Multi-field-of-view Deep Learning Model Predicts Non-small Cell Lung Cancer Programmed Death Ligand-1 Status from Whole-slide Hematoxylin and Eosin Images

Multi-field-of-view Deep Learning Model Predicts Non-small Cell Lung Cancer Programmed Death Ligand-1 Status from Whole-slide Hematoxylin and Eosin Images

PUBLICATIONS

July 23, 2019

Journal of Pathology Informatics, Tempus-authored – Background: Tumor programmed death-ligand 1 (PD-L1) status is useful in determining which patients may benefit from programmed death-1 (PD-1)/PD-L1 inhibitors. However, little is known about the association between PD-L1 status and tumor histopathological patterns. Using deep learning, we predicted PD-L1 status from hematoxylin and eosin (H and E) whole-slide images (WSIs) of non-small cell lung cancer (NSCLC) tumor samples.

Materials and Methods: One hundred and thirty NSCLC patients were randomly assigned to training (n = 48) or test (n = 82) cohorts. A pair of H and E and PD-L1-immunostained WSIs was obtained for each patient. A pathologist annotated PD-L1 positive and negative tumor regions on the training samples using immunostained WSIs for reference. From the H and E WSIs, over 145,000 training tiles were generated and used to train a multi-field-of-view deep learning model with a residual neural network backbone.

Results: The trained model accurately predicted tumor PD-L1 status on the held-out test cohort of H and E WSIs, which was balanced for PD-L1 status (area under the receiver operating characteristic curve [AUC] =0.80, P << 0.01). The model remained effective over a range of PD-L1 cutoff thresholds (AUC = 0.67–0.81, P ≤ 0.01) and when different proportions of the labels were randomly shuffled to simulate interpathologist disagreement (AUC = 0.63–0.77, P ≤ 0.03).

Conclusions: A robust deep learning model was developed to predict tumor PD-L1 status from H and E WSIs in NSCLC. These results suggest that PD-L1 expression is correlated with the morphological features of the tumor microenvironment.

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Authors: Lingdao Sha, Bo Osinski, Irvin Ho, Tim Tan, Caleb Willis, Hannah Weiss, Nike Beaubier, Brett Mahon, Tim Taxter, Stephen Yip