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

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

doi:10.1007/s00428-020-02987-2

Abstract

Tumor 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.

Highlights

Pancreatic adenocarcinoma (PDAC) is a major cause of cancer-associated mortality in Western countries and it is expected to emerge as the second leading cause of cancer-related death by 2030 [1]
Tumor budding was scored by independent observers at five participating centers in Switzerland, Germany, and Canada, on Hematoxylin and Eosin (H&E)-stained slides and matched pancytokeratin (AE1/ AE3)-stained slides of 50 pancreatic ductal adenocarcinoma (PDAC) cases
All these factors are regularly included in the histopathology reports except tumor budding, which is still missing in most reports of resection specimens of treatment-naïve PDACs

Summary

Introduction

Pancreatic adenocarcinoma (PDAC) is a major cause of cancer-associated mortality in Western countries and it is expected to emerge as the second leading cause of cancer-related death by 2030 [1]. Recent advances in the multimodal management of patients with PDAC have improved the 5-year overall survival rates up to 20–40% following oncologic resection for PDAC [4, 5]. Despite these significant advances in the treatment of PDAC, tumor recurrence following radical resection remains high limiting long-term survival [6]. PDAC is a highly heterogeneous disease and even patients with the same TNM stage have different outcomes [7]. The identification of biomarkers that would enable a more accurate prediction of the tumor biology of Virchows Arch (2021) 478:719–726

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Results

Conclusion