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Cited by 131AI in cancer detectionRadiomics and Machine Learning in Medical ImagingCancer Immunotherapy and Biomarkers

The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.

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

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Sha, Lingdao, Osinski, Boleslaw L., Ho, Irvin Y., Tan, Timothy L., Willis, Caleb, Weiss, Hannah, Beaubier, Nike, Mahon, Brett M., Taxter, Tim J., Yip, Stephen S. F·2019·Journal of Pathology Informatics
DOI 10.4103/jpi.jpi_24_19PMC PMC6669997
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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.

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Figure 1 (a) Network architecture: Our deep learning framework consists of a fully convolutional ResNet-18 that processes a large field of view, along with two additional branches that process small field of views. The ResNet-18 backbone contains multiple shortcut connections. The dotted lines indicate shortcut connections where feature maps are also downsampled by 2. The small field-of-view branches emerge after the second convolutional block. The feature maps of the small field-of-view branches are downsampled by 8 to match the dimensions of the ResNet-18 feature map. These feature maps are concatenated before passing through a softmax output to produce a programmed death-ligand 1 staining probability map. (b) Model training: matching areas on Immunohistochemistry and H and E slides were annotated. The annotated regions of the H and E image were tiled into overlapping tiles (466 × 466 pixels) with a stride of 32 pixels, producing our training data. The multi-field-of-view ResNet-18 model was then trained using a cross-entropy loss function. The yellow square in the model schematic depicts the central region that is cropped for the small field of views. (c) Model inference: each image was divided into large nonoverlapping 4096 × 4096 input windows (blue dashed lines). Each large window was passed through the trained model. Because the model is fully convolutional, each tile within the large input window was processed in parallel, producing a 128 × 128 × 3 probability cube (the last dimension represents three classes). The resulting probability cubes were slotted into place and assembled to generate a probability map of the whole image. The class with the maximum probability was assigned to each tile
Figure 1 (a) Network architecture: Our deep learning framework consists of a fully convolutional ResNet-18 that processes a large field of view, along with two additional branches that process small field of views. The ResNet-18 backbone contains multiple shortcut connections. The dotted lines indicate shortcut connections where feature maps are also downsampled by 2. The small field-of-view branches emerge after the second convolutional block. The feature maps of the small field-of-view branches are downsampled by 8 to match the dimensions of the ResNet-18 feature map. These feature maps are concatenated before passing through a softmax output to produce a programmed death-ligand 1 staining probability map. (b) Model training: matching areas on Immunohistochemistry and H and E slides were annotated. The annotated regions of the H and E image were tiled into overlapping tiles (466 × 466 pixels) with a stride of 32 pixels, producing our training data. The multi-field-of-view ResNet-18 model was then trained using a cross-entropy loss function. The yellow square in the model schematic depicts the central region that is cropped for the small field of views. (c) Model inference: each image was divided into large nonoverlapping 4096 × 4096 input windows (blue dashed lines). Each large window was passed through the trained model. Because the model is fully convolutional, each tile within the large input window was processed in parallel, producing a 128 × 128 × 3 probability cube (the last dimension represents three classes). The resulting probability cubes were slotted into place and assembled to generate a probability map of the whole image. The class with the maximum probability was assigned to each tileCC BY-NC-SA · PubMed Central
Figure 2 Top row: representative positive case (a) H and E whole-slide image, (b) probability map overlaid on H and E, and (c) programmed death-ligand 1 immunohistochemistry stain. Bottom row: representative negative case (d) H and E whole-slide image, (e) probability map overlaid on H and E, and (f) programmed death-ligand 1 immunohistochemistry stain. The color bar indicates the predicted probability of the tumor programmed death-ligand 1 + class. The outline marked in A and B is a laboratory remnant and unrelated to the model
Figure 2 Top row: representative positive case (a) H and E whole-slide image, (b) probability map overlaid on H and E, and (c) programmed death-ligand 1 immunohistochemistry stain. Bottom row: representative negative case (d) H and E whole-slide image, (e) probability map overlaid on H and E, and (f) programmed death-ligand 1 immunohistochemistry stain. The color bar indicates the predicted probability of the tumor programmed death-ligand 1 + class. The outline marked in A and B is a laboratory remnant and unrelated to the modelCC BY-NC-SA · PubMed Central
Figure 3 Test cohort results. Top row: Box plots depicting how tumor programmed death-ligand 1 statuses are separated by deep learning model score in (a) all nonsmall cell lung cancer, (b) lung adenocarcinoma, (c) lung squamous cell carcinoma. Bottom row: Receiver operating characteristic curve for (d) all nonsmall cell lung cancer, (e) adenocarcinoma, and (f) squamous cell carcinoma. The horizontal line indicates median
Figure 3 Test cohort results. Top row: Box plots depicting how tumor programmed death-ligand 1 statuses are separated by deep learning model score in (a) all nonsmall cell lung cancer, (b) lung adenocarcinoma, (c) lung squamous cell carcinoma. Bottom row: Receiver operating characteristic curve for (d) all nonsmall cell lung cancer, (e) adenocarcinoma, and (f) squamous cell carcinoma. The horizontal line indicates medianCC BY-NC-SA · PubMed Central
Figure 4 (a) Area under the receiver operating characteristic curve as a function of the programmed death-ligand 1 positivity cutoffs (b) area under the receiver operating characteristic curve as a function of the percentage of shuffled programmed death-ligand 1 labels
Figure 4 (a) Area under the receiver operating characteristic curve as a function of the programmed death-ligand 1 positivity cutoffs (b) area under the receiver operating characteristic curve as a function of the percentage of shuffled programmed death-ligand 1 labelsCC BY-NC-SA · PubMed Central

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Research works printed but not linked: 1

  1. Schaumberg, 2018, H and E-stained Whole Slide Image Deep Learning Predicts SPOP Mutation State in Prostate Cancer, bioRxiv, 064279

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Metadata

Authors
Sha, L., Osinski, B. L., Ho, I. Y., Tan, T. L., Willis, C., Weiss, H., Beaubier, N., Mahon, B. M., Taxter, T. J., & Yip, S. S. F.
Affiliations
  • Tempus Labs (United States) (US)
  • Northwestern University (US)
Corresponding author
Yip, Stephen S. F
ORCID
Venue
Journal of Pathology Informatics
Publisher
Elsevier BV
Published
2019-01
Volume
10
Issue
1
Pages
24
ISSN
2153-3539 (ISSN-L)
DOI
10.4103/jpi.jpi_24_19
PMC
PMC6669997
OpenAlex
W2963652908
Cited by
130
References
48
Citations by year
2026: 9 · 2025: 21 · 2024: 25 · 2023: 13 · 2022: 27 · 2021: 16 · 2020: 18 · 2019: 1
FWCI
7.30
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Pathology Informatics (publishedVersion, cc-by-nc-sa)
  • PubMed Central (publishedVersion, cc-by-nc-sa)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
Keywords
non-small cell lung cancer, PD-L1 expression, whole slide images, H&E staining, deep learning, ResNet, tumor microenvironment, immunohistochemistry, PD-1/PD-L1 inhibitors, histopathological features

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