Yang Shan-Shan, OuYang Pu-Yun, Guo Jian-Gui, Cai Jia-Jun, Zhang Jun, Peng Qing-He, He Yun, Zhang Bao-Yu, Liu Zhi-Qiao, Hu Xue-Feng, Chen Yan-Feng, Chen Chun-Yan, Xie Fang-Yun
Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, No. 651 Dongfeng Road East, Guangzhou, China; Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, No. 651 Dongfeng Road East, Guangzhou, China.
Int J Radiat Oncol Biol Phys. 2023 Apr 1;115(5):1291-1300. doi: 10.1016/j.ijrobp.2022.11.036. Epub 2022 Dec 1.
We aimed to assess the value of dose distribution-based dosiomics and planning computed tomography-based radiomics to predict radiation-induced temporal lobe injury (TLI) and guide individualized intensity modulated radiation therapy.
A total of 5599 nasopharyngeal carcinoma patients were enrolled, including 2503, 1072, 988, and 1036 patients in the training, validation, prospective test, and external test cohorts, respectively. The concordance index (C-index) was used to compare the performance of the radiomics and dosiomics models with that of the quantitative analyses of normal tissue effects in the clinic and Wen's models. The predicted TLI-free survival rates of redesigned simulated plans with the same dose-volume histogram but different dose distributions for same patient in a cohort of 30 randomly selected patients were compared by the Wilcoxon matched-pairs signed-rank test.
The radiomics and dosiomics signatures were constructed based on 30 selected computed tomography features and 10 selected dose distribution features, respectively, which were important predictors of TLI-free survival (all P <.001). However, the radiomics signature had a low C-index. The dosiomics risk model combining the dosiomics signature, D, and age had favorable performance, with C-index values of 0.776, 0.811, 0.805, and 0.794 in the training, validation, prospective test, and external test cohorts, respectively, which were better than those of the quantitative analyses of normal tissue effects in the clinic model and Wen's model (all P <.001). The dosiomics risk model can further distinguish patients in a same risk category divided by other models (all P <.05). Conversely, the other models were unable to separate populations classified by the dosiomics risk model (all P > .05). Two simulated plans with the same dose-volume histogram but different dose distributions had different TLI-free survival rates predicted by dosiomics risk model (all P ≤ .002).
The dosiomics risk model was superior to traditional models in predicting the risk of TLI. This is a promising approach to precisely predict radiation-induced toxicities and guide individualized intensity modulated radiation therapy.
我们旨在评估基于剂量分布的剂量组学和基于计划计算机断层扫描的影像组学在预测放射性颞叶损伤（TLI）及指导个体化调强放射治疗方面的价值。
共纳入5599例鼻咽癌患者，其中训练队列、验证队列、前瞻性测试队列和外部测试队列分别有2503例、1072例、988例和1036例患者。一致性指数（C指数）用于比较影像组学和剂量组学模型与临床正常组织效应定量分析模型及Wen模型的性能。通过Wilcoxon配对符号秩检验比较在30例随机选择患者的队列中，相同剂量体积直方图但不同剂量分布的重新设计模拟计划的预测无TLI生存率。
影像组学和剂量组学特征分别基于30个选定的计算机断层扫描特征和10个选定的剂量分布特征构建，它们是无TLI生存的重要预测指标（所有P <.001）。然而，影像组学特征的C指数较低。结合剂量组学特征、D和年龄的剂量组学风险模型表现良好，在训练队列、验证队列、前瞻性测试队列和外部测试队列中的C指数值分别为0.776、0.811、0.805和0.794，优于临床模型中正常组织效应定量分析模型和Wen模型（所有P <.001）。剂量组学风险模型可以进一步区分其他模型划分的同一风险类别中的患者（所有P <.05）。相反，其他模型无法区分剂量组学风险模型分类的人群（所有P >.05）。两个具有相同剂量体积直方图但不同剂量分布的模拟计划，剂量组学风险模型预测的无TLI生存率不同（所有P ≤.002）。
剂量组学风险模型在预测TLI风险方面优于传统模型。这是一种精确预测放射性毒性并指导个体化调强放射治疗的有前景的方法。
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