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Predicting and Visualizing STK11 Mutation in Lung Adenocarcinoma Histopathology Slides Using Deep Learning

View ORCID ProfileRunyu Hong, View ORCID ProfileWenke Liu, View ORCID ProfileDavid Fenyö
doi: https://doi.org/10.1101/2020.02.20.956557
Runyu Hong
1Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA
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  • For correspondence: david@fenyolab.org Runyu.Hong@nyu.edu
Wenke Liu
1Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA
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David Fenyö
1Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA
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  • For correspondence: david@fenyolab.org Runyu.Hong@nyu.edu
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Abstract

Studies have shown that STK11 mutation plays a critical role in affecting the lung adenocarcinoma (LUAD) tumor immune environment. By training an Inception-Resnet-v2 deep convolutional neural network model, we were able to classify STK11-mutated and wild type LUAD tumor histopathology images with a promising accuracy (per slide AUROC=0.795). Dimensional reduction of the activation maps before the output layer of the test set images revealed that fewer immune cells were accumulated around cancer cells in STK11-mutation cases. Our study demonstrated that deep convolutional network model can automatically identify STK11 mutations based on histopathology slides and confirmed that the immune cell density was the main feature used by the model to distinguish STK11-mutated cases.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Runyu.Hong{at}nyu.edu; Wenke.Liu{at}nyulangone.org

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Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 15, 2021.
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Predicting and Visualizing STK11 Mutation in Lung Adenocarcinoma Histopathology Slides Using Deep Learning
Runyu Hong, Wenke Liu, David Fenyö
bioRxiv 2020.02.20.956557; doi: https://doi.org/10.1101/2020.02.20.956557
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Predicting and Visualizing STK11 Mutation in Lung Adenocarcinoma Histopathology Slides Using Deep Learning
Runyu Hong, Wenke Liu, David Fenyö
bioRxiv 2020.02.20.956557; doi: https://doi.org/10.1101/2020.02.20.956557

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