Construction method of chronic hepatitis B cirrhosis prediction model and prediction method of chronic hepatitis B cirrhosis prediction model

A predictive model and technology for chronic hepatitis B, applied in computing models, medical simulation, 2D image generation, etc., can solve the problems of cost, inaccurate prediction, and inability to be widely used, so as to achieve multiple clinical benefits and improve diagnosis performance, effect of improved reproducibility

Pending Publication Date: 2022-06-24
南京亨达生物科技有限公司
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Problems solved by technology

[0003] The purpose of the present invention is to solve the problem that the existing methods for predicting liver fibrosis and liver cirrhosis cannot be widely used due to cost problems, and the prediction effect is not accurate enough, and propose a method for constructing a chronic hepatitis B cirrhosis prediction model and its prediction method

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  • Construction method of chronic hepatitis B cirrhosis prediction model and prediction method of chronic hepatitis B cirrhosis prediction model
  • Construction method of chronic hepatitis B cirrhosis prediction model and prediction method of chronic hepatitis B cirrhosis prediction model
  • Construction method of chronic hepatitis B cirrhosis prediction model and prediction method of chronic hepatitis B cirrhosis prediction model

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specific Embodiment approach 1

[0057] The construction method of a kind of chronic hepatitis B liver cirrhosis prediction model of the present embodiment, such as figure 1 and Figure 5 As shown, the method is realized through the following steps:

[0058] Step 1. Delineate the ROI in the CT plain scan image at the level of the right portal vein of the liver, wherein the Chinese meaning of ROI is the region of interest, and the full English name is region on interest;

[0059] Step 2, extracting radiomics features in the defined ROI, extracting radiomics features including first-order statistics, texture features and wavelet transform;

[0060] Step 3. Select radiomics features using interobserver and interobserver reproducibility and lasso regression;

[0061] Step 4: Construct a preliminary radiomics model with a support vector machine, and build a prediction model Y based on a nomogram of radiomics by combining a simple radiomics model with independent predictors of serum indicators:

[0062] Y=0.734×...

specific Embodiment approach 2

[0069] The difference from Embodiment 1 is that in the method for constructing a predictive model of chronic hepatitis B cirrhosis in this embodiment, in the step of delineating the ROI in the CT plain scan image at the level of the right portal vein of the liver described in Step 1,

[0070] 3Dslicer software (version 4.8.0; http: / / www.slicer.org) was used to select the ROI area in the right portal vein level CT plain scan images of multiple livers, and the content shown in the ROI area was removed from the plain CT images. The great vessels of the liver are delineated along the margin of the right lobe at the level of the right portal vein; Figure 2a Shown is a plain CT scan image of the liver at the level of the right portal vein, Figure 2b Shown is the delineation of the ROI area in the plain CT image at the level of the right portal vein of the liver; where the area of ​​the ROI ranges from 19 to 106 cm 2 .

specific Embodiment approach 3

[0071] The difference from the second specific embodiment is that in the construction method of a chronic hepatitis B cirrhosis prediction model in this embodiment, the average area of ​​the ROI is 47±15cm 2 .

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Abstract

The invention discloses a construction method of a chronic hepatitis B cirrhosis prediction model and a chronic hepatitis B cirrhosis prediction method. The existing liver fibrosis and liver cirrhosis prediction method cannot be widely applied due to the cost problem, and the prediction effect is not accurate enough. A construction method of a chronic hepatitis B cirrhosis prediction model and a chronic hepatitis B cirrhosis prediction method are disclosed, an ROI is delimited in a right portal vein level CT plain scan image of a liver, the Chinese meaning of the ROI is an interested area, radiomics features are extracted in the delimited ROI, and the extracted radiomics features comprise first-order statistics, texture features and wavelet transform; selecting radiomics characteristics by using repeatability between observers and repeatability between the observers and lasso regression; establishing a preliminary image omics model by using a support vector machine, and combining a pure image omics model and the serum index independent prediction factor to construct a prediction model based on a nomogram of image omics. According to the method, non-invasive liver cirrhosis prediction can be carried out by combining the CT image with the serum index independent prediction factor.

Description

technical field [0001] The invention relates to the construction and prediction of a liver cirrhosis prediction model, in particular to a construction method of a chronic hepatitis B liver cirrhosis prediction model and a prediction method thereof. Background technique [0002] Noninvasive methods currently used to predict the degree of liver fibrosis include serum indices and elastography. TE and MRE are known to have excellent diagnostic performance for liver fibrosis staging. However, these well-performing on-device approaches have not been widely used due to high prices. In China, HBV carriers are usually recommended to undergo enhanced CT examination to determine whether they have tumors, but due to limited cost-effectiveness, many patients only receive plain CT examination. Although contrast-enhanced CT or MRI can provide more information than unenhanced CT, we hoped to develop noninvasive models to predict cirrhosis based on readily available data at relatively low ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/30G16H50/50G06T11/00G06K9/62G06N20/00A61B6/03A61B6/00
CPCG16H50/30G16H50/50G06T11/003G06N20/00A61B6/032A61B6/469A61B6/50G06F18/2411G06F18/214
Inventor 尹胤王锦程刘巧玉徐晓亮刘洋周喆聿王琨张文杰
Owner 南京亨达生物科技有限公司
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