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Numrering	Referens	Omslagsbild	Hitta
1.	Pham, Tuan, et al. (författare) Deep Learning of P73 Biomarker Expression in Rectal Cancer Patients 2019 Ingår i: 2019 International Joint Conference on Neural Networks (IJCNN). - : IEEE. - 9781728119854 - 9781728119861 Konferensbidrag (refereegranskat)abstract 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.

Pham, Tuan, et al. (författare)
Deep Learning of P73 Biomarker Expression in Rectal Cancer Patients
2019
Ingår i: 2019 International Joint Conference on Neural Networks (IJCNN). - : IEEE. - 9781728119854 - 9781728119861
Konferensbidrag (refereegranskat)abstract
- 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.

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