  Lung adenocarcinoma is a morphologically heterogeneous disease, characterized
by five primary histologic growth patterns. The quantity of these patterns can
be related to tumor behavior and has a significant impact on patient prognosis.
In this work, we propose a novel machine learning pipeline capable of
classifying tissue tiles into one of the five patterns or as non-tumor, with an
Area Under the Receiver Operating Characteristic Curve (AUCROC) score of 0.97.
Our model's strength lies in its comprehensive consideration of cellular
spatial patterns, where it first generates cell maps from Hematoxylin and Eosin
(H&E) whole slide images (WSIs), which are then fed into a convolutional neural
network classification model. Exploiting these cell maps provides the model
with robust generalizability to new data, achieving approximately 30% higher
accuracy on unseen test-sets compared to current state of the art approaches.
The insights derived from our model can be used to predict prognosis, enhancing
patient outcomes.
