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Prediction of RAS gene status of CRLM patient based on radiomics and semantic features

A radiomics and gene technology, applied in the field of intelligent medical care, can solve the problem of high cost and achieve the effect of rapid prediction

Pending Publication Date: 2022-07-22
中国医科大学
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Problems solved by technology

Currently, detection of the mutation status of the RAS gene usually requires the analysis of tissue samples obtained through surgery or biopsy, which is not only invasive but also expensive

Method used

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  • Prediction of RAS gene status of CRLM patient based on radiomics and semantic features
  • Prediction of RAS gene status of CRLM patient based on radiomics and semantic features

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Experimental program
Comparison scheme
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Embodiment

[0039] 1. Research methods and results.

[0040] 1.1 Case collection.

[0041] According to the international ISBER standard, patients with colorectal cancer liver metastases from January 2014 to October 2019 were retrospectively collected.

[0042] Inclusion criteria: 1) Patients with advanced colorectal cancer liver metastases confirmed by pathology; 2) Age 18-80 years; 3) ECOG score 0-2; 4) RAS gene mutation status detection in surgical or biopsy specimens 5) The enhanced CT image of the lung and abdomen before the start of treatment can be obtained, and the thickness of the CT image is ≤2 mm; 6) The time interval between the enhanced CT examination of the lung and abdomen and the acquisition of surgical or needle biopsy specimens should not exceed 30 days (range 4-30 days). ).

[0043] Exclusion criteria: 1) patients with other tumors; 2) CT images with artifacts caused by implanted metal or motion; 3) the borders of liver metastases in the CT images are too blurred to a...

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Abstract

The invention belongs to the technical field of intelligent medical treatment, and particularly relates to a model for predicting the RAS gene state of a patient suffering from advanced colorectal cancer liver metastasis based on radiomics characteristics and application of the model. According to the method, a model for predicting the RAS gene state of the advanced colorectal cancer liver metastasis patient based on the radiomics characteristics is successfully constructed, specifically, an enhanced CT image of the patient before initial treatment is collected, the radiomics characteristics are extracted, 12 radiomics characteristics are obtained through 1000 times of Lasso-Logistic analysis, and a radiomics prediction model is constructed through a multi-factor logistic regression method. According to the method, the enhanced CT image is utilized, the RAS gene state of the colorectal cancer liver metastasis patient can be economically, noninvasively and rapidly predicted, genetic typing is assisted, clinical doctors can be helped to make accurate individualized treatment decisions, and the method has great clinical significance.

Description

technical field [0001] The invention belongs to the technical field of intelligent medical treatment, and in particular relates to a model for predicting the RAS gene status of patients with liver metastases from advanced colorectal cancer based on imaging omics features and its application. Background technique [0002] Colorectal cancer (CRC) is the third most common cause of cancer-related death in the world. Previous studies have shown that about 25-30% of patients diagnosed with colorectal cancer develop liver metastases during the course of the disease. In colorectal cancer patients, RAS (KRAS and NRAS) gene mutation status is increasingly used to guide clinical decision-making. In the surgical treatment of colorectal liver metastasis (CRLM) patients, the recommendation for a safe surgical margin is at least 10 mm. However, for CRLM patients with RAS mutations, there is no clear optimal resection margin. Therefore, the choice of surgical margins for liver metastases...

Claims

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Application Information

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IPC IPC(8): G16B20/50G16B20/20G06T7/11
CPCG16B20/50G16B20/20G06T7/11G06T2207/10081G06T2207/20104
Inventor 王昕缇石芮川孟林
Owner 中国医科大学
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