Laboratory of Radiation Physics, Odense University Hospital, Denmark; Institute of Clinical Research, University of Southern Denmark, Odense, Denmark; Institute of Medical Physics, School of Physics, University of Sydney, Australia; Danish Centre for Particle Therapy, Aarhus University Hospital, Denmark.
Laboratory of Radiation Physics, Odense University Hospital, Denmark.
Radiother Oncol. 2020 Jun;147:15-21. doi: 10.1016/j.radonc.2020.03.013. Epub 2020 Mar 28.
PURPOSE/OBJECTIVE: Radiation-induced mucositis is a severe acute side effect, which can jeopardize treatment compliance and cause weight loss during treatment. The study aimed to develop robust models to predict the risk of severe mucositis.
MATERIALS/METHODS: Mucosal toxicity scores were prospectively recorded for 802 consecutive Head and Neck (H&N) cancer patients and dichotomised into non-severe event (grade 0-2) and severe event (grade 3+) groups. Two different model approaches were utilised to evaluate the robustness of the models. These used LASSO and Best Subset selection combined with 10-fold cross-validation performed on two-thirds of the patient cohort using principal component analysis of DVHs. The remaining one-third of the patients were used for validation. Model performance was tested through calibration plot and model performance metrics.
The main predicted risk factors were treatment acceleration and the first two principal dose components, which reflect the mean dose and the balance between high and low doses to the oral cavity. For the LASSO model, gender and current smoker status were also included in the model. The AUC values of the two models on the validation cohort were 0.797 (95%CI: 0.741-0.857) and 0.808 (95%CI: 0.749-0.859), respectively. The two models predicted very similar risk values with an internal Pearson coefficient of 0.954, indicating their robustness.
Robust prediction models of the risk of severe mucositis have been developed based on information from the entire dose distribution for a large cohort of patients consisting of all patients treated H&N for within our institution over a five year period.
目的/目标：放射性黏膜炎是一种严重的急性副作用，可能会影响治疗依从性，并导致治疗期间体重下降。本研究旨在开发强大的模型来预测严重黏膜炎的风险。
材料/方法：前瞻性记录了 802 例连续头颈部（H&N）癌症患者的黏膜毒性评分，并将其分为非严重事件（0-2 级）和严重事件（3+级）组。使用两种不同的模型方法来评估模型的稳健性。这些方法使用 LASSO 和最佳子集选择，结合主成分分析 DVHs 对三分之二的患者队列进行 10 倍交叉验证。其余三分之一的患者用于验证。通过校准图和模型性能指标测试模型性能。
主要预测危险因素是治疗加速和前两个主要剂量成分，这反映了口腔的平均剂量和高剂量与低剂量之间的平衡。对于 LASSO 模型，性别和当前吸烟状况也包含在模型中。两个模型在验证队列中的 AUC 值分别为 0.797（95%CI：0.741-0.857）和 0.808（95%CI：0.749-0.859）。两个模型对验证队列的风险值预测非常相似，内部 Pearson 系数为 0.954，表明其稳健性。
基于来自包括我们机构在五年内治疗的所有 H&N 患者在内的大队列患者的整个剂量分布信息，已经开发出严重黏膜炎风险的强大预测模型。
