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Abstract：
OBJECTIVE: This study explores integrating clinical features with radiomic and dosiomic characteristics into AI models to enhance the prediction accuracy of radiation dermatitis (RD) in breast cancer patients undergoing volumetric modulated arc therapy (VMAT).
METHODS: This study involved a retrospective analysis of 120 breast cancer patients treated with VMAT at Kaohsiung Veterans General Hospital from 2018 to 2023. Patient data included CT images, radiation doses, Dose-Volume Histogram (DVH) data, and clinical information. Using a Treatment Planning System (TPS), we segmented CT images into Regions of Interest (ROIs) to extract radiomic and dosiomic features, focusing on intensity, shape, texture, and dose distribution characteristics. Features significantly associated with the development of RD were identified using ANOVA and LASSO regression (p-value < 0.05). These features were then employed to train and evaluate Logistic Regression (LR) and Random Forest (RF) models, using tenfold cross-validation to ensure robust assessment of model efficacy.
RESULTS: In this study, 102 out of 120 VMAT-treated breast cancer patients were included in the detailed analysis. Thirty-two percent of these patients developed Grade 2+ RD. Age and BMI were identified as significant clinical predictors. Through feature selection, we narrowed down the vast pool of radiomic and dosiomic data to 689 features, distributed across 10 feature subsets for model construction. In the LR model, the J subset, comprising DVH, Radiomics, and Dosiomics features, demonstrated the highest predictive performance with an AUC of 0.82. The RF model showed that subset I, which includes clinical, radiomic, and dosiomic features, achieved the best predictive accuracy with an AUC of 0.83. These results emphasize that integrating radiomic and dosiomic features significantly enhances the prediction of Grade 2+ RD.
CONCLUSIONS: Integrating clinical, radiomic, and dosiomic characteristics into AI models significantly improves the prediction of Grade 2+ RD risk in breast cancer patients post-VMAT. The RF model analysis demonstrates that a comprehensive feature set maximizes predictive efficacy, marking a promising step towards utilizing AI in radiation therapy risk assessment and enhancing patient care outcomes.
METHODS: This study involved a retrospective analysis of 120 breast cancer patients treated with VMAT at Kaohsiung Veterans General Hospital from 2018 to 2023. Patient data included CT images, radiation doses, Dose-Volume Histogram (DVH) data, and clinical information. Using a Treatment Planning System (TPS), we segmented CT images into Regions of Interest (ROIs) to extract radiomic and dosiomic features, focusing on intensity, shape, texture, and dose distribution characteristics. Features significantly associated with the development of RD were identified using ANOVA and LASSO regression (p-value < 0.05). These features were then employed to train and evaluate Logistic Regression (LR) and Random Forest (RF) models, using tenfold cross-validation to ensure robust assessment of model efficacy.
RESULTS: In this study, 102 out of 120 VMAT-treated breast cancer patients were included in the detailed analysis. Thirty-two percent of these patients developed Grade 2+ RD. Age and BMI were identified as significant clinical predictors. Through feature selection, we narrowed down the vast pool of radiomic and dosiomic data to 689 features, distributed across 10 feature subsets for model construction. In the LR model, the J subset, comprising DVH, Radiomics, and Dosiomics features, demonstrated the highest predictive performance with an AUC of 0.82. The RF model showed that subset I, which includes clinical, radiomic, and dosiomic features, achieved the best predictive accuracy with an AUC of 0.83. These results emphasize that integrating radiomic and dosiomic features significantly enhances the prediction of Grade 2+ RD.
CONCLUSIONS: Integrating clinical, radiomic, and dosiomic characteristics into AI models significantly improves the prediction of Grade 2+ RD risk in breast cancer patients post-VMAT. The RF model analysis demonstrates that a comprehensive feature set maximizes predictive efficacy, marking a promising step towards utilizing AI in radiation therapy risk assessment and enhancing patient care outcomes.
摘要：
目的：本研究探索将临床特征与影像组学和剂量组学特征整合到AI模型中，以提高接受体积调节电弧治疗（VMAT）的乳腺癌患者放射性皮炎（RD）的预测准确性。
方法：本研究涉及2018年至2023年在高雄退伍军人总医院接受VMAT治疗的120例乳腺癌患者的回顾性分析。患者数据包括CT图像，辐射剂量,剂量-体积直方图(DVH)数据,和临床信息。使用治疗计划系统（TPS），我们将CT图像分割为感兴趣区域(ROI)来提取放射组学和剂量组学特征，专注于强度，形状,纹理,和剂量分布特征。使用ANOVA和LASSO回归(p值<0.05)鉴定与RD发展显著相关的特征。然后将这些特征用于训练和评估Logistic回归(LR)和随机森林(RF)模型，使用十倍交叉验证，以确保模型疗效的稳健评估。
结果：在这项研究中，120例接受VMAT治疗的乳腺癌患者中有102例纳入了详细分析。这些患者中有32%发展为2级+RD。年龄和BMI被确定为重要的临床预测因子。通过特征选择，我们将大量的放射学和剂量学数据缩小到689个特征，分布在10个特征子集上，用于模型构建。在LR模型中，J子集，包括DVH,Radiomics,和Dosiomics功能，表现出最高的预测性能，AUC为0.82。RF模型显示子集I，其中包括临床，放射学,和剂量学特征，在AUC为0.83的情况下取得了最好的预测准确性。这些结果强调，整合影像组学和剂量组学特征可显着增强2级RD的预测。
结论：整合临床，放射学,和AI模型的剂量组学特征显着提高了VMAT后乳腺癌患者2级RD风险的预测。射频模型分析表明，全面的特征集可最大限度地提高预测效果，标志着在放射治疗风险评估中利用人工智能和提高患者护理结果方面迈出了有希望的一步。
方法：本研究涉及2018年至2023年在高雄退伍军人总医院接受VMAT治疗的120例乳腺癌患者的回顾性分析。患者数据包括CT图像，辐射剂量,剂量-体积直方图(DVH)数据,和临床信息。使用治疗计划系统（TPS），我们将CT图像分割为感兴趣区域(ROI)来提取放射组学和剂量组学特征，专注于强度，形状,纹理,和剂量分布特征。使用ANOVA和LASSO回归(p值<0.05)鉴定与RD发展显著相关的特征。然后将这些特征用于训练和评估Logistic回归(LR)和随机森林(RF)模型，使用十倍交叉验证，以确保模型疗效的稳健评估。
结果：在这项研究中，120例接受VMAT治疗的乳腺癌患者中有102例纳入了详细分析。这些患者中有32%发展为2级+RD。年龄和BMI被确定为重要的临床预测因子。通过特征选择，我们将大量的放射学和剂量学数据缩小到689个特征，分布在10个特征子集上，用于模型构建。在LR模型中，J子集，包括DVH,Radiomics,和Dosiomics功能，表现出最高的预测性能，AUC为0.82。RF模型显示子集I，其中包括临床，放射学,和剂量学特征，在AUC为0.83的情况下取得了最好的预测准确性。这些结果强调，整合影像组学和剂量组学特征可显着增强2级RD的预测。
结论：整合临床，放射学,和AI模型的剂量组学特征显着提高了VMAT后乳腺癌患者2级RD风险的预测。射频模型分析表明，全面的特征集可最大限度地提高预测效果，标志着在放射治疗风险评估中利用人工智能和提高患者护理结果方面迈出了有希望的一步。
