  Histological classification of colorectal polyps plays a critical role in
both screening for colorectal cancer and care of affected patients. An accurate
and automated algorithm for the classification of colorectal polyps on
digitized histopathology slides could benefit clinicians and patients. Evaluate
the performance and assess the generalizability of a deep neural network for
colorectal polyp classification on histopathology slide images using a
multi-institutional dataset. In this study, we developed a deep neural network
for classification of four major colorectal polyp types, tubular adenoma,
tubulovillous/villous adenoma, hyperplastic polyp, and sessile serrated
adenoma, based on digitized histopathology slides from our institution,
Dartmouth-Hitchcock Medical Center (DHMC), in New Hampshire. We evaluated the
deep neural network on an internal dataset of 157 histopathology slide images
from DHMC, as well as on an external dataset of 238 histopathology slide images
from 24 different institutions spanning 13 states in the United States. We
measured accuracy, sensitivity, and specificity of our model in this evaluation
and compared its performance to local pathologists' diagnoses at the
point-of-care retrieved from corresponding pathology laboratories. For the
internal evaluation, the deep neural network had a mean accuracy of 93.5% (95%
CI 89.6%-97.4%), compared with local pathologists' accuracy of 91.4% (95% CI
87.0%-95.8%). On the external test set, the deep neural network achieved an
accuracy of 87.0% (95% CI 82.7%-91.3%), comparable with local pathologists'
accuracy of 86.6% (95% CI 82.3%-90.9%). If confirmed in clinical settings, our
model could assist pathologists by improving the diagnostic efficiency,
reproducibility, and accuracy of colorectal cancer screenings.
