Radiotherapy plan assessment method and device based on unsupervised learning

Abstract

The invention discloses a radiotherapy plan assessment method and device based on unsupervised learning, and the method comprises the following steps: S1, obtaining a corresponding anatomical structure vector according to the data of n patients for any disease; s2, performing dimensionality reduction on the high-dimensional anatomical structure vector by using a dimensionality reduction algorithm; s3, clustering the data according to an anatomical structure by using a clustering algorithm to obtain k categories; s4, according to the k categories, analyzing and processing a clinically implemented high-quality radiotherapy plan result in the data corresponding to each category to obtain a plan scoring template corresponding to each category; s5, new data of any patient is calculated, and the category of the new data is judged; and S6, adopting the plan scoring template of the corresponding category to guide the design of the radiotherapy plan corresponding to the new data, and scoring the dose distribution result of the radiotherapy plan. According to the method, personalized evaluation of cases with different geometric structure complexity is realized, and the quality and efficiency of the radiotherapy plan are improved.

Description

technical field [0001] The invention belongs to the technical field of radiotherapy, and in particular relates to a radiotherapy plan evaluation method and device based on unsupervised learning. Background technique [0002] Tumor radiotherapy has become one of the main methods of tumor treatment due to its unique advantages. Its main goal is to protect the surrounding normal tissue as much as possible while ensuring that the target area reaches a specific dose. In clinical applications, professional doctors and physicists are required to design a radiotherapy plan before radiotherapy is implemented. The design and production of radiotherapy plan can be divided into three steps: Planning Target Volume (PTV) and Organs at Risk (OARs) delineation, plan design and plan evaluation. [0003] How to evaluate the plan effectively and objectively is a question worth exploring. [0004] Dose Volume Histogram (DVH) has become an important tool for designing, formulating and evaluati...

AI Technical Summary

Problems solved by technology

[0007] First, the existing evaluation methods of radiotherapy plans refer to the existing academic consensus and guidelines, and more senior and authoritative institutions may customize a set of modified evaluation criteria on this basis

This method has a certain degree of subjectivity, which will lead to unstable planning quality

[0008] Second, if the clinical plans are all subject to uniform normative standards, it will be difficult to achieve the clinical goals for cases with complex geometric structures, but it is easy to achieve the clinical goals for cases with simple geometric structures. Stop further optimization, may get sub-optimal quality radiotherapy plan

[0009] Third, the existing professional software for radiotherapy plan evaluation simulates and calculates dose attenuation based on the geometric relationship between organs at risk and target volumes, thereby quantitatively calculating the exposure dose. When measuring, only the physical properties of radiation in human tissues are considered, and the performance of linear accelerators and gratings, as well as the mutual constraints of other normal organs at risk, etc. are not considered, so the predicted dose to the target area and organs at risk is generally the most ideal result. , does not fit well with the actual situation

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