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Constructing method of predicting model and nomograph for HCC recurrence and RFS and application thereof

A technique for predicting models and constructing methods, which can be applied to medical images, character and pattern recognition, health index calculation, etc., and can solve problems such as low specificity, poor universality, and low sensitivity

Inactive Publication Date: 2019-09-20
BEIJING YOUAN HOSPITAL CAPITAL MEDICAL UNIV
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Problems solved by technology

[0009] (1) Poor universality;
[0010] (2) Low sensitivity;
[0011] (3) Low specificity;
[0012] (4) Low accuracy rate;

Method used

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  • Constructing method of predicting model and nomograph for HCC recurrence and RFS and application thereof
  • Constructing method of predicting model and nomograph for HCC recurrence and RFS and application thereof
  • Constructing method of predicting model and nomograph for HCC recurrence and RFS and application thereof

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Embodiment Construction

[0048] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with examples of implementation. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0049] The application principle of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0050] Such as figure 1 As shown, the method for constructing the prediction model and nomogram for HCC recurrence and RFS provided by the implementation examples of the present invention includes the following steps:

[0051] S101: Using MRMRA, 647 radiomics features were extracted from the three-phase CECT scan images of the liver of all patients;

[0052] S102: Using the LASSO-Cox regression model to confirm the radiomics label;

[0053] S103: Establish a prediction mo...

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Abstract

The invention belongs to the field of predicting model and nomograph construction technology, and discloses a constructing method of a predicting model and the nomograph for HCC recurrence and RFS and application thereof. The method comprises the steps of by means a minimum redundancy maximum relevance algorithm (MRMRA), respectively extracting 647 radiomics characteristics from liver 3-period enhanced CT scanning images of all patients; confirming a radiomics label by means of a least absolute shrinkage and selection operator (LASSO)-Cox regression model; and establishing a predicting model and a predicting naomograph for HCC recurrence after therapeutic ablation treatment and teh RFS by means of clinical and pathological factors and the radiomics label. According to the method of the invention, the predicting model and the predicting nomograph are constructed through the radiomics method. The method is used for predicting tumor recurrence and RFS of an HCC patient after therapeutic ablation treatment of a focus.

Description

technical field [0001] The invention belongs to the technical field of prediction model and nomogram construction, and in particular relates to a construction method and application of a prediction model and nomogram for HCC recurrence and RFS. Specifically, it is a construction method and application of a predictive model and a nomogram for hepatocellular carcinoma (hepatocellular carcinoma, HCC) recurrence and recurrence free survival (RFS). Background technique [0002] Currently, the closest prior art: [0003] There is no prediction model similar to the existing technology. At present, HCC recurrence is predicted based on the results of alpha fetoprotein (AFP) in patients. However, once AFP increases, the tumor may already be obvious; About 20%) are AFP-negative HCC. Even if tumor recurrence occurs, AFP is still negative. Therefore, monitoring AFP in negative HCC patients cannot predict HCC recurrence. There is also a method to predict recurrence based on the degree o...

Claims

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Application Information

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IPC IPC(8): G16H10/20G16H30/20G16H50/30G16H70/60G06K9/32G06K9/34G06K9/46
CPCG16H30/20G16H50/30G16H10/20G16H70/60G06V10/25G06V10/267G06V10/40
Inventor 袁春旺王振常田捷魏靖伟顾东升赵鹏何宁高文峰杨晓珍孙玉
Owner BEIJING YOUAN HOSPITAL CAPITAL MEDICAL UNIV
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