Development of Normal Tissue Complication Probability Model for Trismus in Head and Neck Cancer Patients Treated With Radiotherapy: The Role of Dosimetric and Clinical Factors.

BACKGROUND/AIM: The aim of this study was to develop a normal tissue complication probability (NTCP) model for trismus in head and neck cancer (HNC) patients treated with radiotherapy (RT). PATIENTS AND METHODS: Prospective measurements of maximum inter-incisal opening (MIO) were performed at baseline and 6 months after definitive RT in 132 HNC patients. The primary endpoint of this study was defined when a patient fulfilled both of the following criteria: 1) MIO at 6 months after RT ≤35 mm and 2) MIO at 6 months after RT ≤80% of baseline MIO. Eleven clinical factors and a wide range of dosimetric factors (mean dose, maximum dose, V5, V10, V20, and V40) in twelve organs at risk (OARs) were chosen as candidate prognostic variables. RESULTS: Thirty out of 132 patients (23%) developed the primary endpoint. Multivariate logistic regression analysis revealed that the mean dose to the contralateral mandible joint (p=0.001) and baseline MIO (p=0.027) were independent prognostic factors. CONCLUSION: A multivariable NTCP model for trismus in HNC patients treated with RT was established including the mean dose to contralateral mandible joint and baseline MIO.

External validation of three dimensional conformal radiotherapy based NTCP models for patient-rated xerostomia and sticky saliva among patients treated with intensity modulated radiotherapy.

PURPOSE: The purpose of this study was to investigate the ability of predictive models for patient-rated xerostomia (XER(6M)) and sticky saliva (STIC(6M)) at 6 months after completion of primary (chemo)radiation developed in head and neck cancer patients treated with 3D-conformal radiotherapy (3D-CRT) to predict outcome in patients treated with intensity modulated radiotherapy (IMRT). METHODS AND MATERIALS: Recently, we published the results of a prospective study on predictive models for patient-rated xerostomia and sticky saliva in head and neck cancer patients treated with 3D-CRT (3D-CRT based NTCP models). The 3D-CRT based model for XER(6M) consisted of three factors, including the mean parotid dose, age, and baseline xerostomia (none versus a bit). The 3D-CRT based model for STIC(6M) consisted of the mean submandibular dose, age, the mean sublingual dose, and baseline sticky saliva (none versus a bit). In the current study, a population consisting of 162 patients treated with IMRT was used to test the external validity of these 3D-CRT based models. External validity was described by the explained variation (R(2) Nagelkerke) and the Brier score. The discriminative abilities of the models were calculated using the area under the receiver operating curve (AUC) and calibration (i.e. the agreement between predicted and observed outcome) was assessed with the Hosmer-Lemeshow "goodness-of-fit" test. RESULTS: Overall model performance of the 3D-CRT based predictive models for XER(6M) and STIC(6M) was significantly worse in terms of the Brier score and R(2) Nagelkerke among patients treated with IMRT. Moreover the AUC for both 3D-CRT based models in the IMRT treated patients were markedly lower. The Hosmer-Lemeshow test showed a significant disagreement for both models between predicted risk and observed outcome. CONCLUSION: 3D-CRT based models for patient-rated xerostomia and sticky saliva among head and neck cancer patients treated with primary radiotherapy or chemoradiation turned out to be less valid for patients treated with IMRT. The main message from these findings is that models developed in a population treated with a specific technique cannot be generalised and extrapolated to a population treated with another technique without external validation.

Development of NTCP models for head and neck cancer patients treated with three-dimensional conformal radiotherapy for xerostomia and sticky saliva: the role of dosimetric and clinical factors.

PURPOSE: The purpose of this multicentre prospective study was to investigate the significance of the radiation dose in the major and minor salivary glands, and other pre-treatment and treatment factors, with regard to the development of patient-rated xerostomia and sticky saliva among head and neck cancer (HNC) patients treated with primary (chemo-) radiotherapy ((CH)RT). METHODS AND MATERIALS: The study population was composed of 167 consecutive HNC patients treated with three-dimensional conformal (3D-CRT) (CH) RT. The primary endpoint was moderate to severe xerostomia (XER6m) as assessed by the EORTC QLQ-H&N35 at 6 months after completing (CH)RT. The secondary endpoint was moderate to severe sticky saliva at 6 months (STIC6m). All 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 at baseline were excluded. The optimum number of variables for a multivariate logistic regression model was determined using a bootstrapping method. RESULTS: The multivariate analysis showed the mean parotid dose, age and baseline xerostomia (none versus a bit) to be the most important predictors for XER6m. The risk of developing xerostomia increased with age and was higher when minor baseline xerostomia was present in comparison with patients without any xerostomia complaints at baseline. Model performance was good with an area under the curve (AUC) of 0.82. For STIC6m, the mean submandibular dose, age, the mean sublingual dose and baseline sticky saliva (none versus a bit) were most predictive for sticky saliva. The risk of developing STIC6m increased with age and was higher when minor baseline sticky saliva was present in comparison with patients without any sticky saliva complaints at baseline. Model performance was good with an AUC of 0.84. CONCLUSIONS: Dose distributions in the minor salivary glands in patients receiving 3D-CRT have limited significance with regard to patient-rated symptoms related to salivary dysfunction. Besides the parotid and submandibular glands, only the sublingual glands were significantly associated with sticky saliva. In addition, reliable risk estimation also requires information from other factors such as age and baseline subjective scores. When these selected factors are included in predictive models, instead of only dose volume histogram parameters, model performance can be improved significantly.