| RESEARCH ARTICLE |
|
| Year : 2019 | Volume
: 10
| Issue : 1 | Page : 24 |
|
|
Multi-field-of-view deep learning model predicts nonsmall cell lung cancer programmed death-ligand 1 status from whole-slide hematoxylin and eosin images
Lingdao Sha1, Boleslaw L Osinski1, Irvin Y Ho1, Timothy L Tan2, Caleb Willis1, Hannah Weiss3, Nike Beaubier1, Brett M Mahon1, Tim J Taxter1, Stephen S F Yip1
1 Tempus Labs, Inc, Chicago, IL, USA
2 Tempus Labs, Inc; Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
3 Tempus Labs, Inc; Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
Correspondence Address:
Dr. Stephen S F Yip
Tempus Labs, Inc., 600 West Chicago Ave. Ste 510, Chicago, IL 60608
USA
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/jpi.jpi_24_19
|
|
|
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.
|
|
|
|
| [FULL TEXT] [PDF]* |
|
 |
|
