Analytical method for prognosis survival case of non-small cell lung cancer

Abstract

The invention provides an analytical method for prognosis survival case of non-small cell lung cancer. The analytical method for prognosis survival case of non-small cell lung cancer designs an experiment to perform prognosis survival analysis and research on a non-small cell lung cancer patient, constructs a prognostic analysis model for the non-small cell lung cancer based on CT image omics characteristics, according to a traditional imaging omics research framework, performing tumor segmentation, characteristic extraction, characteristic screening, and modeling of a correlation analysis andprognosis survival analysis model for image omics characteristics and the prognosis survival case on the data of the non-small cell lung cancer patient to obtain an image omics prognostic factor andprognosis survival analysis model correlated with prognosis survival significance of the patient with the non-small cell lung cancer, so as to provide the doctor with data information including the survival time of the patient and a series of late lesion development situations, and at the same time, to evaluate the performance of the prognosis survival model to ensure the accuracy of the prognosissurvival model.

Description

technical field [0001] The invention relates to the technical field of computer-aided medicine, in particular to an analysis method for the prognosis and survival of 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 lung cancer, non-small cell ...

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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 radiomics, more and more More types of radiomics features can reflect more hidden information of tumors, which can effectively solve the problem of difficult quantitative assessment of tumor heterogeneity

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