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A machine learning-based method for automatic design of radiotherapy plan for nasopharyngeal carcinoma

A technology of radiotherapy and machine learning for nasopharyngeal carcinoma, applied in machine learning, computer-aided medical procedures, instruments, etc., to achieve the effects of reducing time burden, fast and simple search, and increasing generation speed

Active Publication Date: 2022-02-01
福建省肿瘤医院
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Current methods are not yet capable of meeting such requirements

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  • A machine learning-based method for automatic design of radiotherapy plan for nasopharyngeal carcinoma
  • A machine learning-based method for automatic design of radiotherapy plan for nasopharyngeal carcinoma
  • A machine learning-based method for automatic design of radiotherapy plan for nasopharyngeal carcinoma

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Embodiment Construction

[0048] The technical solution of the present invention will be specifically described below in conjunction with the accompanying drawings.

[0049] The present invention provides a method for generating an automatic plan for nasopharyngeal carcinoma based on machine learning, which is implemented according to the following steps:

[0050] Step S1: Establish a case template database, and determine the objective function corresponding to the case for dose-volume histogram data acquisition;

[0051] Step S2: Obtain the organ overlap volume histogram (OverlapVolume Histogram, OVH) of the case in the case template database, and store it in the case template database;

[0052] In this example, if figure 2 shown, for the OVH data and the resulting image, as image 3 Shown is the histogram of organ overlapping volumes.

[0053] Step S3: Acquiring the organ overlapping volume histogram of the patient case;

[0054] Step S4: if Figure 4 As shown, based on the structural similarit...

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Abstract

The present invention relates to a method for automatically designing radiotherapy plans for nasopharyngeal carcinoma based on machine learning, which provides data based on an organ volume histogram (Overlap Volume Histogram, OVH) and combines the nearest neighbor method of machine learning to search for the best objective function for patients Methods. Use the existing data to build a template library, use the machine learning nearest neighbor method to search for the most similar cases between the OVH data of newly enrolled patients and the template library, and use the objective function of the template cases found to search for newly enrolled cases radiotherapy plan design. In the case of no need for strong human subjective factors, the case template is searched, and the patient's nasopharyngeal carcinoma radiotherapy plan is automatically designed.

Description

technical field [0001] The invention relates to the fields of computer graphics and machine learning, in particular to a machine learning-based automatic design method for radiotherapy plans for nasopharyngeal carcinoma. Background technique [0002] Radiation therapy is one of the three main treatments for malignant tumors, and about 60% to 70% of malignant tumor patients need to receive radiation therapy. The purpose of radiotherapy is to increase the gain ratio of radiotherapy, that is, to maximize the concentration of radiation dose into the tumor, so that the surrounding normal tissues are less or protected from unnecessary irradiation. Nasopharyngeal carcinoma is a high-incidence head and neck tumor in southern China and Asian Chinese. Radiation therapy is currently the main treatment for nasopharyngeal carcinoma. The intensity modulated radiation therapy (IMRT) technique is suitable for the treatment of nasopharyngeal carcinoma because: (1) there are many important n...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16H30/20G16H50/70G06N20/00
CPCG06N20/00G16H30/20G16H50/70
Inventor 柏朋刚陈济鸿戴艺涛陈传本翁星
Owner 福建省肿瘤医院
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