| Chen Luqiao,Ni Qianxi,Wu Yu,et al.A multicenter study on the prediction of gamma passing rate based on radiomic features[J].Chinese Journal of Radiological Medicine and Protection,2024,44(12):1027-1033 |
| A multicenter study on the prediction of gamma passing rate based on radiomic features |
| Received:December 11, 2023 |
| DOI：10.3760/cma.j.cn112271-20231211-00205 |
| KeyWords:Machine learning Volumetric-modulated arc therapy Radiomics Gamma passing rate Multicenter |
| FundProject:湖南省自然科学基金(2023JJ30373);湖南省科技创新计划资助项目(2021SK51116,2023SK4034);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 | | Chen Luqiao | Department of Radiation Therapy, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China | | | Ni Qianxi | Department of Radiation Therapy, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China | niqianxi2014@163.com | | Wu Yu | Department of Oncology, Yueyang Central Hospital, Yueyang 414020, China | | | Ren Huan | Department of Oncology, The First People's Hospital of Changde City, Changde 415000, China | | | Pang Jinmeng | Department of Radiation Therapy, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China | | | Tan Jianfeng | Department of Radiation Therapy, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China | | | Luo Longjun | Department of Radiation Therapy, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China | | | Wu Zhili | Department of Radiation Therapy, Hunan Cancer Hospital and 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 | |
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| Abstract:: |
| Objective To construct classification prediction models for gamma passing rate using radiomics-based machine learning approaches and data from multiple radiotherapy institutions and evaluate the models' performance. Methods The data from 572 volumetric-modulated arc therapy (VMAT) patients across three radiotherapy institutions (514 for training and 58 for testing) were retrospectively collected. Additionally, 45 VMAT plans were collected from a single institution as an independent external validation set. For all the data, a three-dimensional dose validation approach based on actual measurements of phantoms was utilized, and gamma analysis was performed at the 3%/2 mm criterion using a dose threshold of 10%, absolute doses, and global normalization. After radiomic features were extracted from dose files, feature selection was performed using the random forest (RF) method and RF combined with Shapley Additive exPlanation (SHAP). Then, feature subsets of varying sizes (10, 20, 30, 40, and 50) were selected based on feature rankings. Using these subsets as inputs, data training was conducted using the Extreme Gradient Boosting (XGBoost) algorithm. Finally, the models' classification performance was assessed using the area under the curve (AUC) values and F1-score. Results Under the 3%/2 mm criterion, all models performed the best in the case of 20 feature subsets. The optimal prediction model established based on the feature selection using RF exhibited AUC and F1-score of 0.88 and 0.89, respectively on the testing set and 0.82 and 0.90, respectively, on the validation set. The optimal prediction model built based on the feature selection using RF combined with SHAP yielded AUC and F1-score of 0.86 and 0.92 on the testing set and 0.87 and 0.89, respectively, on the validation set, along with superior robustness. Therefore, the second model possessed certain advantages over the first model. Conclusions For multicenter dose verification result, it is feasible to construct a machine learning prediction model with high classification performance using radiomic features derived from dose files, combined with feature selection based on SHAP. This approach can assist in advancing the clinical applications and implementation of gamma passing rate prediction models. |
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