Validation of computer-assisted tumour-bud and T-cell detection in pT1 colorectal cancer

L. Studer, J. Bokhorst, I. Zlobec, A. Lugli, A. Fischer, F. Ciompi, J. van der Laak, I. Nagtegaal and H. Dawson

European Congress of pathology 2020.

Background & objectives: Tumour budding, and T-cells are robust prognostic biomarkers in colorectal cancer. A combined analysis is complex and can be greatly expedited and automated using deep learning. The implementation of computer-based analysis in diagnostics is challenging and necessitates extensive validation.

Methods: Randomly selected (n=61) double-stained immunohistochemical slides (AE1-AE3 pancytokeratin for tumour buds and CD8 for cytotoxic T-cells) from our pT1 cohort from 3 different institutions were used to validate the deep learning algorithms for tumour budding and CD8 T-cell detection developed by the International Budding Consortium Computational Pathology Group. Staining and scanning were performed in a single laboratory.

Results: In the visually identified tumour budding hotspot (0.785 mm2), tumour buds were manually annotated, and the output of the T-cell algorithm manually corrected by a single observer. For budding, 645 out of the 1'306 buds were correctly identified by the algorithm. Recall and precision were 49.4% and 61.4%, respectively. For the T-cells, 89.3% were correctly detected (from a total of 16'296). The recall was 90.3% and the precision was 87.3%. Reasons for misclassified T-cells included staining intensity, suboptimal tissue recognition and slide artifacts.

Conclusion: Our preliminary data demonstrates satisfactory results for T-cell detection. Automated budding detection is more difficult, as inter-observer variability of bud calling is high among experts. These issues merit consideration when developing reliable deep learning algorithms examining the tumour/host interface.