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J Pathol Inform. 2019 Jul 23;10:24. doi: 10.4103/jpi.jpi_24_19. eCollection 2019.

Multi-Field-of-View Deep Learning Model Predicts Nonsmall Cell Lung Cancer Programmed Death-Ligand 1 Status from Whole-Slide Hematoxylin and Eosin Images.

Author information

Tempus Labs, Inc, Chicago, IL USA.
Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.



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 nonsmall 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.


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).


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.


Artificial intelligence; deep learning; digital pathology; lung cancer

Conflict of interest statement

L.S., B.L.O., I.Y.H., C.W., N.B., B.M.M., T.J.T., and S.S.F.Y. are employees and/or shareholders of Tempus Labs. H.W. is an intern at Tempus Labs. T.L.T. was compensated by Tempus Labs for his participation as a pathologist.

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