| Ni Qianxi,Du Yangfeng,Zhu Zhaozhong,Pang Jinmeng,Tan Jianfeng,Wu Zhili,Cao Jinjia,Chen Luqiao.Radiomics-based prediction of gamma pass rates for different intensity-modulated radiation therapy techniques for pelvic tumors[J].Chinese Journal of Radiological Medicine and Protection,2023,43(8):595-600 |
| Radiomics-based prediction of gamma pass rates for different intensity-modulated radiation therapy techniques for pelvic tumors |
| Received:March 14, 2023 |
| DOI：10.3760/cma.j.cn112271-20230314-00073 |
| KeyWords:Pelvic tumor Intensity-modulated radiation therapy technique Radiomics Gamma pass rate Dose validation |
| FundProject:湖南省自然科学基金面上项目（2023JJ30373）；湖南省卫生健康委适宜技术推广项目（202218015767）；湖南省肿瘤医院攀登科研计划重点研发项目（YF2021006） |
| Author Name | Affiliation | E-mail | | Ni Qianxi | Department of Radiation Oncology, Hunan Cancer Hospital/the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China | | | Du Yangfeng | Department of Oncology, The First People's Hospital of Changde City, Changde 415000, China | | | Zhu Zhaozhong | School of Public Health, University of South China, Hengyang 421001, China | | | Pang Jinmeng | Department of Radiation Oncology, Hunan Cancer Hospital/the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China | | | Tan Jianfeng | Department of Radiation Oncology, Hunan Cancer Hospital/the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China | | | Wu Zhili | Department of Radiation Oncology, Hunan Cancer Hospital/the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China | | | Cao Jinjia | School of Nuclear Science and Technology, University of South China, Hengyang 421001, China | | | Chen Luqiao | Department of Radiation Oncology, Hunan Cancer Hospital/the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China | 1462113671@qq.com |
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| Abstract:: |
| Objective To explore the feasibility of a classification prediction model for gamma pass rates (GPRs) under different intensity-modulated radiation therapy techniques for pelvic tumors using a radiomics-based machine learning approach, and compare the classification performance of four integrated tree models.Methods With a retrospective collection of 409 plans using different IMRT techniques, the three-dimensional dose validation results were adopted based on modality measurements, with a GPR criterion of 3%/2 mm and 10% dose threshold. Then prediction were built models by extracting radiomics features based on dose documentation. Four machine learning algorithms were used, namely random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). Their classification performance was evaluated by calculating sensitivity, specificity, F1 score, and AUC value.Results The RF, AdaBoost, XGBoost, and LightGBM models had sensitivities of 0.96, 0.82, 0.93, and 0.89, specificities of 0.38, 0.54, 0.62, and 0.62, F1 scores of 0.86, 0.81, 0.88, and 0.86, and AUC values of 0.81, 0.77, 0.85, and 0.83, respectively. XGBoost model showed the highest sensitivity, specificity, F1 score, and AUC value, outperforming the other three models.Conclusions To build a GPR classification prediction model using a radiomics-based machine learning approach is feasible for plans using different intensity-modulated radiotherapy techniques for pelvic tumors, providing a basis for future multi-institutional collaborative research on GPR prediction. |
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