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Deep Learning of P73 Biomarker Expression in Rectal Cancer Patients
Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.
Department of Oncology, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden; nstitute of Digestive Surgery, Sichuan University, West China Hospital, Chengdu, China.
Örebro University, School of Medical Sciences.ORCID iD: 0000-0003-1834-1578
Department of Oncology, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden.
2019 (English)In: 2019 International Joint Conference on Neural Networks (IJCNN), IEEE , 2019Conference paper, Published paper (Refereed)
Abstract [en]

By applying deep learning, we were able to compare p73 protein expression patterns of different tissue types including normal mucosa, primary tumor and lymph node metastasis in rectal cancer patients using immunohistochemical slides. The pair-wise pattern comparisons were automatedly carried out by considering color, edge, blobs, and other morphological information in the images. We discovered that when the pattern dissimilarity between primary tumor and lymph node metastasis is relatively low among other tissue pairs (primary tumor and distant normal, biopsy and distant normal, biopsy and primary tumor, biopsy and primary tumor, lymph node metastasis and distant normal, lymph node metastasis and biopsy), there was an implication of short-time survival. This original result suggests a novel application of advanced artificial intelligence in machine learning for clinical finding in rectal cancer and encourages relevant study of multiple biomarker expressions in cancer patients.

Place, publisher, year, edition, pages
IEEE , 2019.
Series
IEEE International Joint Conference on Neural Networks (IJCNN), ISSN 2161-4393
Keywords [en]
Deep learning, convolutional neural networks, tumor protein, p73 expression, rectal cancer
National Category
Cancer and Oncology
Identifiers
URN: urn:nbn:se:oru:diva-84546DOI: 10.1109/IJCNN.2019.8852245ISI: 000530893804049Scopus ID: 2-s2.0-85073214014ISBN: 978-1-7281-1985-4 (electronic)OAI: oai:DiVA.org:oru-84546DiVA, id: diva2:1457782
Conference
International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, July 14-19, 2019
Available from: 2020-08-13 Created: 2020-08-13 Last updated: 2024-03-05Bibliographically approved

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Zhang, Hong

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CiteExportLink to record
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