Method for constructing prediction model of survival rate after lung cancer surgery and prediction model system
A predictive model and post-operative technology, applied in the field of surgery, can solve the problems of inaccurate disease-free survival rate, large difference in disease-free survival rate of patients, and lack of precision in disease-free survival rate, so as to achieve the effect of improving accuracy
Active Publication Date: 2022-04-12
BIOISLAND LAB +1
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However, using TNM staging to predict the disease-free survival rate after lung cancer surgery is not accurate enough. The disease-free survival rate of different patients at the same stage varies greatly, and the prediction of the postoperative disease-free survival rate is very inaccurate.
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[0094] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. Also, for clarity, parts not related to describing the exemplary embodiments are omitted in the drawings.
[0095] In the present disclosure, it should be understood that terms such as "comprising" or "having" are intended to indicate the existence of labels, numbers, steps, acts, components, parts or combinations thereof disclosed in this specification, and are not intended to exclude one or multiple other labels, numbers, steps, acts, parts, parts or combinations thereof exist or are added to the possibility.
[0096] In addition, it should be noted that, in the case of no conflict, the embodiments in the present disclosure and the labels in the embodiments can be combined with each other. The present disclosure will be described in detail below with reference to the accompanyi...
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The embodiment of the present disclosure discloses a method and system for predicting survival rate after lung cancer surgery. Among them, the method for predicting the survival rate after lung cancer surgery by measuring clinical data including gene mutation typing includes: a data acquisition step, obtaining clinical data after lung cancer surgery; a preprocessing step, classifying and grouping the clinical data after lung cancer surgery, The clinical data of the modeling group and the clinical data of the verification group are obtained; the risk factor screening step is to screen the risk factors of the clinical data of the modeling group to obtain the risk factor data and overall survival data; the regression analysis step is to analyze the risk factor data and overall survival Regression analysis was performed on the data to obtain the data after regression analysis. The clinical data after lung cancer surgery included gene mutation type, age, tumor size, lymph node metastasis, and surgical method.
Description
technical field [0001] The present disclosure relates to the field of surgery, in particular to a model building method and a prediction model system for predicting the survival rate of lung cancer after surgery by measuring clinical data including gene mutation typing. Background technique [0002] Early lung cancer includes a subset of stage I, II, and stage III disease. The standard treatment for non-small cell lung cancer is radical resection. After lung cancer surgery, it is necessary to predict the survival rate of patients after surgery. [0003] In the prior art, the TNM staging is used to predict the disease-free survival rate after lung cancer surgery. The seventh edition of the TNM staging system, the most widely used staging system, stratifies patients with non-metastatic NSCLC according to the size and extent of tumor invasion and lymph node involvement. However, the prediction of disease-free survival rate after lung cancer surgery by TNM staging is not accu...
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IPC IPC(8): G16H50/70G16B20/50G06N20/00A61B5/00
CPCG16H50/70G16B20/50G06N20/00A61B5/7275
Inventor 何建行梁文华李坚福
Owner BIOISLAND LAB