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Image-Based Network Analysis of DNp73 Expression by Immunohistochemistry in Rectal Cancer Patients
Department of Biomedical Engineering, Linköping University, Linköping, Sweden; The Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia.
Department of Oncology, Clinical and Experimental Medicine, Linköping University, Linköping, Sweden; Institute of Digestive Surgery, West China Hospital, Sichuan University, Chengdu, China.
Department of Oncology, Clinical and Experimental Medicine, Linköping University, Linköping, Sweden.
Örebro universitet, Institutionen för medicinska vetenskaper.
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2020 (Engelska)Ingår i: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 10, artikel-id 1551Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Background: Rectal cancer is a disease characterized with tumor heterogeneity. The combination of surgery, radiotherapy, and chemotherapy can reduce the risk of local recurrence. However, there is a significant difference in the response to radiotherapy among rectal cancer patients even they have the same tumor stage. Despite rapid advances in knowledge of cellular functions affecting radiosensitivity, there is still a lack of predictive factors for local recurrence and normal tissue damage. The tumor protein DNp73 is thought as a biomarker in colorectal cancer, but its clinical significance is still not sufficiently investigated, mainly due to the limitation of human-based pathology analysis. In this study, we investigated the predictive value of DNp73 in patients with rectal adenocarcinoma using image-based network analysis.

Methods: The fuzzy weighted recurrence network of time series was extended to handle multi-channel image data, and applied to the analysis of immunohistochemistry images of DNp73 expression obtained from a cohort of 25 rectal cancer patients who underwent radiotherapy before surgery. Two mathematical weighted network properties, which are the clustering coefficient and characteristic path length, were computed for the image-based networks of the primary tumor (obtained after operation) and biopsy (obtained before operation) of each cancer patient.

Results: The ratios of two weighted recurrence network properties of the primary tumors to biopsies reveal the correlation of DNp73 expression and long survival time, and discover the non-effective radiotherapy to a cohort of rectal cancer patients who had short survival time.

Conclusion: Our work contributes to the elucidation of the predictive value of DNp73 expression in rectal cancer patients who were given preoperative radiotherapy. Mathematical properties of fuzzy weighted recurrence networks of immunohistochemistry images are not only able to show the predictive factor of DNp73 expression in the patients, but also reveal the identification of non-effective application of radiotherapy to those who had poor overall survival outcome.

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Frontiers Media S.A., 2020. Vol. 10, artikel-id 1551
Nyckelord [en]
fuzzy weighted recurrence networks, network properties, multi-channel images, DNp73, immunohistochemistry, predictive biomarker, rectal cancer, survival outcome
Nationell ämneskategori
Cancer och onkologi Fysiologi
Identifikatorer
URN: urn:nbn:se:oru:diva-79608DOI: 10.3389/fphys.2019.01551ISI: 000508453300001PubMedID: 31969833OAI: oai:DiVA.org:oru-79608DiVA, id: diva2:1390090
Tillgänglig från: 2020-01-31 Skapad: 2020-01-31 Senast uppdaterad: 2020-01-31Bibliografiskt granskad

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