| 倪千喜,杜阳峰,朱兆中,等.基于放射组学的盆腔肿瘤不同调强放疗技术γ通过率的预测研究[J].中华放射医学与防护杂志,2023,43(8):595-600.Ni Qianxi,Du Yangfeng,Zhu Zhaozhong,et al.Radiomics-based prediction of gamma pass rates for different intensity-modulated radiation therapy techniques for pelvic tumors[J].Chin J Radiol Med Prot,2023,43(8):595-600 |
| 基于放射组学的盆腔肿瘤不同调强放疗技术γ通过率的预测研究 |
| Radiomics-based prediction of gamma pass rates for different intensity-modulated radiation therapy techniques for pelvic tumors |
| 投稿时间：2023-03-14 |
| DOI：10.3760/cma.j.cn112271-20230314-00073 |
| 中文关键词: 盆腔肿瘤 调强放疗技术 放射组学 γ通过率 剂量验证 |
| 英文关键词:Pelvic tumor Intensity-modulated radiation therapy technique Radiomics Gamma pass rate Dose validation |
| 基金项目: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） |
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| 中文摘要: |
| 目的 采用基于放射组学的机器学习方法，探索盆腔肿瘤不同调强放疗技术下γ通过率（GPR）分类预测模型的可行性，并比较了4种集成树模型的分类性能。方法 回顾性收集了409例使用不同调强放疗技术的计划，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、自适应增强、极端梯度提升树和轻量级梯度提升机4种机器学习算法，并且通过计算灵敏度、特异度、F1分数及曲线下面积（AUC）值来评估它们的分类性能。结果 随机森林、自适应增强、极端梯度提升树、轻量级梯度提升机模型的灵敏度和特异度分别为0.96、0.82、0.93、0.89和0.38、0.54、0.62、0.62，F1分数和AUC值分别为0.86、0.81、0.88、0.86和0.81、0.77、0.85、0.83。其中极端梯度提升树模型的灵敏度达到0.93，特异度、F1分数和AUC值均为最高，要优于其他3种模型。结论 针对采用不同调强放疗技术的盆腔肿瘤调强计划，使用基于放射组学的机器学习方法来构建伽马通过率分类预测模型具有一定的可行性，能够为将来GPR预测的多机构合作研究提供基础。 |
| 英文摘要: |
| 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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