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.,