Late-xerostomia prediction model based on 18F-FDG PET image biomarkers of the main salivary glands.

BACKGROUND AND PURPOSE: Recently, a comprehensive xerostomia prediction model was published, based on baseline xerostomia, mean dose to parotid glands (PG) and submandibular glands (SMG). Previously, PET imaging biomarkers (IBMs) of PG were shown to improve xerostomia prediction. Therefore, this study aimed to explore the potential improvement of the additional PET-IBMs from both PG and SMG to the recent comprehensive xerostomia prediction model (i.e., the reference model). MATERIALS AND METHODS: Totally, 540 head and neck cancer patients were split into training and validation cohorts. PET-IBMs from the PG and SMG, were selected using bootstrapped forward selection based on the reference model. The IBMs from both the PG and SMG with the highest selection frequency were added to the reference model, resulting in a PG-IBM model and a SMG-IBM model which were combined into a composite model. Model performance was assessed using the area under the curve (AUC). Likelihood ratio test compared the predictive performance between the reference model and models including IBMs. RESULTS: The final selected PET-IBMs were 90th percentile of the PG SUV and total energy of the SMG SUV. The additional two PET-IBMs in the composite model improved the predictive performance of the reference model significantly. The AUC of the reference model and the composite model were 0.67 and 0.69 in the training cohort, and 0.71 and 0.73 in the validation cohort, respectively. CONCLUSION: The composite model including two additional PET-IBMs from PG and SMG improved the predictive performance of the reference xerostomia model significantly, facilitating a more personalized prediction approach.

An efficient strategy to select head and neck cancer patients for adaptive radiotherapy.

BACKGROUND AND PURPOSE: Adaptive radiotherapy (ART) is workload intensive but only benefits a subgroup of patients. We aimed to develop an efficient strategy to select candidates for ART in the first two weeks of head and neck cancer (HNC) radiotherapy. MATERIALS AND METHODS: This study retrospectively enrolled 110 HNC patients who underwent modern photon radiotherapy with at least 5 weekly in-treatment re-scan CTs. A semi auto-segmentation method was applied to obtain the weekly mean dose (Dmean) to OARs. A comprehensive NTCP-profile was applied to obtain NTCP's. The difference between planning and actual values of Dmean (ΔDmean) and dichotomized difference of clinical relevance (BIOΔNTCP) were used for modelling to determine the cut-off maximum ΔDmean of OARs in week 1 and 2 (maxΔDmean_1 and maxΔDmean_2). Four strategies to select candidates for ART, using cut-off maxΔDmean were compared. RESULTS: The Spearman's rank correlation test showed significant positive correlation between maxΔDmean and BIOΔNTCP (p-value <0.001). For major BIOΔNTCP (>5%) of acute and late toxicity, 10.9% and 4.5% of the patients were true candidates for ART. Strategy C using both cut-off maxΔDmean_1 (3.01 and 5.14 Gy) and cut-off maxΔDmean_2 (3.41 and 5.30 Gy) showed the best sensitivity, specificity, positive and negative predictive values (0.92, 0.82, 0.38, 0.99 for acute toxicity and 1.00, 0.92, 0.38, 1.00 for late toxicity, respectively). CONCLUSIONS: We propose an efficient selection strategy for ART that is able to classify the subgroup of patients with >5% BIOΔNTCP for late toxicity using imaging in the first two treatment weeks.

External validation of NTCP-models for radiation pneumonitis in lung cancer patients treated with chemoradiotherapy.

PURPOSE: Normal tissue complication probability (NTCP) models can be used to estimate the risk of radiation pneumonitis (RP). The aim of this study was to externally validate the most frequently used prediction models for RP, i.e., the QUANTEC and APPELT models, in a large cohort of lung cancer patients treated with IMRT or VMAT. [1-2] METHODS AND MATERIALS: This prospective cohort study, included lung cancer patients treated between 2013 and 2018. A closed testing procedure was performed to test the need for model updating. To improve model performance, modification or removal of variables was considered. Performance measures included tests for goodness of fit, discrimination, and calibration. RESULTS: In this cohort of 612 patients, the incidence of RP ≥ grade 2 was 14.5%. For the QUANTEC-model, recalibration was recommended which resulted in a revised intercept and adjusted regression coefficient (from 0.126 to 0.224) of the mean lung dose (MLD),. The APPELT-model needed revision including model updating with modification and elimination of variables. After revision, the New RP-model included the following predictors (and regression coefficients): MLD (B = 0.250), age (B = 0.049, and smoking status (B = 0.902). The discrimination of the updated APPELT-model was higher compared to the recalibrated QUANTEC-model (AUC: 0.79 vs. 0.73). CONCLUSIONS: This study demonstrated that both the QUANTEC- and APPELT-model needed revision. Next to changes of the intercept and regression coefficients, the APPELT model improved further by model updating and performed better than the recalibrated QUANTEC model. This New RP-model is widely applicable containing non-tumour site specific variables, which can easily be collected.

CT-based deep multi-label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma.

BACKGROUND: Personalized treatment is increasingly required for oropharyngeal squamous cell carcinoma (OPSCC) patients due to emerging new cancer subtypes and treatment options. Outcome prediction model can help identify low or high-risk patients who may be suitable to receive de-escalation or intensified treatment approaches. PURPOSE: To develop a deep learning (DL)-based model for predicting multiple and associated efficacy endpoints in OPSCC patients based on computed tomography (CT). METHODS: Two patient cohorts were used in this study: a development cohort consisting of 524 OPSCC patients (70% for training and 30% for independent testing) and an external test cohort of 396 patients. Pre-treatment CT-scans with the gross primary tumor volume contours (GTVt) and clinical parameters were available to predict endpoints, including 2-year local control (LC), regional control (RC), locoregional control (LRC), distant metastasis-free survival (DMFS), disease-specific survival (DSS), overall survival (OS), and disease-free survival (DFS). We proposed DL outcome prediction models with the multi-label learning (MLL) strategy that integrates the associations of different endpoints based on clinical factors and CT-scans. RESULTS: The multi-label learning models outperformed the models that were developed based on a single endpoint for all endpoints especially with high AUCs ≥ 0.80 for 2-year RC, DMFS, DSS, OS, and DFS in the internal independent test set and for all endpoints except 2-year LRC in the external test set. Furthermore, with the models developed, patients could be stratified into high and low-risk groups that were significantly different for all endpoints in the internal test set and for all endpoints except DMFS in the external test set. CONCLUSION: MLL models demonstrated better discriminative ability for all 2-year efficacy endpoints than single outcome models in the internal test and for all endpoints except LRC in the external set.

Prediction of Radiation-Induced Parotid Gland-Related Xerostomia in Patients With Head and Neck Cancer: Regeneration-Weighted Dose.

PURPOSE: Despite improvements to treatment, patients with head and neck cancer (HNC) still experience radiation-induced xerostomia due to salivary gland damage. The stem cells of the parotid gland (PG), concentrated in the gland's main ducts (stem cell rich [SCR] region), play a critical role in the PG's response to radiation. Treatment optimization requires a dose metric that properly accounts for the relative contributions of dose to this SCR region and the PG's remainder (non-SCR region) to the risk of xerostomia in normal tissue complication probability (NTCP) models for xerostomia. MATERIALS AND METHODS: Treatment and toxicity data of 1013 prospectively followed patients with HNC treated with definitive radiation therapy (RT) were used. The regeneration-weighted dose, enabling accounting for the hypothesized different effects of dose to the SCR and non-SCR region on the risk of xerostomia, was defined as Dreg PG = Dmean SCR region + r × Dmean non-SCR region, where Dreg is the regeneration-weighted dose, Dmean is the mean dose, and r is the weighting factor. Considering the different volumes of these regions, r > 3.6 in Dreg PG demonstrates an enhanced effect of the SCR region. The most predictive value of r was estimated in 102 patients of a previously published trial testing stem cell sparing RT. For each endpoint, Dreg PG, dose to other organs, and clinical factors were used to develop NTCP models using multivariable logistic regression analysis in 663 patients. The models were validated in 350 patients. RESULTS: Dose to the contralateral PG was associated with daytime, eating-related, and physician-rated grade ≥2 xerostomia. Consequently, r was estimated and found to be smaller than 3.6 for most PG function-related endpoints. Therefore, the contribution of Dmean SCR region to the risk of xerostomia was larger than predicted by Dmean PG. Other frequently selected predictors were pretreatment xerostomia and Dmean oral cavity. The validation showed good discrimination and calibration. CONCLUSIONS: Tools for clinical implementation of stem cell sparing RT were developed: regeneration-weighted dose to the parotid gland that accounted for regional differences in radiosensitivity within the gland and NTCP models that included this new dose metric and other prognostic factors.

The relation between prediction model performance measures and patient selection outcomes for proton therapy in head and neck cancer.

BACKGROUND: Normal-tissue complication probability (NTCP) models predict complication risk in patients receiving radiotherapy, considering radiation dose to healthy tissues, and are used to select patients for proton therapy, based on their expected reduction in risk after proton therapy versus photon radiotherapy (ΔNTCP). Recommended model evaluation measures include area under the receiver operating characteristic curve (AUC), overall calibration (CITL), and calibration slope (CS), whose precise relation to patient selection is still unclear. We investigated how each measure relates to patient selection outcomes. METHODS: The model validation and consequent patient selection process was simulated within empirical head and neck cancer patient data. By manipulating performance measures independently via model perturbations, the relation between model performance and patient selection was studied. RESULTS: Small reductions in AUC (-0.02) yielded mean changes in ΔNTCP between 0.9-3.2 %, and single-model patient selection differences between 2-19 %. Deviations (-0.2 or +0.2) in CITL or CS yielded mean changes in ΔNTCP between 0.3-1.4 %, and single-model patient selection differences between 1-10 %. CONCLUSIONS: Each measure independently impacts ΔNTCP and patient selection and should thus be assessed in a representative sufficiently large external sample. Our suggested practical model selection approach is considering the model with the highest AUC, and recalibrating it if needed.

Robustness assessment of clinical adaptive proton and photon radiotherapy for oesophageal cancer in the model-based approach.

PURPOSE: In the Netherlands, oesophageal cancer (EC) patients are selected for intensity modulated proton therapy (IMPT) using the expected normal tissue complication probability reduction (ΔNTCP) when treating with IMPT compared to volumetric modulated arc therapy (VMAT). In this study, we evaluate the robustness of the first EC patients treated with IMPT in our clinic in terms of target and organs-at-risk (OAR) dose with corresponding NTCP, as compared to VMAT. MATERIALS AND METHODS: For 20 consecutive EC patients, clinical IMPT and VMAT plans were created on the average planning 4DCT. Both plans were robustly evaluated on weekly repeated 4DCTs and if target coverage degraded, replanning was performed. Target coverage was evaluated for complete treatment trajectories with and without replanning. The planned and accumulated mean lung dose (MLD) and mean heart dose (MHD) were additionally evaluated and translated into NTCP. RESULTS: Replanning in the clinic was performed more often for IMPT (15x) than would have been needed for VMAT (8x) (p = 0.11). Both adaptive treatments would have resulted in adequate accumulated target dose coverage. Replanning in the first week of treatment had most clinical impact, as anatomical changes resulting in insufficient accumulated target coverage were already observed at this stage. No differences were found in MLD between the planned dose and the accumulated dose. Accumulated MHD differed from the planned dose (p < 0.001), but since these differences were similar for VMAT and IMPT (1.0 and 1.5 Gy, respectively), the ΔNTCP remained unchanged. CONCLUSION: Following an adaptive clinical workflow, adequate target dose coverage and stable OAR doses with corresponding NTCPs was assured for both IMPT and VMAT.

A Decision Support Tool to Optimize Selection of Head and Neck Cancer Patients for Proton Therapy.

Selection of head and neck cancer (HNC) patients for proton therapy (PT) using plan comparison (VMAT vs. IMPT) for each patient is labor-intensive. Our aim was to develop a decision support tool to identify patients with high probability to qualify for PT, at a very early stage (immediately after delineation) to avoid delay in treatment initiation. A total of 151 HNC patients were included, of which 106 (70%) patients qualified for PT. Linear regression models for individual OARs were created to predict the Dmean to the OARs for VMAT and IMPT plans. The predictors were OAR volume percentages overlapping with target volumes. Then, actual and predicted plan comparison decisions were compared. Actual and predicted OAR Dmean (VMAT R2 = 0.953, IMPT R2 = 0.975) and NTCP values (VMAT R2 = 0.986, IMPT R2 = 0.992) were highly correlated. The sensitivity, specificity, PPV and NPV of the decision support tool were 64%, 87%, 92% and 51%, respectively. The expected toxicity reduction with IMPT can be predicted using only the delineation data. The probability of qualifying for PT is >90% when the tool indicates a positive outcome for PT. This tool will contribute significantly to a more effective selection of HNC patients for PT at a much earlier stage, reducing treatment delay.

Performance of binary prediction models in high-correlation low-dimensional settings: a comparison of methods.

BACKGROUND: Clinical prediction models are developed widely across medical disciplines. When predictors in such models are highly collinear, unexpected or spurious predictor-outcome associations may occur, thereby potentially reducing face-validity of the prediction model. Collinearity can be dealt with by exclusion of collinear predictors, but when there is no a priori motivation (besides collinearity) to include or exclude specific predictors, such an approach is arbitrary and possibly inappropriate. METHODS: We compare different methods to address collinearity, including shrinkage, dimensionality reduction, and constrained optimization. The effectiveness of these methods is illustrated via simulations. RESULTS: In the conducted simulations, no effect of collinearity was observed on predictive outcomes (AUC, R2, Intercept, Slope) across methods. However, a negative effect of collinearity on the stability of predictor selection was found, affecting all compared methods, but in particular methods that perform strong predictor selection (e.g., Lasso). Methods for which the included set of predictors remained most stable under increased collinearity were Ridge, PCLR, LAELR, and Dropout. CONCLUSIONS: Based on the results, we would recommend refraining from data-driven predictor selection approaches in the presence of high collinearity, because of the increased instability of predictor selection, even in relatively high events-per-variable settings. The selection of certain predictors over others may disproportionally give the impression that included predictors have a stronger association with the outcome than excluded predictors.

Parotid Gland Stem Cell Sparing Radiation Therapy for Patients With Head and Neck Cancer: A Double-Blind Randomized Controlled Trial.

PURPOSE: Radiation therapy for head and neck cancer frequently leads to salivary gland damage and subsequent xerostomia. The radiation response of the parotid glands of rats, mice, and patients critically depends on dose to parotid gland stem cells, mainly located in the gland's main ducts (stem cell rich [SCR] region). Therefore, this double-blind randomized controlled trial aimed to test the hypothesis that parotid gland stem cell sparing radiation therapy preserves parotid gland function better than currently used whole parotid gland sparing radiation therapy. METHODS AND MATERIALS: Patients with head and neck cancer (n = 102) treated with definitive radiation therapy were randomized between standard parotid-sparing and stem cell sparing (SCS) techniques. The primary endpoint was >75% reduction in parotid gland saliva production compared with pretreatment production (FLOW12M). Secondary endpoints were several aspects of xerostomia 12 months after treatment. RESULTS: Fifty-four patients were assigned to the standard arm and 48 to the SCS arm. Only dose to the SCR regions (contralateral 16 and 11 Gy [P = .004] and ipsilateral 26 and 16 Gy [P = .001] in the standard and SCS arm, respectively) and pretreatment patient-rated daytime xerostomia (35% and 13% [P = .01] in the standard and SCS arm, respectively) differed significantly between the arms. In the SCS arm, 1 patient (2.8%) experienced FLOW12M compared with 2 (4.9%) in the standard arm (P = 1.00). However, a trend toward better relative parotid gland salivary function in favor of SCS radiation therapy was shown. Moreover, multivariable analysis showed that mean contralateral SCR region dose was the strongest dosimetric predictor for moderate-to-severe patient-rated daytime xerostomia and grade ≥2 physician-rated xerostomia, the latter including reported alteration in diet. CONCLUSIONS: No significantly better parotid function was observed in SCS radiation therapy. However, additional multivariable analysis showed that dose to the SCR region was more predictive of the development of parotid gland function-related xerostomia endpoints than dose to the entire parotid gland.

Relationship between videofluoroscopic and subjective (physician- and patient- rated) assessment of late swallowing dysfunction after (chemo) radiation: Results of a prospective observational study.

BACKGROUND AND PURPOSE: Primary (chemo)radiation (CHRT) for HNC may lead to late dysphagia. The purpose of this study was to assess the pattern of swallowing disorders based on prospectively collected objective videofluoroscopic (VF) assessment and to assess the correlations between VF findings and subjective (physician- and patient-rated) swallowing measures. MATERIAL AND METHODS: 189 consecutive HNC patients receiving (CH)RT were included. Swallowing evaluation at baseline and 6 months after treatment (T6) encompassed: CTCAE v.4.0 scores (aspiration/dysphagia), PROMs: SWAL QOL/ EORTC QLQ-H&N35 (swallowing domain) questionnaires and VF evaluation: Penetration Aspiration Scale, semi-quantitative swallowing pathophysiology evaluation, temporal measures and oral/pharyngeal residue quantification. Aspiration specific PROMs (aPROMs) were selected. Correlations between late penetration/aspiration (PA_T6) and: clinical factors, CTCAE and aPROMs were assessed using uni- and multivariable analysis. RESULTS: Prevalence of PA increased from 20% at baseline to 43% after treatment (p < 0.001). The most relevant baseline predictors for PA_T6 were: PA_T0, age, disease stage III-IV, bilateral RT and baseline aPROM 'Choking when drinking' (AUC: 0.84). In general aPROMs correlated better with VF-based PA than CTCAE scores. The most of physiological swallowing components significantly correlated and predictive for PA (i.e. Laryngeal Vestibular Closure, Laryngeal Elevation and Pharyngeal Contraction) were prone to radiation damage. CONCLUSION: The risk of RT-induced PA is substantial. Presented prediction models for late penetration/aspiration may support patient selection for baseline and follow-up VF examination. Furthermore, all aspiration related OARs involved in aforementioned swallowing components should be addressed in swallowing sparing strategies. The dose to these structures as well as baseline PROMs should be included in future NTCP models for aspiration.

Can the mean linear energy transfer of organs be directly related to patient toxicities for current head and neck cancer intensity-modulated proton therapy practice?

BACKGROUND AND PURPOSE: The relative biological effectiveness (RBE) of proton therapy is predicted to vary with the dose-weighted average linear energy transfer (LETd). However, RBE values may substantially vary for different clinical endpoints. Therefore, the aim of this study was to assess the feasibility of relating mean D⋅LETd parameters to patient toxicity for HNC patients treated with proton therapy. MATERIALS AND METHODS: The delivered physical dose (D) and the voxel-wise product of D and LETd (D⋅LETd) distributions were calculated for 100 head and neck cancer (HNC) proton therapy patients using our TPS (Raystation v6R). The means and covariance matrix of the accumulated D and D⋅LETd of all relevant organs-at-risk (OARs) were used to simulate 2.500 data sets of different sizes. For each dataset, an attempt was made to add mean D⋅LETd parameters to a multivariable NTCP model based on mean D parameters of the same OAR for xerostomia, tube feeding and dysphagia. The likelihood of creating an NTCP model with statistically significant parameters (i.e. power) was calculated as a function of the simulated sample size for various RBE models. RESULTS: The sample size required to have a power of at least 80% to show an independent effect of mean D⋅LETd parameters on toxicity is over 15,000 patients for all toxicities. CONCLUSION: For current clinical practice, it is not feasible to directly model NTCP with both mean D and mean D⋅LETd of OARs. These findings should not be interpreted as a contradiction of previous evidence for the relationship between RBE and LETd.

Validation of separate multi-atlases for auto segmentation of cardiac substructures in CT-scans acquired in deep inspiration breath hold and free breathing.

BACKGROUND AND PURPOSE: Developing NTCP-models for cardiac complications after breast cancer (BC) radiotherapy requires cardiac dose-volume parameters for many patients. These can be obtained by using multi-atlas based automatic segmentation (MABAS) of cardiac structures in planning CT scans. We investigated the relevance of separate multi-atlases for deep inspiration breath hold (DIBH) and free breathing (FB) CT scans. MATERIALS AND METHODS: BC patients scanned in DIBH (n = 10) and in FB (n = 20) were selected to create separate multi-atlases consisting of expert panel delineations of the whole heart, atria and ventricles. The accuracy of atlas-generated contours was validated with expert delineations in independent datasets (n = 10 for DIBH and FB) and reported as Dice coefficients, contour distances and dose-volume differences in relation to interobserver variability of manual contours. Dependency of MABAS contouring accuracy on breathing technique was assessed by validation of a FB atlas in DIBH patients and vice versa (cross-validation). RESULTS: For all structures the FB and DIBH atlases resulted in Dice coefficients with their respective reference contours ≥ 0.8 and average contour distances ≤ 2 mm smaller than slice thickness of (CTs). No significant differences were found for dose-volume parameters in volumes receiving relevant dose levels (WH, LV and RV). Accuracy of the DIBH atlas was at least similar to, and for the ventricles better than, the interobserver variation in manual delineation. Cross-validation between breathing techniques showed a reduced MABAS performance. CONCLUSION: Multi-atlas accuracy was at least similar to interobserver delineation variation. Separate atlases for scans made in DIBH and FB could benefit atlas performance because accuracy depends on breathing technique.

National Protocol for Model-Based Selection for Proton Therapy in Head and Neck Cancer.

In the Netherlands, the model-based approach is used to identify patients with head and neck cancer who may benefit most from proton therapy in terms of prevention of late radiation-induced side effects in comparison with photon therapy. To this purpose, a National Indication Protocol Proton therapy for Head and Neck Cancer patients (NIPP-HNC) was developed, which has been approved by the health care authorities. When patients qualify according to the guidelines of the NIPP-HNC, proton therapy is fully reimbursed. This article describes the procedures that were followed to develop this NIPP-HNC and provides all necessary information to introduce model-based selection for patients with head and neck cancer into routine clinical practice.

Quality of life and toxicity guided treatment plan optimisation for head and neck cancer.

PURPOSE: To evaluate the feasibility of semi-automatic Quality of Life (QOL)-weighted normal tissue complication probability (NTCP)-guided VMAT treatment plan optimisation in head and neck cancer (HNC) and compare predicted QOL to that obtained with conventional treatment. MATERIALS AND METHODS: This study included 30 HNC patients who were treated with definitive radiotherapy. QOL-weighted NTCP-guided VMAT plans were optimised directly on 80 multivariable NTCP models of 20 common toxicities and symptoms on 4 different time points (6, 12, 18 and 24 months after radiotherapy) and each NTCP model was weighted relative to its impact on QOL. Planning results, NTCP and predicted QOL were compared with the clinical conventional VMAT plans. RESULTS: QOL-weighted NTCP-guided VMAT plans were clinically acceptable, had target coverage equally adequate as the clinical plans, but prioritised sparing of organs at risk (OAR) related to toxicities and symptoms that had the highest impact on QOL. NTCP was reduced for, e.g., dysphagia (-6.1% for ≥grade 2/-7.6% for ≥grade 3) and moderate-to-severe fatigue/speech problems/hoarseness (-0.7%/-1.5%/-2.5%) at 6 months, respectively. Concurrently, the average NTCP of toxicities related to salivary function increased with +0.4% to +5.7%. QOL-weighted NTCP-guided plans were produced in less time, were less dependent on the treatment planner experience and yielded more consistent results. The average predicted QOL improved by 0.7, 0.9, 1.0, and 1.1 points on a 0-100 scale (p < 0.001) at 6, 12, 18, and 24 months, respectively, compared to the clinical plans. CONCLUSION: Semi-automatic QOL-weighted NTCP-guided VMAT treatment plan optimisation is feasible. It prioritised sparing of OARs related to high-impact toxicities and symptoms and resulted in a systematic improvement of predicted QOL compared to conventional VMAT.

Patient-Reported Toxicity and Quality-of-Life Profiles in Patients With Head and Neck Cancer Treated With Definitive Radiation Therapy or Chemoradiation.

PURPOSE: Radiation therapy is an effective but burdensome treatment for head and neck cancer (HNC). We aimed to characterize the severity and time pattern of patient-reported symptoms and quality of life in a large cohort of patients with HNC treated with definitive radiation therapy, with or without systemic treatment. METHODS AND MATERIALS: A total of 859 patients with HNC treated between 2007 and 2017 prospectively completed the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire-Head and Neck Cancer module (QLQ-HN35) and Core Quality of Life Questionnaire (QLQ-C30) at regular intervals during and after treatment for up to 5 years. Patients were classified into 3 subgroups: early larynx cancer, infrahyoideal cancer, and suprahyoideal cancer. Outcome scales of both questionnaires were quantified per subgroup and time point by means of average scores and the frequency distribution of categorized severity (none, mild, moderate, and severe). Time patterns and symptom severity were characterized. Toxicity profiles were compared using linear mixed model analysis. Additional toxicity profiles based on age, human papillomavirus status, treatment modality, smoking status, tumor site, and treatment period were characterized as well. RESULTS: The study population consisted of 157 patients with early larynx cancer, 304 with infrahyoideal cancer, and 398 with suprahyoideal cancer. The overall questionnaire response rate was 83%. Generally, the EORTC QLQ-HN35 symptoms reported showed a clear time pattern, with increasing scores during treatment followed by a gradual recovery in the first 2 years. Distinct toxicity profiles were seen across subgroups (P < .001), with generally less severe symptom scores in the early larynx subgroup. The EORTC QLQ-C30 functioning, quality-of-life, and general symptoms reported showed a less evident time pattern and less pronounced differences in mean scores between subgroups, although differences were still significant (P < .001). Differences in mean scores were most pronounced for role functioning, appetite loss, fatigue, and pain. CONCLUSIONS: We established patient-reported toxicity and quality-of-life profiles that showed different patterns for 3 subgroups of patients with HNC. These profiles provide detailed information on the severity and persistence of various symptoms as experienced by patients during and after definitive radiation therapy. These profiles can be used to inform treatment of future patients and may serve as a benchmark for future studies.