早期非小细胞肺癌立体定向放疗肺剂量预测研究

doi:10.3760/cma.j.issn.1004-4221.2020.02.006

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Abstract Objective To study a lung dose prediction method for the early stage non-small cell lung cancer (NSCLC) treated with stereotactic body radiotherapy based on machine learning algorithm,and to evaluate the feasibility of application in planning quality assurance. Methods A machine learning algorithm was utilized to achieve DVH prediction. First, an expert plan dataset with 125 cases was built,and the geometric features of ROI,beam angle anddose-volume histogram(DVH) parameters in the dataset were extracted. Following a correlation model was established between the features and DVHs. Second,the geometric and beam features from 10 cases outside the training pool were extracted,and the model was adopted to predict the achievable DVHs values of the lung. The predicted DVHs values were compared with the actual planned results. Results The mean squared errors of external validation for the 10 cases inmean lung dose (MLD)MLD and V20 of the lung were 91.95 cGy and 3.12%,respectively. Two cases whose lungdoseswere higher than the predicted values were re-planned,and the results showed that the the lung doses were reduced. Conclusion It is feasible to utilize the anatomy and beam angle features to predict the lung DVH parameters for plan evaluation and quality assurance in early stage NSCLC patients treated with stereotactic body radiotherapy

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	Articles by authors
	Bai Xue
	Wang Binbing
	Shao Kainan
	Yang Yiwei
	Shan Guoping
	Chen Ming

Key words： Lung neoplasm/stereotactic body radiotherapy Machine learning Dose volume histogram

Received: 03 July 2018

Fund:The National Key Research and Development Projects (2017YFC0113201);The Medical and Health Science and Technology Projects in Zhejiang Province (2017PY013, 2018PY005)

Corresponding Authors: Wang Binbing,Email:wangbb@zjcc.org.cn

Cite this article:

Bai Xue,Wang Binbing,Shao Kainan et al. A study of prediction model of lung dose in early stage non-small cell lung cancer with stereotactic body radiotherapy[J]. Chinese Journal of Radiation Oncology, 2020, 29(2): 106-110.

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http://journal12.magtechjournal.com/Jweb_fszlx/EN/10.3760/cma.j.issn.1004-4221.2020.02.006 OR http://journal12.magtechjournal.com/Jweb_fszlx/EN/Y2020/V29/I2/106

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