Method for establishing liver cancer diagnosis model based on liver cancer triple detection

A diagnostic model and method establishment technology, which is applied in the field of clinical examination and diagnosis, can solve problems such as differences in test report issuance time, deviation of the final calculation result of the model, maintenance difficulty and result variation, etc., so as to improve clinical application effect, clinical applicability and Feasible and easy to obtain effects

Pending Publication Date: 2021-02-05
高春芳
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Problems solved by technology

Although the GAPTALAD model established by our team has a high diagnostic efficiency for HBV-related HCC, the laboratory indicators contained in the model involve detection systems of multiple clinical tests and multiple sub-disciplines, and there are many reporting units, so the use of the model will be affected. Limitations of many factors such as different testing sub-disciplines, different testing principles, and different instruments and equipment: First, the release time of the test reports of the professional groups of each testing sub-discipline is different, and the final model results require the coordination and cooperation of multiple professional groups, which limits the clinical suitability ; Second, the automatic calculation of model values ​​by too many reporting units has certain maintenance difficulties and the risk of result variation calculation; The final calculation results of the model may be biased

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  • Method for establishing liver cancer diagnosis model based on liver cancer triple detection
  • Method for establishing liver cancer diagnosis model based on liver cancer triple detection
  • Method for establishing liver cancer diagnosis model based on liver cancer triple detection

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0057] Example 1 Establishment of Liver Cancer Diagnosis Model C-GALAD

[0058] The model parameters selected in this example are the patient’s basic information gender (G), age (A), liver cancer serological markers alpha-fetoprotein heterogeneity (L), alpha-fetoprotein (A), abnormal prothrombin (D) , and then use binary Logistic regression to conduct multivariate binary Logistic regression analysis on all the included indicators, and on this basis, establish a multivariate Logistic regression model based on gender, age and liver cancer triple detection (AFP, AFP-L3, DCP) C -GALAD, the results are shown in Table 2.

[0059] Table 2 Multivariate Logistic regression model C-GALAD

[0060] Variable beta (95%CI) OR (95%CI) P value Gender(G) 1.329(1.142-1.516) 3.778(3.134-4.554) <0.001

Age(A) 0.044(0.037-0.052) 1.045(1.038-1.053) <0.001

log 10 AFP,ng / mL(A)

0.885(0.750-1.020) 2.423(2.117-2.773) <0.001

log 10 DCP,mAU / mL(D)

...

Embodiment 2

[0065] Example 2 Establishment of Liver Cancer Diagnosis Model LAD

[0066] The model parameters selected in this embodiment are liver cancer serological markers alpha-fetoprotein heterogeneity (L), alpha-fetoprotein (A) and abnormal prothrombin (D). Factor binary Logistic regression analysis, and on this basis, a multi-factor Logistic regression model LAD based on liver cancer triple detection (AFP, AFP-L3, DCP) was established. The results are shown in Table 3.

[0067] Table 3 Multivariate Logistic regression model LAD

[0068] Variable beta (95%CI) OR (95%CI) P value log 10 AFP,ng / mL(A)

0.892(0.759-1.025) 2.439(2.136-2.786) <0.001

log 10 DCP,mAU / mL(D)

3.524(3.277-3.772) 33.931(26.488-43.464) <0.001

AFP-L3, %(L) 0.057(0.039-0.075) 1.059(1.040-1.078) <0.001

Constant -5.930

[0069] Among them, the Chinese names and abbreviations corresponding to English in Table 3 are as follows:

[0070] (1) AFP-L3, alpha...

Embodiment 3

[0073] Example 3 Diagnostic Efficiency Verification

[0074] (1) Verification of the diagnostic efficiency of liver cancer diagnostic model C-GALAD in patients with primary hepatocellular carcinoma

[0075] According to the maximum principle of Youden index (Sensitivity+Specificity-1), the cut-off value is 0.9382. In the training set, the C-GALAD model had a sensitivity of 86.9%, a specificity of 90.0%, an accuracy of 87.6%, and an area under the curve of 0.952 [95% CI (0.947-0.957 )]; in the verification group, the sensitivity of the C-GALAD model to the diagnosis of patients with primary hepatocellular carcinoma was 86.9%, the specificity was 80.2%, the accuracy was 85.2%, and the area under the curve was 0.908 [95% CI ( 0.889-0.926)] (see Table 4 and figure 1 , figure 2 ), and the model cut-off value is between 0.5268 and 1.1734, the Youden index is almost the same, which conforms to the principle of the maximum Youden index, and the sensitivity and specificity of the m...

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Abstract

The invention discloses a method for establishing a liver cancer diagnosis model based on liver cancer triple detection. The method comprises the following steps of: 1, establishing a primary hepatocellular carcinoma database of primary hepatocellular carcinoma clinical characterization and laboratory data, and collecting laboratory indexes of a patient; and 2, arranging laboratory indexes, and establishing a multi-factor Logistic regression model to obtain a liver cancer diagnosis model. The laboratory indexes of the liver cancer diagnosis model CGALAD comprise the basic information gender and age of a patient, and liver cancer serological marker alpha fetoprotein heteroplasmon, alpha fetoprotein and abnormal prothrombin. Laboratory indexes of the liver cancer diagnosis model LAD compriseliver cancer serological marker alpha fetoprotein heteroplasmon, alpha fetoprotein and abnormal prothrombin. Parameters of the two liver cancer diagnosis models are easy to obtain, the parameters areon the same inspection subdisciplinary detection platform, a single report unit is few, influence factors are few, so that the clinical applicability and feasibility of the method are high.

Description

technical field [0001] The invention belongs to the technical field of clinical examination and diagnosis, and in particular relates to a method for establishing a liver cancer diagnosis model based on liver cancer triple detection. Background technique [0002] Worldwide, the incidence of liver cancer accounts for the sixth among all cancers, and the mortality rate ranks the third, mainly including hepatocellular carcinoma (HCC) (about 75%-85%), intrahepatic cholangiocarcinoma (about 10%-15%) and some other rare pathological types. Although Chinese hepatologists and researchers have made remarkable progress in the prevention, diagnosis, management and treatment of liver diseases in the past two decades, the overall 5-year survival rate is still lower than 40%. Two-thirds of liver cancers are in the middle and advanced stages, and the best time for liver resection and liver transplantation has been lost, and even palliative treatment cannot be implemented. Therefore, findi...

Claims

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Application Information

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IPC IPC(8): G16H50/20G16H50/70G16B20/00
CPCG16H50/20G16H50/70G16B20/00
Inventor 高春芳童林林长青方建庆
Owner 高春芳
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