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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

1
Tempus Labs, Inc, Chicago, IL USA.
2
Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
3
Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Abstract

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 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.

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.

KEYWORDS:

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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