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

Abstract

PurposeThe 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 (XERM6) and sticky saliva 6months (STICM6) after primary treatment with intensity modulated radiotherapy (IMRT) with or without chemotherapy for head and neck cancer (HNC). Methods and materialsThe 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 XERM6 and STICM6 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. ResultsEventually, 51.6% of the cases suffered from XERM6. The multivariate analysis showed that the mean contralateral parotid gland dose and baseline xerostomia (none vs. a bit) were the most important predictors for XERM6. 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 6months after IMRT, 35.6% of the cases reported STICM6. 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 STICM6. 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. ConclusionsThe 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.