Feasibility of machine learning in OAR dosimetric prediction in VMAT plan for lung cancer(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2020年第7期
- Page:
- 934-939
- Research Field:
- 医学人工智能
- Publishing date:
Info
- Title:
- Feasibility of machine learning in OAR dosimetric prediction in VMAT plan for lung cancer
- Author(s):
- YAN Feng1; NIU Zhenyang1; FEI Zhenle1; WU Xianxiang2; CUI Xiangli3; LIU Lingling3
- 1. Department of Radiation Oncology, No.901 Hospital of PLA, Hefei 230031, China 2. Department of Radiation Oncology, the First Affiliated Hospital of Bengbu Medical College, Bengbu 233004, China 3. Cancer Hospital, Chinese Academy of Sciences, Hefei 230031, China
- Keywords:
- Keywords: lung cancer volumetric modulated arc therapy artificial neural network model machine learning dose-volume histogram
- PACS:
- R815.6；R318
- DOI:
- DOI:10.3969/j.issn.1005-202X.2020.07.025
- Abstract:
- Abstract: Objective To investigate the feasibility of machine learning for dose-volume histogram (DVH) predictions of the heart and the lungs in volumetric modulated arc therapy (VMAT) plan for lung cancer. Methods Among the VMAT plans of 51 cases of lung cancer, 43 VMAT plans were randomly selected as training group, and the other 8 plans were taken as validation group. The anatomical information of patients in training group was analyzed, and the relationships between the V5, V20 of bilateral lungs and the V30, V40 of the heart were investigated. With the anatomical information as the input and the DVH of organs-at-risk (OAR) as the output, machine learning method was adopted to construct and train the artificial neural network models for bilateral lungs and the heart, separately. The anatomical information of 8 VMAT plans in validation group was input into the constructed artificial neural network model for predicting the DVH of OAR. Results The V5, V20 of bilateral lungs and the V30, V40 of the heart were affected by the relative spatial relationship between OAR and target areas, but didnt affected by the volume of OAR itself. In the artificial neural network structure models of the affected lung, the contralateral lung and the heart, the hidden layers contained 41, 38 and 34 neural nodes, respectively, and the linear regression coefficients were 0.994, 0.975 and 0.986, respectively. In validation group, the prediction errors for the V5, V20 of the affected lung were 2.70%±1.83% and 2.84%±1.97%, and those for the V5, V20 of the contralateral lung were 13.7%±7.8% and 0.72%±0.75% and the prediction errors for the V30 and V40 of the heart were 3.20%±0.63% and 2.1%±1.5%, respectively. There was statistically significant difference between the predicted and actual values of the V5 of the contralateral lung. Conclusion Artificial neural network method can learn the anatomical information in the lung cancer VMAT plan and the DVH data of OAR. The constructed artificial neural network model can be used to accurately predict the DVH of the affected lung, the V25-V60 of the heart and the V20 of the contralateral lung, providing reference for clinical treatment planning.
Last Update: 2020-07-28