Deep learning based tumor bud detection in pan-cytokeratin stained colorectal cancer whole-slide images

J.-M. Bokhorst, I. Nagtegaal, I. Zlobec, A. Lugli, M. Vieth, R. Kirsch, J. van der Laak and F. Ciompi

in: European Congress of Pathology, 2020


Background & objectives Tumor budding (TB) is an established prognosticator for colorectal cancer. Deep learning based TB assessment has the potential to improve diagnostic reproducibility and efficiency. We developed an algorithm that can detect individual tumor buds in pan-cytokeratin stained colorectal cancer slides Methods Tumor-bud candidates (n=1765, collected from 58 whole slide images; WSI) were labeled by seven experts as either TB, poorly differentiated cluster, or neither. The 58 slides were randomly split into a training (49) and test-set (9). A deep learning (DL) model was trained using the buds identified by the experts in the training set. Results The algorithm was tested on the nine remaining WSI and 270 WSI from pan-cytokeratin stained slides from Bern University hospital, in which hot spots and TB were manually scored. An F1 score of 0.82 was found for correspondence at the bud level between experts and DL. A correlation of 0.745 was found between the manually counted buds within the hotspots and the automated method in the 270 WSIs. Conclusion Assessment of tumor budding as a prognostic factor for colorectal cancer can be automated using deep learning. At the level of individual tumor buds, correspondence between DL and experts is high and comparable to the inter-rater variability. However, compared to the manual procedure, the algorithm yields higher counts for cases with relatively high bud densities (>15). Follow-up studies will focus on the assessment of TB in H&E stained slides.