Relevant prediction system for colorectal cancer, electronic equipment and storage medium

Abstract

The invention discloses a relevant prediction system for the colorectal cancer. The relevant prediction system comprises a collection unit, an analysis unit and a processing unit, wherein the collection unit is used for collecting and storing the faeces of relevant personnel, and the faeces can be called a faeces specimen; the analysis unit is used for analyzing the faeces to obtain an analysis sample, wherein the analysis sample comprises one or multiple of microorganism information and microorganism metabolite information; and the processing unit is used for constructing a prediction model according to the analysis sample and a machine learning model and obtaining a prediction result according to a target sample and the prediction model, wherein the target sample is a sample obtained byanalyzing the faeces of the relevant personnel. The invention also discloses electronic equipment and a computer storage medium. By use of the invention, through faeces analysis, relevant predictionsof the colorectal cancer can be obtained.

Description

technical field [0001] The invention relates to the technical field of colorectal cancer prediction, in particular to a colorectal cancer related prediction system, electronic equipment and storage medium. Background technique [0002] Colorectal cancer (CRC) is one of the most common clinical malignant tumors and one of the leading causes of cancer-related deaths. It is estimated that by 2030, there will be more than 2.2 million new cases worldwide, and the death cases to 1.1 million. The 5-year survival time of patients with early colorectal cancer is more than 40%, and the 5-year PFS (progression-free survival time) of patients reaches 22%. Although colorectal cancer has made great progress in comprehensive treatment and MDT (multidisciplinary diagnosis and treatment model) in the past few years, the median PFS of most stage III and IV colorectal cancer is about 13-15 months, and OS ( overall survival) was less than 3 years. Therefore, early screening and early predict...

AI Technical Summary

Problems solved by technology

Although colorectal cancer has made great progress in comprehensive treatment and MDT (multidisciplinary diagnosis and treatment model) in the past few years, the median PFS of most stage III and IV colorectal cancer is about 13-15 months, and OS ( overall survival) less than 3 years

However, these clinical and signs are often not easy to attract the attention of patients

(2) Imaging colonoscopy: ① Ordinary endoscopy is currently the most effective method for detecting asymptomatic precancerous polyps and colorectal cancer, but it needs to be cleaned first, and general anesthesia and invasive procedures expose patients to potential complications. serious complications; ②CT colonoscopy is an emerging non-invasive examination method, but it has defects such as radiation exposure, lack of standardization, and high false negative rate; ③Sigmoidoscopy can prevent a small number of proximal colon cancers, but protects the right The benefit of semi-colon cancer is low; ④ Capsule colonoscopy can complete endoscopic imaging without invasive operation, avoiding the risk of colonoscopy, but the requirements for bowel preparation are stricter than colonoscopy; (3) Serological examination: Such as tumor markers, Septin9 test, etc., have been used clinically, but the sensitivity is low

(4) Pathological examination: pathological biopsy report is the gold standard for the diagnosis of colorectal cancer, but the difficulty in obtaining materials prevents it from being used as a method for screening and early diagnosis of colorectal cancer

In summary, the current methods for predicting and early diagnosis of colorectal cancer are limited by various factors, and the risk assessment of colorectal cancer in healthy people has not yet been incorporated into clinical applications. Therefore, it is urgent to find new ways for early screening and risk assessment of colorectal cancer. Clinical Problems Solved

Images