  Classification of histologic patterns in lung adenocarcinoma is critical for
determining tumor grade and treatment for patients. However, this task is often
challenging due to the heterogeneous nature of lung adenocarcinoma and the
subjective criteria for evaluation. In this study, we propose a deep learning
model that automatically classifies the histologic patterns of lung
adenocarcinoma on surgical resection slides. Our model uses a convolutional
neural network to identify regions of neoplastic cells, then aggregates those
classifications to infer predominant and minor histologic patterns for any
given whole-slide image. We evaluated our model on an independent set of 143
whole-slide images. It achieved a kappa score of 0.525 and an agreement of
66.6% with three pathologists for classifying the predominant patterns,
slightly higher than the inter-pathologist kappa score of 0.485 and agreement
of 62.7% on this test set. All evaluation metrics for our model and the three
pathologists were within 95% confidence intervals of agreement. If confirmed in
clinical practice, our model can assist pathologists in improving
classification of lung adenocarcinoma patterns by automatically pre-screening
and highlighting cancerous regions prior to review. Our approach can be
generalized to any whole-slide image classification task, and code is made
publicly available at https://github.com/BMIRDS/deepslide.
