Image-Based Network Analysis of DNp73 Expression by Immunohistochemistry in Rectal Cancer Patients

Abstract

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.

AI-based Five-Year Survival Prediction and Prognosis of DNp73 Expression in Rectal Cancer Patients

“…The use of AI offers more advantages over conventional methods for validating biomarkers in terms of high accuracy of prediction or prognosis instead of estimated percentage of survival time, objectivity, reproducibility, scale, and time. The work that we have recently reported in [14] carried out the fuzzy weighted recurrence network analysis of IHC images of DNp73 expression in surgical tumors (obtained after surgery) and biopsies (obtained before surgery) taken from a small cohort of rectal cancer patients and discovered the predictive power of DNp73 expression in the patients who were given pRT. The AI approach can be applied to explore the role of DNp73 in rectal cancer in this direction when a sufficiently large data set become available for reliable training of CNN models.…”

“…A recent study used a network-based approach for studying the IHC patterns of DNp73 expression in biopsies and surgically resected tumors in rectal cancer patients with or without pRT, and found that DNp73 expression in the patients with pRT had better survival [14]. The results indicated that IHC image patterns can provide useful information concerning the relationships of certain proteins with clinical outcomes.…”

p53/p73 Protein Network in Colorectal Cancer and Other Human Malignancies

“…∆Np73 expression was also related to the local recurrence of rectal cancer [291]. However, the same authors revealed a correlation of ∆Np73 expression and long survival time of rectal cancer patients who were subjected to preoperative radiotherapy [311]. Interestingly, higher levels of ∆Np73 in patient-derived exosomes were also noted in sera of advanced-stage CRC patients and were associated with shorter DFS [292].…”

Features and networks of the mandible on computed tomography

“…where I is the identity matrix of size c × c, and A a can be used to compute graph properties such as the characteristic path length and average clustering coefficient [19][20][21].…”