Deep Domain Adversarial Learning for Species-Agnostic Classification of Histologic Subtypes of Osteosarcoma.

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Abstract

Osteosarcomas (OSs) are aggressive bone tumors with many divergent histologic patterns. During pathology review, OSs are subtyped based on the predominant histologic pattern; however, tumors often demonstrate multiple patterns. This high tumor heterogeneity coupled with scarcity of samples compared with other tumor types render histology-based prognosis of OSs challenging. To combat lower case numbers in humans, dogs with spontaneous OSs have been suggested as a model species. Herein, we adversarially train a convolutional neural network to classify distinct histologic patterns of OS in humans using mostly canine OS data during training. We show that adversarial training improves domain adaption of a histologic subtype classifier from canines to humans, achieving an average multiclass F1 score of 0.77 (95% CI, 0.74-0.79) and 0.80 (95% CI, 0.78-0.81) when compared with the ground truth in canines and humans, respectively. Finally, we applied our trained model to characterize the histologic landscape of 306 canine OSs and uncovered distinct clusters with markedly different clinical responses to standard-of-care therapy.Copyright © 2022 American Society for Investigative Pathology. All rights reserved.