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Predicting Endometrial Cancer Subtypes and Molecular Features from Histopathology Images Using Multi-resolution Deep Learning Models

View ORCID ProfileRunyu Hong, View ORCID ProfileWenke Liu, View ORCID ProfileDeborah DeLair, View ORCID ProfileNarges Razavian, View ORCID ProfileDavid Fenyö
doi: https://doi.org/10.1101/2020.02.25.965038
Runyu Hong
1Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA
2Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
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Wenke Liu
1Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA
2Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
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Deborah DeLair
3Department of Pathology, NYU Langone Health, New York, NY 10016, USA
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Narges Razavian
4Department of Population Health, NYU Langone Health, 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
2Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
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  • For correspondence: David.Fenyo@nyulangone.org
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Summary

The determination of endometrial carcinoma histological subtypes, molecular subtypes, and mutation status is a critical diagnostic process that directly affects patients’ prognosis and treatment options. Compared to the histopathological approach, however, the availability of molecular subtyping is limited as it can only be accurately obtained by genomic sequencing, which may be cost prohibitive. Here, we implemented a customized multi-resolution deep convolutional neural network, Panoptes, that predicts not only the histological subtypes, but also molecular subtypes and 18 common gene mutations based on digitized H&E stained pathological images. The model achieved high accuracy and generalized well on independent datasets. Our results suggest that Panoptes has potential clinical application of helping pathologists determine molecular subtypes and mutations of endometrial carcinoma without sequencing.

Significance Recently, molecular subtyping and mutation status are increasingly utilized in clinical practice as they offer better-informed prognosis and the possibility of individualized therapies for endometrial carcinoma patients. Taking advantage of the multi-resolution nature of the whole slide digital histopathology images, our Panoptes models integrate features of different magnification and make accurate predictions of histological subtypes, molecular subtypes, and key mutations in much faster workflows compared to conventional sequencing-based analyses. Feature extraction and visualization revealed that the model relied on human-interpretable patterns. Overall, our multi-resolution deep learning model is capable of assisting pathologists determine molecular subtypes of endometrial carcinoma, which can potentially accelerate diagnosis process.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • External validation. POLE.

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 May 10, 2021.
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Predicting Endometrial Cancer Subtypes and Molecular Features from Histopathology Images Using Multi-resolution Deep Learning Models
Runyu Hong, Wenke Liu, Deborah DeLair, Narges Razavian, David Fenyö
bioRxiv 2020.02.25.965038; doi: https://doi.org/10.1101/2020.02.25.965038
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Predicting Endometrial Cancer Subtypes and Molecular Features from Histopathology Images Using Multi-resolution Deep Learning Models
Runyu Hong, Wenke Liu, Deborah DeLair, Narges Razavian, David Fenyö
bioRxiv 2020.02.25.965038; doi: https://doi.org/10.1101/2020.02.25.965038

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