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A Deep Learning Approach for Rapid Mutational Screening in Melanoma

Randie H. Kim, Sofia Nomikou, Zarmeena Dawood, George Jour, Douglas Donnelly, Una Moran, Jeffrey S. Weber, Narges Razavian, View ORCID ProfileMatija Snuderl, Richard Shapiro, Russell S. Berman, Nicolas Coudray, Iman Osman, View ORCID ProfileAristotelis Tsirigos
doi: https://doi.org/10.1101/610311
Randie H. Kim
1The Ronald O. Perelman Department of Dermatology, New York University School of Medicine, New York, New York
2Interdisciplinary Melanoma Cooperative Group, New York University School of Medicine, New York, New York
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Sofia Nomikou
3Applied Bioinformatics Laboratories, New York University School of Medicine, New York, New York
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Zarmeena Dawood
2Interdisciplinary Melanoma Cooperative Group, New York University School of Medicine, New York, New York
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George Jour
1The Ronald O. Perelman Department of Dermatology, New York University School of Medicine, New York, New York
2Interdisciplinary Melanoma Cooperative Group, New York University School of Medicine, New York, New York
4Department of Pathology, New York University School of Medicine, New York, New York
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Douglas Donnelly
2Interdisciplinary Melanoma Cooperative Group, New York University School of Medicine, New York, New York
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Una Moran
2Interdisciplinary Melanoma Cooperative Group, New York University School of Medicine, New York, New York
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Jeffrey S. Weber
2Interdisciplinary Melanoma Cooperative Group, New York University School of Medicine, New York, New York
5Department of Medicine, New York University School of Medicine, New York, New York
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Narges Razavian
6Deparmtent of Radiology, New York University School of Medicine, New York, New York
7Department of Population Health, New York University School of Medicine, New York, New York
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Matija Snuderl
4Department of Pathology, New York University School of Medicine, New York, New York
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  • ORCID record for Matija Snuderl
Richard Shapiro
2Interdisciplinary Melanoma Cooperative Group, New York University School of Medicine, New York, New York
8Department of Surgery, New York University School of Medicine, New York, New York
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Russell S. Berman
2Interdisciplinary Melanoma Cooperative Group, New York University School of Medicine, New York, New York
8Department of Surgery, New York University School of Medicine, New York, New York
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Nicolas Coudray
3Applied Bioinformatics Laboratories, New York University School of Medicine, New York, New York
9Skirball Institute Department of Cell Biology, New York University School of Medicine, New York, New York
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Iman Osman
1The Ronald O. Perelman Department of Dermatology, New York University School of Medicine, New York, New York
2Interdisciplinary Melanoma Cooperative Group, New York University School of Medicine, New York, New York
5Department of Medicine, New York University School of Medicine, New York, New York
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  • For correspondence: Aristotelis.Tsirigos@nyulangone.org Iman.Osman@nyulangone.org
Aristotelis Tsirigos
2Interdisciplinary Melanoma Cooperative Group, New York University School of Medicine, New York, New York
3Applied Bioinformatics Laboratories, New York University School of Medicine, New York, New York
4Department of Pathology, New York University School of Medicine, New York, New York
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  • ORCID record for Aristotelis Tsirigos
  • For correspondence: Aristotelis.Tsirigos@nyulangone.org Iman.Osman@nyulangone.org
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Abstract

DNA-based molecular assays for determining mutational status in melanomas are time-consuming and costly. As an alternative, we applied a deep convolutional neural network (CNN) to histopathology images of tumors from 257 melanoma patients and developed a fully automated model that first selects for tumor-rich areas (Area under the curve AUC=0.98), and second, predicts for the presence of mutated BRAF or NRAS. Network performance was enhanced on BRAF-mutated melanomas ≤1.0 mm (AUC=0.83) and on non-ulcerated NRAS-mutated melanomas (AUC=0.92). Applying our models to histological images of primary melanomas from The Cancer Genome Atlas database also demonstrated improved performances on thinner BRAF-mutated melanomas and non-ulcerated NRAS-mutated melanomas. We propose that deep learning-based analysis of histological images has the potential to become integrated into clinical decision making for the rapid detection of mutations of interest in melanoma.

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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 April 16, 2019.
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A Deep Learning Approach for Rapid Mutational Screening in Melanoma
Randie H. Kim, Sofia Nomikou, Zarmeena Dawood, George Jour, Douglas Donnelly, Una Moran, Jeffrey S. Weber, Narges Razavian, Matija Snuderl, Richard Shapiro, Russell S. Berman, Nicolas Coudray, Iman Osman, Aristotelis Tsirigos
bioRxiv 610311; doi: https://doi.org/10.1101/610311
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A Deep Learning Approach for Rapid Mutational Screening in Melanoma
Randie H. Kim, Sofia Nomikou, Zarmeena Dawood, George Jour, Douglas Donnelly, Una Moran, Jeffrey S. Weber, Narges Razavian, Matija Snuderl, Richard Shapiro, Russell S. Berman, Nicolas Coudray, Iman Osman, Aristotelis Tsirigos
bioRxiv 610311; doi: https://doi.org/10.1101/610311

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