobserver agreement across all centers was observed for pancytokeratin 10HPFs (ICC = 0.6). ICC values were 0.49, 0.48, 0.41, and 0.4 for H&E in 1HPF hotspot, H&E in 10HPFs, pancytokeratin in 1HPF, and H&E in one hotspot at ×20, respectively (ITBCC method). This interobserver study reveals a range between moderately poor to moderate agreement levels between pathologists for the different tumor budding assessment methods in PDAC. Acceptable levels of agreement were reached with the pancytokeratin 10HPF method, which can thus be recommended for the assessment of tumor budding in PDAC resection specimens. To improve the levels of interobserver agreement, the implementation of machine learning applications should be considered.

Funder

University of Bern

Publisher

Springer Science and Business Media LLC

Subject

Cell Biology,Molecular Biology,General Medicine,Pathology and Forensic Medicine

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