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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 90 th 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

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 (D mean ) to OARs. A comprehensive NTCP-profile was applied to obtain NTCP's. The difference between planning and actual values of D mean (ΔD mean ) and dichotomized difference of clinical relevance (BIOΔNTCP) were used for modelling to determine the cut-off maximum ΔD mean of OARs in week 1 and 2 (maxΔD mean&#95;1 and maxΔD mean&#95;2 ). Four strategies to select candidates for ART, using cut-off maxΔD mean were compared., Results: The Spearman's rank correlation test showed significant positive correlation between maxΔD mean 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 maxΔD mean&#95;1 (3.01 and 5.14 Gy) and cut-off maxΔD mean&#95;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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Background and Purpose: Previously, PET image biomarkers (PET-IBMs) - the 90 th percentile standardized uptake value (P90-SUV) and the Mean SUV (Mean-SUV) of the contralateral parotid gland (cPG) - were identified as predictors for late-xerostomia following head and neck cancer (HNC) radiotherapy. The aim of the current study was to assess in an independent validation cohort whether these pre-treatment PET-IBM can improve late-xerostomia prediction compared to the prediction with baseline xerostomia and mean cPG dose alone., Materials and Methods: The prediction endpoint was patient-rated moderate-to-severe xerostomia at 12 months after radiotherapy. The PET-IBMs were extracted from pre-treatment 18 F-FDG PET images. The performance of the model (base model) with baseline xerostomia and mean cPG dose alone and models with additionally P90-SUV or Mean-SUV were tested in the current independent validation cohort. Specifically, model discrimination (area under the curve: AUC) and calibration (calibration plot) were evaluated., Results: The current validation cohort consisted of 137 patients of which 40% developed moderate-to-severe xerostomia at 12 months. Both the PET-P90 model (AUC:PET-P90 = 0.71) and the PET-Mean model (AUC: PET-Mean = 0.70) performed well in the current validation cohort. Moreover, their performance were improved compared to the base model (AUC:base model= 0.68). The calibration plots showed a good fit of the prediction to the actual rates for all tested models., Conclusion: PET-IBMs showed an improved prediction of late-xerostomia when added to the base model in this validation cohort. This contributed to the published hypothesis that PET-IBMs include individualized information and can serve as a pre-treatment risk factor for late-xerostomia., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

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., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: J.A. Langendijk reports a relationship with Dutch Cancer Society: funding grants. J.A. Langendijk reports his department has research contracts with IBA, RaySearch, Siemens, Elekta, Leoni, and Mirada. J.A. Langendijk reports a relationship with Global Scientific Advisory Board of IBA, RayCare International Advisory Board of RaySearch that includes: board membership, consulting or advisory, and speaking and lecture fees. J.A. Langendijk reports a relationship with Netherlands Society for Radiation Oncology that includes: board membership. J.B. Reitsma and E. Schuit are involved as methodologist in the development of indication protocols for patient selection for proton therapy in the Netherlands. Conflicts of Interest J.B. Reitsma: Involvement as methodologist in the development of indication protocols for patient selection for proton therapy in the Netherlands. J.A. Langendijk: Department has research contracts with IBA, RaySearch, Siemens, Elekta, Leoni, and Mirada. Received grants from Dutch Cancer Society and EU. Member of Global Scientific Advisory Board of IBA. Member of RayCare International Advisory Board of RaySearch. Chair of the Netherlands Society for Radiation Oncology. E. Schuit: Involvement as methodologist in the development of indication protocols for patient selection for proton therapy in the Netherlands., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background and Purpose: Sarcopenia is related to late radiation-induced toxicities and worse survival in head and neck cancer (HNC) patients. This study tested the hypothesis that sarcopenia improves the performance of current normal tissue complication probability (NTCP) models of radiation-induced acute toxicity in HNC patients., Material/methods: This was a retrospective analysis in a prospective cohort of HNC patients treated from January 2007 to December 2018 with (chemo)radiotherapy. Planning CT scans were used for evaluating skeletal muscle mass. Characteristics of sarcopenic and non-sarcopenic patients were compared. The impact of sarcopenia was analysed by adding sarcopenia to the linear predictors of current NTCP models predicting physician- and patient-rated acute toxicities., Results: The cut-off values of sarcopenia in the study population (n = 977) were established at skeletal muscle index < 42.0 cm2/m2 (men) and < 31.2 cm2/m2 (women), corresponding to the lowest sex-specific quartile. Compared to non-sarcopenic patients, sarcopenic patients were more frequently smokers (61% vs. 48%, p < 0.001), had more often advanced stage of disease (stage III-IV, p = 0.004), higher age (67 vs. 63 years, p < 0.001) and experienced more pretreatment complaints, such as dysphagia (grade ≥ 2, p < 0.001). Sarcopenia remained statistically significant, next to the linear predictor, only for physician-rated grade ≥ 3 dysphagia (week 3-6 during RT, p < 0.01). However, sarcopenia did not improve the performance of these NTCP models (p > 0.99)., Conclusion: Sarcopenia in HNC patients was an independent prognostic factor for radiation-induced physician-rated acute grade ≥ 3 dysphagia, which might be explained by its impact on swallowing muscles. However, addition of sarcopenia did not improve the NTCP model performance., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

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., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

Purpose: The aim of this study is to establish the relative impact of physician-rated toxicities and patient-rated symptoms in head and neck cancer (HNC) on quality of life (QOL) and to weigh the various toxicities and symptoms during treatment plan optimization and selection., Materials and Methods: This prospective cohort study comprised 1083 HNC patients (development: 750, validation: 333) treated with definitive radiotherapy with or without chemotherapy. Clinical factors were scored at baseline. Physician-rated and patient-rated outcome measures and QOL (EORTC QLQ-HN35 and QLQ-C30) were prospectively scored at baseline and 6, 12, 18 and 24 months after radiotherapy. The impact of 20 common toxicities and symptoms (related to swallowing, salivary function, speech, pain and general complaints) on QOL (0-100 scale) was established for each time point by combining principal component analysis and multivariable linear regression., Results: Radiation-induced toxicities and symptoms resulted in a significant decline in QOL of patients with 12.4 ± 12.8 points at 6 months to 16.6 ± 17.1 points at 24 months. The multivariable linear models described the QOL points subtracted for each toxicity and symptom after radiotherapy. For example, xerostomia and weight loss had a significant but minor effect (on average -0.5 and -0.6 points) while speech problems and fatigue had a much greater impact (on average -11.9 and -17.4 points) on QOL. R 2 goodness-of-fit values for the QOL models ranged from 0.64 (6 months) to 0.72 (24 months)., Conclusion: The relative impact of physician-rated toxicities and patient-rated symptoms on QOL was quantified and can be used to optimize, compare and select HNC radiotherapy treatment plans, to balance the relevance of toxicities and to achieve the best QOL for individual patients., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background and Purpose: Radiotherapy in the head and neck area may cause vascular damage to the carotid arteries, increasing the risk of anterior circulation ischaemic cerebrovascular events (ICVEs). However, limited data exists on the relationship between radiation dose to the carotid arteries and risk of ICVE. The purpose of this study was therefore to determine the relationship between radiation dose to the carotid arteries and anterior circulation ICVE risk., Materials and Methods: A retrospective analysis of a prospective study cohort of 750 head and neck cancer patients treated with definitive (chemo)radiotherapy was performed. Carotid arteries were delineated, and dose-volume parameters of the treatment plans were calculated. ICVEs were scored prospectively and checked retrospectively by analysing all patient records. Cox proportional hazards analysis was performed to analyse the dose-effect relationships., Results: The median follow-up period was 3.4 years, 27 patients experienced an ICVE and the 5-year cumulative risk was 4.6%. ICVE risk was significantly associated with dose to the carotid arteries. Multivariable analysis showed that the absolute volume (cm 3 ) of the carotid arteries that received at least a radiation dose of 10 Gy was the most important prognostic factor for ICVE (HR = 1.11, AUC = 0.68, p < 0.001)., Conclusion: This is the first large prospective cohort study that demonstrates an independent dose-effect relationship between radiation dose to the carotid arteries and the risk of ICVE. These findings may be used to identify patients at risk for ICVE after radiotherapy who may benefit from primary or secondary preventive measures., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background and Purpose: A comprehensive individual toxicity risk profile is needed to improve radiation treatment optimisation, minimising toxicity burden, in head and neck cancer (HNC) patients. We aimed to develop and externally validate NTCP models for various toxicities at multiple time points., Materials and Methods: Using logistic regression, we determined the relationship between normal tissue irradiation and the risk of 22 toxicities at ten time points during and after treatment in 750 HNC patients. The toxicities involved swallowing, salivary, mucosal, speech, pain and general complaints. Studied predictors included patient, tumour and treatment characteristics and dose parameters of 28 organs. The resulting NTCP models were externally validated in 395 HNC patients., Results: The NTCP models involved 14 organs that were associated with at least one toxicity. The oral cavity was the predominant organ, associated with 12 toxicities. Other important organs included the parotid and submandibular glands, buccal mucosa and swallowing muscles. In addition, baseline toxicity, treatment modality, and tumour site were common predictors of toxicity. The median discrimination performance (AUC) of the models was 0.71 (interquartile range: 0.68-0.75) at internal validation and 0.67 (interquartile range: 0.62-0.71) at external validation., Conclusion: We established a comprehensive individual toxicity risk profile that provides essential insight into how radiation exposure of various organs translates into multiple acute and late toxicities. This comprehensive understanding of radiation-induced toxicities enables a new radiation treatment optimisation concept that balances multiple toxicity risks simultaneously and minimises the overall toxicity burden for an individual HNC patient who needs to undergo radiation treatment., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

Normal Tissue Complication Probability (NTCP) models can be used for treatment plan optimisation and patient selection for emerging treatment techniques. We discuss and suggest methodological approaches to address key challenges in NTCP model development and validation, including: missing data, non-linear response relationships, multicollinearity between predictors, overfitting, generalisability and the prediction of multiple complication grades at multiple time points. The methodological approaches chosen are aimed to improve the accuracy, transparency and robustness of future NTCP-models. We demonstrate our methodological approaches using clinical data., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

Background and Purpose: Sarcopenia is emerging as an adverse prognostic factor for survival and complication risk in cancer patients. This study aims to determine the impact of sarcopenia on survival and late toxicity in a large cohort of head and neck squamous cell carcinoma (HNSCC) patients treated with definitive (chemo)radiotherapy ((C)RT)., Materials and Methods: HNSCC patients treated with definitive (C)RT from January 2007 to June 2016 were included. Sarcopenia was assessed from radiation planning computed tomography (CT) scans using skeletal muscles at level C3. The impact of sarcopenia on overall survival (OS) and disease-free survival (DFS) was evaluated using the Kaplan-Meier method. Multivariable association models were developed to assess the impact of sarcopenia on late toxicity., Results: The study population was composed of 750 HNSCC patients. Cut-off values for sarcopenia were set at SMI < 42.4 cm 2 /m 2 (men) and <30.6 cm 2 /m 2 (women) corresponding lowest gender specific quartile. Sarcopenic patients had significantly poorer survival rates, especially those with lower performance status and locally advanced disease. In oropharyngeal cancer patients, survival was more determined by p16 status than by sarcopenia. In multivariable analysis, sarcopenia was associated with worse OS (HR 0.72, p = 0.012) and DFS (HR 0.67, p = 0.001). In multivariable association models, sarcopenia was associated with physician-rated xerostomia six months after treatment (OR 1.65, p = 0.027) and physician-rated dysphagia six and twelve months after treatment (OR 2.02, p = 0.012 and 2.51, p = 0.003, respectively)., Conclusion: Sarcopenia in HNSCC patients receiving definitive (C)RT is an independent prognostic factor for worse survival outcomes and is associated with physician-rated toxicity., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

Introduction: Adequate head and neck (HN) organ-at-risk (OAR) delineation is crucial for HN radiotherapy and for investigating the relationships between radiation dose to OARs and radiation-induced side effects. The automatic contouring algorithms that are currently in clinical use, such as atlas-based contouring (ABAS), leave room for improvement. The aim of this study was to use a comprehensive evaluation methodology to investigate the performance of HN OAR auto-contouring when using deep learning contouring (DLC), compared to ABAS., Methods: The DLC neural network was trained on 589 HN cancer patients. DLC was compared to ABAS by providing each method with an independent validation cohort of 104 patients, which had also been manually contoured. For each of the 22 OAR contours - glandular, upper digestive tract and central nervous system (CNS)-related structures - the dice similarity coefficient (DICE), and absolute mean and max dose differences (|Δmean-dose| and |Δmax-dose|) performance measures were obtained. For a subset of 7 OARs, an evaluation of contouring time, inter-observer variation and subjective judgement was performed., Results: DLC resulted in equal or significantly improved quantitative performance measures in 19 out of 22 OARs, compared to the ABAS (DICE/|Δmean dose|/|Δmax dose|: 0.59/4.2/4.1 Gy (ABAS); 0.74/1.1/0.8 Gy (DLC)). The improvements were mainly for the glandular and upper digestive tract OARs. DLC significantly reduced the delineation time for the inexperienced observer. The subjective evaluation showed that DLC contours were more often preferable to the ABAS contours overall, were considered to be more precise, and more often confused with manual contours. Manual contours still outperformed both DLC and ABAS; however, DLC results were within or bordering the inter-observer variability for the manual edited contours in this cohort., Conclusion: The DLC, trained on a large HN cancer patient cohort, outperformed the ABAS for the majority of HN OARs., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)