Deep Learning of P73 Biomarker Expression in Rectal Cancer Patients

• 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.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Cancer och onkologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cancer and Oncology (hsv//eng)

MEDICIN OCH HÄLSOVETENSKAP  -- Medicinsk bioteknologi -- Medicinsk bioteknologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Medical Biotechnology -- Medical Biotechnology (hsv//eng)

Nyckelord

Deep learning

convolutional neural networks

tumor protein

p73 expression

rectal cancer

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