Feasibility study of radiomics-based radiotherapy planning characteristics to predict the complexity of intensity-modulated radiotherapy plans
10.3969/j.issn.1672-8270.2024.11.003
- VernacularTitle:基于影像组学的放射治疗计划特征预测调强放疗计划复杂度的可行性研究
- Author:
Hualing LI
1
;
Caihong LI
;
Peipei WANG
;
Jinkai LI
;
Xinchen SUN
Author Information
1. 南京医科大学第一附属医院放疗科 南京 210029
- Keywords:
Planomics;
Plan complexity;
Machine learning;
Quality assurance;
Python software
- From:
China Medical Equipment
2024;21(11):12-17
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the feasibility of predicting complexity of intensity modulated radiotherapy(IMRT)plan through adopted machine learning method to extract planomics features of radiotherapy,so as to provide a new method for comprehensive evaluation of the complexity of IMRT plan.Methods:The medical case data of 3203 patients with pelvic tumor,or abdominal tumor or head and neck tumor,who admitted to The First Affiliated Hospital with Nanjing Medical University from December 2022 to November 2023,were selected.All patients adopted Monaco system to conduct design for plan,and underwent treatment on Precise and Axesse accelerators.The evaluation indicator of complexity of 10 plans was calculated by using Python software,and the planomics features in the files of radiotherapy plans were extracted through format conversion and pyradiomics tool of imaging omics.The planomics features of radiotherapy were selected through data cleaning,filtering method and embedding method of machine learning.The corresponding predictive model of the evaluation indicator of complexity of 10 common plans was respectively constructed through used Gradient Boosting Decision Tree algorithm.The goodness of fit(R2)was adopted to evaluate the prediction performance of the model,and the 5-fold cross-validation method was adopted to detect the generalization ability of the model.Results:There were statistically significant differences between Precise accelerator and Axesse accelerator in average leaf to area(LA),plan irregularity(PI)of beam shape and standard circle,modulation complexity score(MCS)of the variability between shape and area of subfield,and the advantage value of leaf travel(LT)(t=63.894,-63.678,72.582,-48.858,P＜0.01),respectively.A total of 107 planomics features were extracted through pyradiomics tool,and 38 features were remained after filtering method conducted screening,and 4 to 11 features were remained after embedding method conducted screening.The goodness of fits of mean field area(MFA),LA and leaf gap average(LGA)value were better in the validation set,with R2＞0.970,however the goodness of fits of the proportion of small aperture score 20 mm(SAS20)was poor in validation set,with R2=0.917.The 5-fold cross-validation results showed that the average value of prediction accuracy of all indicators of complexity was＞90％.Conclusions:The extracted planomics features of radiotherapy based on radiomics method can accurately predict the complexity of IMRT plan,which are expected to play a greater role in improving the ensure efficiency of individual quality of patient,and screening radiotherapy plan with higher-quality.
