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

Cell Maps Representation For Lung Adenocarcinoma Growth Patterns Classification In Whole Slide Images