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. RESULTS 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. CONCLUSIONS 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.

中文翻译：

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基于大型患者队列的 H&N 癌症患者放射诱发性粘膜炎的预测

目的/目的 辐射诱导的粘膜炎是一种严重的急性副作用，可危及治疗依从性并导致治疗期间体重减轻。该研究旨在开发强大的模型来预测严重粘膜炎的风险。材料/方法 前瞻性记录了 802 名连续头颈 (H&N) 癌症患者的粘膜毒性评分，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。这些使用 LASSO 和最佳子集选择结合使用 DVH 的主成分分析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。结果 主要的预测风险因素是治疗加速和前两个主要剂量成分，它们反映了口腔的平均剂量和高低剂量之间的平衡。对于 LASSO 模型，模型中还包括性别和当前吸烟者状态。验证队列中两个模型的 AUC 值分别为 0.797（95%CI：0.741-0.857）和 0.808（95%CI：0.749-0.859）。这两个模型预测的风险值非常相似，内部 Pearson 系数为 0.954，表明它们的稳健性。结论 严重粘膜炎风险的稳健预测模型已经基于来自所有接受治疗的 H& 患者组成的大型患者队列的整个剂量分布的信息而开发。