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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.

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.

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 1,083 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. R2 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.

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 pre-dictors 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 tox-icity burden for an individual HNC patient who needs to undergo radiation treatment. (C) 2021 The Author(s). Published by Elsevier B.V.

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. (C) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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. (C) 2020 The Authors. Published by Elsevier B.V.

Background and purpose: Previously, PET image biomarkers (PET-IBMs) – the 90th 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.

Item does not contain fulltext Advances in diagnostic imaging create opportunities for improved therapeutic targeting of cancer but conceptual thinking about radiotherapy target volume definition and dose-prescription is not keeping up. In this opinion paper we discuss how modern imaging can contribute to new concepts for radiotherapy dose-prescription and target volume definition illustrated by the example of head and neck cancer. These new insights have the potential to significantly reduce radiation associated toxicity and may have important impact on the combination of radiotherapy with systemic cancer therapies.

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.

Background: In quantitative FDG-PET data analysis, normalization of the standardized uptake value (SUV) with an internal image-derived standard improves its reproducibility. In this study, the cervical spinal cord is proposed as an internal standard that is within the field of view of the radiotherapy planning PET/CT-scan in head and neck cancer. The aim is to evaluate if the tumor to cervical spinal cord standardized uptake ratio (SUR) can improve the reproducibility of a model to determine the metabolic tumor volume (MTV) on FDG-PET/CT in a multicenter setting.Materials and methods: Ninety-five radiotherapy planning FDG-PET/CT-scans of patients with head and neck cancer were analyzed using the Bland-Altman method to evaluate differences in FDG-uptake in the cervical spinal cord and the mediastinal blood pool. Non-linear regression analysis was used to determine the optimal MTV using the gross tumor volume (GTV) as ground truth and a spatial overlap-index as statistical validation metric. Reproducibility was evaluated using the Bland-Altman method and external validation was performed in an independent dataset consisting of 62 patients.Results: Bland-Altman's analyses demonstrated equivalence of FDG-uptake in the mediastinal blood pool and the cervical spinal cord. Reproducibility of the models improved when using SUR instead of SUV. These results were confirmed in the validation cohort.Conclusion: The use of the tumor to cervical spinal cord SUR instead of SUV improves the reproducibility of a model to determine the MTV on FDG-PET/CT in a multicenter setting. This study indicates that SUR may be preferred over SUV based approaches. (C) 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).