Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Jiangong RdSanmin Dist., No.415, Kaohsiung, 80778, Taiwan, ROC.
Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan, ROC.
BMC Cancer. 2024 Aug 6;24(1):965. doi: 10.1186/s12885-024-12753-1.
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).
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.
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.
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.
本研究旨在将临床特征与放射组学和剂量组学特征相结合，纳入人工智能模型，以提高接受容积调强弧形治疗（VMAT）的乳腺癌患者放射性皮炎（RD）预测的准确性。
本研究回顾性分析了 2018 年至 2023 年在高雄荣民总医院接受 VMAT 治疗的 120 例乳腺癌患者。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。我们使用治疗计划系统（TPS）将 CT 图像分割成感兴趣区域（ROI），以提取放射组学和剂量组学特征，重点关注强度、形状、纹理和剂量分布特征。使用方差分析（ANOVA）和套索回归（LASSO regression）（p 值 < 0.05）确定与 RD 发生显著相关的特征。然后，我们使用十折交叉验证来训练和评估逻辑回归（LR）和随机森林（RF）模型，以确保对模型效能进行稳健评估。
在本研究中，对 120 例 VMAT 治疗的乳腺癌患者中的 102 例进行了详细分析。其中 32%的患者发生了 2 级 RD。年龄和 BMI 被确定为显著的临床预测因素。通过特征选择，我们将庞大的放射组学和剂量组学数据缩小到 689 个特征，分布在 10 个特征子集中用于模型构建。在 LR 模型中，包含 DVH、放射组学和剂量组学特征的 J 子集表现出最高的预测性能，AUC 为 0.82。RF 模型表明，包含临床、放射组学和剂量组学特征的 I 子集的预测准确性最佳，AUC 为 0.83。这些结果强调了将放射组学和剂量组学特征相结合可显著提高 VMAT 后乳腺癌患者 2 级 RD 预测的准确性。RF 模型分析表明，全面的特征集可最大限度地提高预测效能，标志着在放射治疗风险评估中利用人工智能和改善患者治疗结局方面迈出了有希望的一步。
