Prognosis evaluation device and method for colorectal cancer established based on characteristics of tumor tissue infiltration immune cell

Abstract

A prognosis evaluation device and method for colorectal cancer established based on characteristics of a tumor tissue infiltration immune cell detect an infiltration condition of three immune cells intissue of the colorectal cancer by utilizing immunohistochemistry and related techniques; and counting is performed on the three detected immune cells detected to obtain the number of immune cells infiltrated per unit area, a relationship between each of the immune cells and prognosis of patients is obtained through combination of clinical prognosis information of the patients, a prognostic evaluation model is constructed by utilizing a multiple linear regression model and with the number of infiltration per unit area of regulatory T cells, macrophages, and neutrophils as an independent variable, and the model can accurately and quickly predict the prognosis of the patients with the colorectal cancer.

Description

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AI Technical Summary

Benefits of technology

This patented technology uses specific features from blood vessels or other body fluids found inside colonic carcinoma to help diagnose it better than traditional methods like histopathology which only look at one aspect of disease (the presence). By counting these different types of lymphocytes called Immunoblasts, this system helps identify how well they were working against certain treatments for Colon Cancer Patients. It also includes factors such as inflammatory cytokines released by activated white blood cells during treatment, microscopically visible changes caused by chemotherapy drugs administered through infusion into the bowel wall, and gene expression data collected over time. These results provide valuable insights about diagnosis and therapies effectiveness across various stages of development.

Problems solved by technology

This patented technical problem addressed in this patents relates to improving the ability to predict how aggressively healthy individuals will develop metastatic disease during treatment compared to those who don't. Current models only consider either single cell data from multiple treatments or small numbers of samples taken over several months due to lacking specific markers indicating whether they may benefit some patient groups differently based upon age/outcome levels.