Abstract：
OBJECTIVE: To evaluate the feasibility and accuracy of radiomics, dosiomics, and deep learning (DL) in predicting Radiation Pneumonitis (RP) in lung cancer patients underwent volumetric modulated arc therapy (VMAT) to improve radiotherapy safety and management.
METHODS: Total of 318 and 31 lung cancer patients underwent VMAT from First Affiliated Hospital of Wenzhou Medical University (WMU) and Quzhou Affiliated Hospital of WMU were enrolled for training and external validation, respectively. Models based on radiomics (R), dosiomics (D), and combined radiomics and dosiomics features (R+D) were constructed and validated using three machine learning (ML) methods. DL models trained with CT (DLR), dose distribution (DLD), and combined CT and dose distribution (DL(R+D)) images were constructed. DL features were then extracted from the fully connected layers of the best-performing DL model to combine with features of the ML model with the best performance to construct models of R+DLR, D+DLD, R+D+DL(R+D)) for RP prediction.
RESULTS: The R+D model achieved a best area under curve (AUC) of 0.84, 0.73, and 0.73 in the internal validation cohorts with Support Vector Machine (SVM), XGBoost, and Logistic Regression (LR), respectively. The DL(R+D) model achieved a best AUC of 0.89 and 0.86 using ResNet-34 in training and internal validation cohorts, respectively. The R+D+DL(R+D) model achieved a best performance in the external validation cohorts with an AUC, accuracy, sensitivity, and specificity of 0.81(0.62-0.99), 0.81, 0.84, and 0.67, respectively.
CONCLUSIONS: The integration of radiomics, dosiomics, and DL features is feasible and accurate for the RP prediction to improve the management of lung cancer patients underwent VMAT.
METHODS: Total of 318 and 31 lung cancer patients underwent VMAT from First Affiliated Hospital of Wenzhou Medical University (WMU) and Quzhou Affiliated Hospital of WMU were enrolled for training and external validation, respectively. Models based on radiomics (R), dosiomics (D), and combined radiomics and dosiomics features (R+D) were constructed and validated using three machine learning (ML) methods. DL models trained with CT (DLR), dose distribution (DLD), and combined CT and dose distribution (DL(R+D)) images were constructed. DL features were then extracted from the fully connected layers of the best-performing DL model to combine with features of the ML model with the best performance to construct models of R+DLR, D+DLD, R+D+DL(R+D)) for RP prediction.
RESULTS: The R+D model achieved a best area under curve (AUC) of 0.84, 0.73, and 0.73 in the internal validation cohorts with Support Vector Machine (SVM), XGBoost, and Logistic Regression (LR), respectively. The DL(R+D) model achieved a best AUC of 0.89 and 0.86 using ResNet-34 in training and internal validation cohorts, respectively. The R+D+DL(R+D) model achieved a best performance in the external validation cohorts with an AUC, accuracy, sensitivity, and specificity of 0.81(0.62-0.99), 0.81, 0.84, and 0.67, respectively.
CONCLUSIONS: The integration of radiomics, dosiomics, and DL features is feasible and accurate for the RP prediction to improve the management of lung cancer patients underwent VMAT.
摘要：
目的：为了评估影像组学的可行性和准确性，dosiomics,和深度学习（DL）预测肺癌患者接受体积调强电弧治疗（VMAT）的放射性肺炎（RP），以提高放疗安全性和管理。
方法：温州医科大学附属第一医院（WMU）和WMU衢州附属医院共318例和31例接受VMAT的肺癌患者进行培训和外部验证。分别。基于影像组学(R)的模型，dosiomics（D），并使用三种机器学习（ML）方法构建和验证了影像组学和剂量组学的组合特征（RD）。用CT(DLR)训练的DL模型，剂量分布（DLD），并构建CT和剂量分布（DL（RD））图像。然后从性能最佳的DL模型的完全连接层中提取DL特征，以与具有最佳性能的ML模型的特征相结合，以构建RDLR的模型。D+DLD,用于RP预测的R+D+DL(R+D))。
结果：在使用支持向量机（SVM）的内部验证队列中，RD模型获得了0.84、0.73和0.73的最佳曲线下面积（AUC），XGBoost,和Logistic回归(LR)，分别。DL(R+D)模型在训练和内部验证队列中使用ResNet-34实现了0.89和0.86的最佳AUC，分别。R+D+DL(R+D)模型在AUC的外部验证队列中取得了最佳性能，准确度,灵敏度,特异性为0.81(0.62-0.99)，分别为0.81、0.84和0.67。
结论：影像组学的整合，dosiomics,和DL特征对于RP预测是可行和准确的，以改善肺癌患者行VMAT的管理。
方法：温州医科大学附属第一医院（WMU）和WMU衢州附属医院共318例和31例接受VMAT的肺癌患者进行培训和外部验证。分别。基于影像组学(R)的模型，dosiomics（D），并使用三种机器学习（ML）方法构建和验证了影像组学和剂量组学的组合特征（RD）。用CT(DLR)训练的DL模型，剂量分布（DLD），并构建CT和剂量分布（DL（RD））图像。然后从性能最佳的DL模型的完全连接层中提取DL特征，以与具有最佳性能的ML模型的特征相结合，以构建RDLR的模型。D+DLD,用于RP预测的R+D+DL(R+D))。
结果：在使用支持向量机（SVM）的内部验证队列中，RD模型获得了0.84、0.73和0.73的最佳曲线下面积（AUC），XGBoost,和Logistic回归(LR)，分别。DL(R+D)模型在训练和内部验证队列中使用ResNet-34实现了0.89和0.86的最佳AUC，分别。R+D+DL(R+D)模型在AUC的外部验证队列中取得了最佳性能，准确度,灵敏度,特异性为0.81(0.62-0.99)，分别为0.81、0.84和0.67。
结论：影像组学的整合，dosiomics,和DL特征对于RP预测是可行和准确的，以改善肺癌患者行VMAT的管理。
