Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks
Recent years have seen a growing awareness of the role the immune system plays in successful cancer treatment, especially in novel therapies like immunotherapy. The characterization of the immunological composition of tumors and their micro-environment is thus becoming a necessity. In this paper we...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
April 10, 2019
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| In: |
PeerJ
Year: 2019, Jahrgang: 7 |
| ISSN: | 2167-8359 |
| DOI: | 10.7717/peerj.6335 |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.7717/peerj.6335
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| Verfasserangaben: | Lilija Aprupe, Geert Litjens, Titus J. Brinker, Jeroen van der Laak, Niels Grabe |
| Zusammenfassung: | Recent years have seen a growing awareness of the role the immune system plays in successful cancer treatment, especially in novel therapies like immunotherapy. The characterization of the immunological composition of tumors and their micro-environment is thus becoming a necessity. In this paper we introduce a deep learning-based immune cell detection and quantification method, which is based on supervised learning, i.e., the input data for training comprises labeled images. Our approach objectively deals with staining variation and staining artifacts in immunohistochemically stained lung cancer tissue and is as precise as humans. This is evidenced by the low cell count difference to humans of 0.033 cells on average. This method, which is based on convolutional neural networks, has the potential to provide a new quantitative basis for research on immunotherapy. |
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| Beschreibung: | Gesehen am 03.06.2019 |
| Beschreibung: | Online Resource |
| ISSN: | 2167-8359 |
| DOI: | 10.7717/peerj.6335 |