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首页> 外文期刊>Radiotherapy and oncology: Journal of the European Society for Therapeutic Radiology and Oncology >NTCP models for patient-rated xerostomia and sticky saliva after treatment with intensity modulated radiotherapy for head and neck cancer: The role of dosimetric and clinical factors
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NTCP models for patient-rated xerostomia and sticky saliva after treatment with intensity modulated radiotherapy for head and neck cancer: The role of dosimetric and clinical factors

机译：NTCP模型用于头颈癌强度调节放疗后的患者额定的口干症和唾液粘稠：剂量和临床因素的作用

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Purpose: The purpose of this multicentre prospective study was to develop multivariable logistic regression models to make valid predictions about the risk of moderate-to-severe patient-rated xerostomia (XER M6) and sticky saliva 6 months (STIC M6) after primary treatment with intensity modulated radiotherapy (IMRT) with or without chemotherapy for head and neck cancer (HNC). Methods and materials: The study population was composed of 178 consecutive HNC patients treated with IMRT. All patients were included in a standard follow up programme in which acute and late side effects and quality of life were prospectively assessed, prior to, during and after treatment. The primary endpoints were XER M6 and STIC M6 as assessed by the EORTC QLQ-H&N35 after completing IMRT. Organs at risk (OARs) potentially involved in salivary function were delineated on planning-CT, including the parotid, submandibular and sublingual glands and the minor glands in the soft palate, cheeks and lips. Patients with moderate-to-severe xerostomia or sticky saliva, respectively, at baseline were excluded. The optimal number of variables for a multivariate logistic regression model was determined using a bootstrapping method. Results: Eventually, 51.6% of the cases suffered from XER M6. The multivariate analysis showed that the mean contralateral parotid gland dose and baseline xerostomia (none vs. a bit) were the most important predictors for XER M6. For the multivariate NTCP model, the area under the receiver operating curve (AUC) was 0.68 (95% CI 0.60-0.76) and the discrimination slope was 0.10, respectively. Calibration was good with a calibration slope of 1.0. At 6 months after IMRT, 35.6% of the cases reported STIC M6. The mean contralateral submandibular gland dose, the mean sublingual dose and the mean dose to the minor salivary glands located in the soft palate were most predictive for STIC M6. For this model, the AUC was 0.70 (95% CI 0.61-0.78) and the discrimination slope was 0.12. Calibration was good with a calibration slope of 1.0. Conclusions: The multivariable NTCP models presented in this paper can be used to predict patient-rated xerostomia and sticky saliva. The dose volume parameters included in the models can be used to further optimise IMRT treatment.
机译：目的：这项多中心前瞻性研究的目的是开发多变量logistic回归模型，以对患者接受初次治疗后6个月中度至重度口腔干燥症（XER M6）和唾液粘稠（STIC M6）的风险做出有效预测调强放疗（IMRT）或不化疗用于头颈癌（HNC）。方法和材料：研究人群由178名接受IMRT治疗的连续HNC患者组成。所有患者均纳入标准随访方案，其中在治疗前，治疗中和治疗后对急性和晚期副作用以及生活质量进行了前瞻性评估。完成IMRT后，由EORTC QLQ-H＆N35评估的主要终点为XER M6和STIC M6。在计划CT上标出了可能与唾液功能有关的高危器官（OAR），包括腮腺，颌下和舌下腺以及软pa，脸颊和嘴唇的小腺。基线时分别患有中度至重度口腔干燥或唾液粘稠的患者被排除在外。使用自举方法确定多元逻辑回归模型的最佳变量数。结果：最终，有51.6％的病例患有XER M6。多元分析表明，对侧腮腺平均剂量和口干口基线（无或有一点）是XER M6的最重要预测指标。对于多变量NTCP模型，接收器工作曲线（AUC）下的面积分别为0.68（95％CI 0.60-0.76）和辨别斜率为0.10。校准斜率为1.0时校准良好。 IMRT后6个月，有35.6％的病例报告了STIC M6。对侧下颌下腺的平均剂量，舌下平均剂量和位于软pa的小唾液腺的平均剂量最能预测STIC M6。对于此模型，AUC为0.70（95％CI为0.61-0.78），辨别斜率为0.12。校准斜率为1.0时校准良好。结论：本文介绍的多变量NTCP模型可用于预测患者评定的口干症和唾液黏性。模型中包含的剂量体积参数可用于进一步优化IMRT治疗。

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