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correlation analysis method based on CT imaging characteristics and prognosis conditions of non-small cell lung cancer patients

A non-small cell lung cancer and correlation analysis technology, applied in image analysis, image enhancement, image data processing, etc., can solve the problems of few types of clinical information and difficult quantitative evaluation of tumor heterogeneity

Inactive Publication Date: 2019-04-19
杭州英库医疗科技有限公司 +1
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Problems solved by technology

However, this type of method also has limitations. There are fewer types of clinical information that can be used, and the characteristics of medical signs only show part of the morphological characteristics of the tumor area. From the perspective of imaging, more and more types of information can be obtained Richer imaging features can reflect more hidden information of tumors, which can effectively solve the problem that tumor heterogeneity is difficult to quantitatively evaluate

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  • correlation analysis method based on CT imaging characteristics and prognosis conditions of non-small cell lung cancer patients
  • correlation analysis method based on CT imaging characteristics and prognosis conditions of non-small cell lung cancer patients
  • correlation analysis method based on CT imaging characteristics and prognosis conditions of non-small cell lung cancer patients

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[0016] The specific implementation manners of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0017] The specific implementation process is as follows:

[0018] 1. The processing of CT images: firstly, the tumor is found from the CT images through RadiAntViewer software, an interactive medical image control system, and its approximate area is selected by a frame to obtain sequential images of the tumor. Then, the semi-automatic segmentation method was used to segment the tumor, and different segmentation schemes were adopted for different types of tumors: (1) solitary tumor: take the intersection of gray threshold segmentation algorithm and region growing algorithm segmentation results; (2) adhesion lung wall type Tumor: First, the edge of the lung area is repaired based on the chain code method and the "rolling ball method". Then, refer to the solitary tumor segmentation method for further segmentation, and finally, ch...

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Abstract

The invention provides a correlation analysis method based on CT imaging characteristics and prognosis survival conditions of non-small cell lung cancer patients, and belongs to the field of computeraided medicine. The method comprises the following steps: firstly, carrying out segmentation and feature extraction and optimization on lung tumors; And then, combining the tumor imaging characteristics with the survival condition of the patient through a machine learning method, constructing a prognostic evaluation model of the non-small cell lung cancer to predict the prognostic survival condition of the patient, and designing an experiment to evaluate the performance of the prognostic analysis model. The invention provides a new scheme for exploring the relationship between the imaging characteristics and the prognostic survival condition of the patient.

Description

technical field [0001] The invention relates to the field of computer-aided medicine, and relates to a correlation analysis method based on CT imaging features and prognosis of patients with non-small cell lung cancer. Background technique [0002] The International Agency for Research on Cancer (IARC) of the World Health Organization (WHO) recently released a new report stating that lung cancer is the malignant tumor with the fastest-growing morbidity and mortality worldwide, and it is expected to cause 1.8 million deaths in 2018, accounting for 1% of the estimated cancer deaths. 18.4% of the total population. According to histological classification, lung cancer is divided into non-small cell lung cancer and small cell lung cancer. Among them, non-small cell lung cancer (NSCLC) accounts for 80% to 85% of the total number of lung cancer patients, including squamous Cell carcinoma (squamous cell carcinoma), adenocarcinoma, large cell carcinoma. Compared with small cell lun...

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Application Information

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IPC IPC(8): G06T7/00G06K9/62
CPCG06T7/0012G06T2207/30096G06T2207/10081G06T2207/20081G06F18/23G06F18/2135G06F18/24
Inventor 郑军聂生东郭小辉王旭顾海洋徐江松
Owner 杭州英库医疗科技有限公司
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