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In Journal of pathology informatics ; h5-index 23.0

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.

Xu Lin, Walker Blair, Liang Peir-In, Tong Yi, Xu Cheng, Su Yu Chun, Karsan Aly

2020

Colorectal cancer, deep learning, digital pathology, medical imaging