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Liver cancer postoperative recurrence risk prediction method combining pathological images and clinical information

A technology of clinical information and pathological images, applied in the field of cancer recurrence risk prediction model construction, can solve the problem of unreliable judgment of recurrence risk

Pending Publication Date: 2020-04-10
深圳市华嘉生物智能科技有限公司
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Problems solved by technology

However, due to the influence of subjective factors and limitations of naked eye vision, it is not reliable to judge the risk of recurrence subjectively only by manual observation of pathological images.

Method used

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  • Liver cancer postoperative recurrence risk prediction method combining pathological images and clinical information
  • Liver cancer postoperative recurrence risk prediction method combining pathological images and clinical information
  • Liver cancer postoperative recurrence risk prediction method combining pathological images and clinical information

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Embodiment Construction

[0077] The present invention provides a method for predicting the recurrence risk of liver cancer based on pathological image features and clinical information. The technical features and advantages of the present invention will be described below in conjunction with the accompanying drawings and embodiments.

[0078] The embodiment data of the present invention originates from public database TCGA-LIHC, and code realization language is Python 3.7 and R 3.6, and specific implementation is as follows figure 1 As shown, a method for predicting the recurrence risk of liver cancer based on pathological image features and clinical information proposed by the present invention includes the following steps:

[0079] Step 1: Extract image features of pathological images, and sort out medically meaningful variables in clinical information;

[0080] Step 2: Data processing, including default value processing, dummy variable setting, removal of obviously unreasonable variables and normal...

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Abstract

The invention discloses a liver cancer postoperative recurrence risk prediction method combining pathological images and clinical information, and belongs to the technical field of cancer postoperative recurrence risk prediction model construction. The method comprises the steps: taking patient clinical information and patient tumor area pathological image features extracted by applying an image processing technology as basic variables, further calculating the interaction between the basic variables as input data, performing the fitting of a survival random forest model, and precisely predicting the survival time of a patient. The result of the embodiment of the invention shows that the cross validation efficacy evaluation index C-index of the model provided by the invention is superior tothe result of prediction by only using pathological image features or clinical information, and the accuracy of liver cancer postoperative recurrence risk prediction is significantly improved; besides, the invention further provides a postoperative recurrence risk classification index, so patients can be divided into a high recurrence risk subgroup and a low recurrence risk subgroup, and a doctorcan be helped to make a targeted treatment scheme for the patient.

Description

technical field [0001] The invention belongs to the technical field of constructing a cancer postoperative recurrence risk prediction model, and in particular relates to a method for predicting the postoperative recurrence risk of liver cancer combined with pathological images and clinical information. Specifically, based on the survival random forest model, combined with pathological image features and clinical parameter features, it is a method to accurately predict the recurrence risk of liver cancer patients after surgery. Background technique [0002] Cancer is a common health problem faced by all mankind. According to statistics from the National Cancer Center in 2019, liver cancer patients accounted for 9.42% of all cancer patients in 2015, ranking fourth in the incidence of malignant tumors. 13.94% of all patients died of cancer, ranking second in the mortality rate of malignant tumors. It can be seen that liver cancer not only has a high incidence rate, but also ...

Claims

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Application Information

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IPC IPC(8): G16H50/30G16H50/70
CPCG16H50/30G16H50/70
Inventor 华芮张游龙李嘉路
Owner 深圳市华嘉生物智能科技有限公司
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