Liver cancer metastasis prediction system and method based on nerve cell adhesion molecules

A liver cancer metastasis and adhesion molecule technology, applied in the field of cell monitoring, can solve the problem of low accuracy of liver cancer metastasis prediction

Pending Publication Date: 2022-03-29
WEST CHINA HOSPITAL SICHUAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention intends to provide a liver cancer metastasis prediction system based on nerve cell adhesion molecules, so as to solve the technical problem in the prior art that the prediction accuracy of liver cancer metastasis is not high

Method used

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  • Liver cancer metastasis prediction system and method based on nerve cell adhesion molecules
  • Liver cancer metastasis prediction system and method based on nerve cell adhesion molecules
  • Liver cancer metastasis prediction system and method based on nerve cell adhesion molecules

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Experimental program
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Embodiment 1

[0045] This embodiment is basically as attached figure 1 Shown: the liver cancer metastasis prediction system based on neural cell adhesion molecules, including sample collection module 1, sample analysis module 2 and control module 3;

[0046] The sample collection module 1 is used to collect venous blood samples of patients without liver cancer metastasis and patients with liver cancer metastasis in a fasting state to form a first analysis sample, and send the first analysis sample to the sample analysis module 2;

[0047] The sample analysis module 2 includes a preprocessing unit 4 and a sample analysis unit 5. The preprocessing unit 4 is used to perform anticoagulant treatment on the first analysis sample to obtain a second analysis sample; the sample analysis unit 5 is used to The analysis strategy analyzes and processes the second analysis sample, and obtains the analysis result of liver cancer metastasis prediction;

[0048] The control module 3 includes a data storage...

Embodiment 2

[0080] This embodiment is basically the same as Embodiment 1, the difference is that the sample analysis module 2 can analyze the collected analysis samples in combination with the analysis of the liver cancer metastasis prediction model, so as to obtain the liver cancer metastasis probability of the liver cancer patient and the estimated time of liver cancer metastasis, and pass The display unit 7 is displayed.

[0081] The specific implementation process of this embodiment is the same as that of Embodiment 1, the difference is that:

[0082] The sixth step is to establish a liver cancer metastasis prediction model based on the indicators directly related to liver cancer metastasis, and analyze the patient's analysis samples through the liver cancer metastasis prediction model. If the calculated value of the calculated model is less than 2.37, it is judged that there is no liver cancer metastasis in the patient. At the same time, combined with the in-depth analysis of the sam...

Embodiment 3

[0086] This embodiment is basically the same as Embodiment 1, the difference is that the NCM-based liver cancer metastasis prediction system also includes an intelligent module, which is used to provide the liver cancer patient with Advice on healthy lifestyle and effective methods for liver cancer not to recur.

[0087] The specific implementation process of this embodiment is the same as that of Embodiment 1, the difference is that:

[0088] The sixth step is to establish a liver cancer metastasis prediction model based on the indicators directly related to liver cancer metastasis, and analyze the patient's analysis samples through the liver cancer metastasis prediction model. If the calculated value of the calculated model is less than 2.37, it is judged that there is no liver cancer metastasis in the patient. At the same time, according to the specific test results of the liver cancer patients currently being tested, the intelligent module provides suggestions on healthy l...

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Abstract

The invention relates to the technical field of cell monitoring, and discloses a liver cancer metastasis prediction system and method based on nerve cell adhesion molecules, and the system comprises a sample collection module, a sample analysis module and a control module; the sample collection module is used for collecting analysis samples of a plurality of specific targets to form a first analysis sample and sending the first analysis sample to the sample analysis module; the sample analysis module analyzes and processes the first sample to find an index most related to liver cancer metastasis and establish a prediction model, so that whether liver cancer metastasis occurs or not is accurately judged, and finally the judgment result is displayed in a display unit of the control module in real time. The method has the advantages that the liver cancer metastasis prediction accuracy is improved, and the survival rate and the survival quality of liver cancer patients are guaranteed.

Description

technical field [0001] The invention relates to the technical field of cell monitoring, in particular to a system and method for predicting liver cancer metastasis based on nerve cell adhesion molecules. Background technique [0002] Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. As a malignant tumor, its incidence rate ranks fifth in the world and its mortality rate ranks third, seriously endangering human health [1]. The main pathogenic factors of hepatocellular carcinoma (hereinafter referred to as liver cancer or HCC) include hepatitis B virus (HBV) infection, hepatitis C virus (HCV) infection and alcoholic liver disease. [0003] In recent years, although various treatment methods have been continuously improved, the mortality rate of liver cancer is still high. The main reason is the high recurrence rate and extrahepatic metastasis after liver cancer resection. Based on this, screening and judging the prognosis index of liver c...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N33/574G01N33/543G01N33/535G01N33/68
CPCG01N33/57438G01N33/6863G01N33/54306G01N33/535G01N2333/70525
Inventor 唐红刘昌海周凌云
Owner WEST CHINA HOSPITAL SICHUAN UNIV
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