| 陈路桥,倪千喜,李啸洲,曹锦佳.基于放射组学的机器学习预测盆腔调强放疗剂量验证的γ通过率[J].中华放射医学与防护杂志,2023,43(2):101-105 |
| 基于放射组学的机器学习预测盆腔调强放疗剂量验证的γ通过率 |
| Prediction of radiomics-based machine learning in dose verification of intensity-modulated pelvic radiotherapy |
| 投稿时间：2022-10-21 |
| DOI：10.3760/cma.j.cn112271-20221021-00416 |
| 中文关键词: 机器学习 调强放疗 放射组学 盆腔 γ通过率 |
| 英文关键词:Machine learning Intensity-modulated radiotherapy Radiomics Plevic Gamma pass rate |
| 基金项目:湖南省科技创新计划资助项目（2021SK51116）；湖南省卫生健康委科研计划项目（202109031926）；南华大学研究生教改项目（213YXJ032） |
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| 中文摘要: |
| 目的 利用放射组学特征构建不同的机器学习分类模型，预测盆腔肿瘤调强放疗剂量验证的γ通过率，并探讨最佳预测模型。方法 回顾性分析196例盆腔肿瘤调强放疗计划，采用基于模体测量方式的三维剂量验证结果，γ通过率标准为3%/2 mm、10%剂量阈值。提取基于剂量文件的放射组学特征构建预测模型。分别采用随机森林、支持向量机、自适应增强和梯度提升决策树4种机器学习算法，计算曲线下面积（AUC）值、敏感度和特异度，评估4种预测模型的分类性能。结果 随机森林、支持向量机、自适应增强、梯度提升决策树模型的灵敏度和特异度分别为0.93、0.85，0.93、0.96，0.38、0.69，0.46、0.46。随机森林模型和自适应增强模型的AUC值分别为0.81和0.82，支持向量机和梯度提升决策树模型的AUC值为0.87。结论 针对盆腔肿瘤调强放疗计划，可以采用基于放射组学特征的机器学习方法来构建γ通过率的预测模型。支持向量机模型和梯度提升决策树模型的分类性能要优于随机森林模型、自适应增强模型。 |
| 英文摘要: |
| Objective Based on radiomics characteristics, different machine learning classification models are constructed to predict the gamma pass rate of dose verification in intensity-modulated radiotherapy for pelvic tumors, and to explore the best prediction model.Methods The results of three-dimensional dose verification based on phantom measurement were retrospectively analyzed in 196 patients with pelvic tumor intensity-modulated radiotherapy plans. The gamma pass rate standard was 3%/2mm and 10% dose threshold. Prediction models were constructed by extracting radiomic features based on dose documentation. Four machine learning algorithms, random forest, support vector machine, adaptive boosting,and gradient boosting decision tree were used to calculate the AUC value, sensitivity, and specificity respectively. The classification performance of the four prediction models was evaluated.Results The sensitivity and specificity of the random forest, support vector machine, adaptive boosting, and gradient boosting decision tree models were 0.93, 0.85, 0.93, 0.96, 0.38, 0.69, 0.46, and 0.46, respectively. The AUC values were 0.81 and 0.82 for the random forest and adaptive boosting models, respectively, and 0.87 for the support vector machine and gradient boosting decision tree models.Conclusions Machine learning method based on radiomics can be used to construct a prediction model of gamma pass rate for specific dosimetric verification of pelvic intensity-modulated radiotherapy. The classification performance of the support vector machine model and gradient boosting decision tree model is better than that of the random forest model and adaptive boosting model. |
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