Colorectal Cancer Detection Based on Deep Learning

Xu L, Walker B, Liang PI, Tong Y, Xu C, Su YC, Karsan A. Colorectal cancer detection based on deep learning. J Pathol Inform [serial online] 2020 [cited 2020 Dec 8];11:28. Available from: https://www.jpathinformatics.org/text.asp?2020/11/1/28/292721 Abstract: Introduction: The initial point in the diagnostic workup of solid tumors remains manual, with the assessment of hematoxylin and eosin (H&E)-stained tissue sections by microscopy. This is a labor-intensive step that requires attention to detail. In addition, diagnoses are influenced by an individual pathologist's knowledge and experience and may not always be reproducible between pathologists. Methods: We introduce a deep learning-based method in colorectal cancer detection and segmentation from digitized H&E-stained histology slides. Results: In this study, we demonstrate that this neural network approach produces median accuracy of 99.9% for normal slides and 94.8% for cancer slides compared to pathologist-based diagnosis on H&E-stained slides digitized from clinical samples. Conclusion: Given that our approach has very high accuracy on normal slides, use of neural network algorithms may provide a screening approach to save pathologist time in identifying tumor regions. We suggest that this new method may be a powerful assistant for colorectal cancer diagnostics.