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Primary radiotherapy treatment is preferred for squamous cell carcinomas in the head and neck region (HNSCC) owing to the preservation of anatomical structures and function. The delineation of treatment volumes, particularly the expansion from the gross tumour volume (GTV) to the high-dose clinical target volume (CTV1), is critical in radiotherapy planning. Historically, the choice of margin size for GTV-CTV1 has varied between different treatment centres in Denmark from zero to over 10 millimetres or could encompass the whole organ (volume) where the tumour is located. Therefore, the first DAHANCA IMRT guidelines could be referred to as volumetric-based. In 2013, national consensus guidelines were introduced by the Danish Head and Neck Cancer Group (DAHANCA), recommending an isotropic margin of five millimetres corrected only for air or natural anatomical barriers resulting in a more uniform geometrical margin. With over a decade of experience in applying geometrically-based GTV-CTV1 guidelines, the primary aim of this study was to assess whether the implementation of the guidelines in 2013 led to improved consistency in CTV1 volumes across different treatment centres. Additionally, we analysed whether changes in GTV-CTV1 margins moving from volumetric to geometric guidelines impacted 3-year local tumour control and local recurrence pattern in patients treated three years before and three years after implementation of the geometrical guidelines. The cohort consisted of 1,948 patients diagnosed with oro-/hypopharyngeal and laryngeal squamous cell carcinomas across three national treatment centres. The patients underwent definitive intensity-modulated radiotherapy (IMRT) in 2010-2012 when volumetric guidelines were used, and in 2013-2015, when geometric guidelines were adopted. The GTV-CTV1 margins were quantitatively assessed by calculating the median surface distance from the primary GTV to CTV1. Three-year local control (LC) rates were determined, and local recurrence patterns were examined (Figure 1) using a centre of mass (COM) analysis. Hazard ratios and corresponding 90% CIs were calculated using the Cox proportional hazard model to identify factors that influenced local tumour control. [Display omitted] The transition from volumetric to geometric guidelines substantially reduced median GTV-CTV1 margins, declining from a median of 9.0 mm (IQR 0.0-9.7) to 4.7 mm (IQR 4.0-5.5). Importantly, adopting geometric guidelines led to increased consistency in CTV1 volumes between treatment centres. Median CTV1 changed from 76 to 61 cm3 for Centre1, from 28 to 53 cm3 for Centre2, and from 42 to 62 cm3 for Centre3 for the periods 2010-2012 and 2013-2015, respectively. The three-year LC rates exhibited a notable trend towards improvement, increasing from 0.84 to 0.87, although statistical significance was not reached (p=0.06). Cox regression analysis indicated that factors such as sex (female), tumour site (HPV p16+ oropharyngeal), T stage (T1-2), treatment schedule (6 or 10 fractions per week), and the administration of nimorazole and chemotherapy were associated with superior three-year LC. At the same time, GTV-CTV1 margin size did not appear to influence local control significantly (Figure 2). [Display omitted] Out of 146 radiology-verified local recurrences (LR), 102 (70%) were inside the CTV1. 45 (63%) and 57 (76%) LRs were inside CTV1 in 2010-2012 and 2013-2015, respectively. The LRs were covered by the 95% isodose in 75% in 2010-2012 and 91% in 2013-2015. The implementation of geometrically-based GTV-CTV1 margins resulted in reduced variation in treatment volumes across 3 different centres. Although our analysis did not demonstrate a direct impact of GTV-CTV1 margin size on local tumour control, a promising trend towards improved local control was observed in the newest cohort. Most recurrences occurred within CTV1 and were encompassed by the prescribed dose. A relevant next step could involve randomised trials comparing standard radiotherapy protocols with reduced margins and escalated doses to CTV1, further exploring the potential benefits of geometrically-based treatment strategies. [ABSTRACT FROM AUTHOR]

In the Danish Head and Neck Cancer Group (DAHANCA) 35 trial, patients are selected for proton treatment based on simulated reductions of Normal Tissue Complication Probability (NTCP) for proton compared to photon treatment at the referring departments. After inclusion in the trial, immobilization, scanning, contouring and planning are repeated at the national proton centre. The new contours could result in reduced expected NTCP gain of the proton plan, resulting in a loss of validity in the selection process. The present study evaluates if contour consistency can be improved by having access to AI (Artificial Intelligence) based contours. The 63 patients in the DAHANCA 35 pilot trial had a CT from the local DAHANCA centre and one from the proton centre. A nationally validated convolutional neural network, based on nnU-Net, was used to contour OARs on both scans for each patient. Using deformable image registration, local AI and oncologist contours were transferred to the proton centre scans for comparison. Consistency was calculated with the Dice Similarity Coefficient (DSC) and Mean Surface Distance (MSD), comparing contours from AI to AI and oncologist to oncologist, respectively. Two NTCP models were applied to calculate NTCP for xerostomia and dysphagia. The AI contours showed significantly better consistency than the contours by oncologists. The median and interquartile range of DSC was 0.85 [0.78 − 0.90] and 0.68 [0.51 − 0.80] for AI and oncologist contours, respectively. The median and interquartile range of MSD was 0.9 mm [0.7 − 1.1] mm and 1.9 mm [1.5 − 2.6] mm for AI and oncologist contours, respectively. There was no significant difference in Δ NTCP. The study showed that OAR contours made by the AI algorithm were more consistent than those made by oncologists. No significant impact on the Δ NTCP calculations could be discerned. [ABSTRACT FROM AUTHOR]

Background: Previously, many radiotherapy (RT) trials were based on a few selected dose measures. Many research questions, however, rely on access to the complete dose information. To support such access, a national RT plan database was created. The system focuses on data security, ease of use, and re-use of data. This article reports on the development and structure, and the functionality and experience of this national database. Methods and materials: A system based on the DICOM-RT standard, DcmCollab, was implemented with direct connections to all Danish RT centres. Data is segregated into any number of collaboration projects. User access to the system is provided through a web interface. The database has a finely defined access permission model to support legal requirements. Results: Currently, data for more than 14,000 patients have been submitted to the system, and more than 50 research projects are registered. The system is used for data collection, trial quality assurance, and audit data set generation. Results: Users reported that the process of submitting data, waiting for it to be processed, and then manually attaching it to a project was resource intensive. This was accommodated with the introduction of triggering features, eliminating much of the need for users to manage data manually. Many other features, including structure name mapping, RT plan viewer, and the Audit Tool were developed based on user input. Conclusion: The DcmCollab system has provided an efficient means to collect and access complete datasets for multi-centre RT research. This stands in contrast with previous methods of collecting RT data in multi-centre settings, where only singular data points were manually reported. To accommodate the evolving legal environment, DcmCollab has been defined as a 'data processor', meaning that it is a tool for other research projects to use rather than a research project in and of itself. [ABSTRACT FROM AUTHOR]

A high-quality treatment plan aims to best achieve the clinical prescription, balancing high target dose to maximise tumour control against sufficiently low organ-at-risk dose for acceptably low toxicity. Treatment planning (TP) includes multiple steps from simulation/imaging and segmentation to technical plan production and reporting. Consistent quality across this process requires close collaboration and communication between clinical and technical experts, to clearly understand clinical requirements and priorities and also practical uncertainties, limitations and compromises. TP quality depends on many aspects, starting from commissioning and quality management of the treatment planning system (TPS), including its measured input data and detailed understanding of TPS models and limitations. It requires rigorous quality assurance of the whole planning process and it links to plan deliverability, assessable by measurement-based verification. This review highlights some factors influencing plan quality, for consideration for optimal plan construction and hence optimal outcomes for each patient. It also indicates some challenges, sources of difference and current developments. The topics considered include: the evolution of TP techniques; dose prescription issues; tools and methods to evaluate plan quality; and some aspects of practical TP. The understanding of what constitutes a high-quality treatment plan continues to evolve with new techniques, delivery methods and related evidence-based science. This review summarises the current position, noting developments in the concept and the need for further robust tools to help achieve it. [ABSTRACT FROM AUTHOR]

In head and neck cancer, distant metastases may be present at diagnosis (M1) or occur after treatment (DM). It is unknown whether M1 and DM follow the same clinical development and share prognosis, as population-based studies regarding outcomes are scarce. Therefore, we investigated the incidence, location of metastases and overall survival of patients with M1 and DM. Patients diagnosed with squamous cell carcinoma of the pharynx and larynx in Denmark 2008–2017 were identified in the Danish Head and Neck Cancer Group (DAHANCA) database. We identified 7300 patients, of whom 197 (3%) had M1 and 498 (8%) developed DM during follow-up. The 5-year cumulative incidence of DM was 8%. 1- and 2-year overall survival for DM (27% and 13%) vs. M1 (28% and 9%) were equally poor. There was no significant difference in location of metastases for M1 and DM and the most frequently involved organs were lungs, bone, lymph nodes and liver, in descending order. In oropharyngeal squamous cell carcinomas, the location of metastases did not differ by p16-status. For p16-positive patients, 21% of DM occurred later than three years of follow-up compared to 7% of p16-negative patients. Incidence, location of metastases and prognosis of primary metastatic (M1) or post-treatment metastatic (DM) disease in pharyngeal and laryngeal squamous cell carcinoma are similar in this register-based study [ABSTRACT FROM AUTHOR]

Deformable image registration (DIR) and contour propagation are used in daily online adaptation for hybrid MRI linac (MRL) treatments. The accuracy of the propagated contours may vary depending on the chosen workflow (WF), affecting the amount of required manual corrections. This study investigated the impact of three different WFs of contour propagations produced by a clinical treatment planning system for a high-field MRL on head and neck cancer patients. Seventeen patients referred for curative radiotherapy for oropharyngeal cancer underwent standard CT-based dose planning and MR scans in the treatment position for planning (pMR), and at the 10th (MR10), 20th (MR20) and 30th (MR30) fraction (±2). The primary tumour, a metastatic lymph node and 8 organs at risk were manually delineated on each set of T2 weighted images. Delineations were repeated one month later on the pMR by the same observer to determine the intra-observer variation (IOV). Three WFs were used to deform images in the treatment planning system for the high-field MRL: In WF1, only the planning image and contours were used as a reference for DIR and propagation to MR10,20,30. The most recently acquired image set prior to the daily images was deformed and uncorrected (WF2) versus manually corrected (WF3) structures propagated to the session image. Dice similarity coefficient (DSC), mean surface distance (MSD) and Hausdorff distance (HD) were calculated for each structure in each model. Population median DSC, MSD and HD for WF1 and WF3 were similar and slightly better than for WF2. WF3 provided higher accuracy than WF1 for structures that are likely to shrink. All DIR workflows were less accurate than the IOV. WF1 and WF3 provide higher accuracy in structure propagation than WF2. Manual revision and correction of propagated structures are required for all evaluated workflows. [ABSTRACT FROM AUTHOR]

Introduction:A significant part of patients with head and neck squamous cell carcinoma (HNSCC) develop recurrent disease after curative radiotherapy. We aimed to analyze loco-regional recurrence pattern by identifying possible points of recurrence origin by three different methods in relation to treatment volumes. Material and methods:A total of 455 patients completed IMRT-based treatment for HNSCC from 2006 to 2012. A total of 159 patients had remaining cancer after IMRT, developed loco-regional recurrence or distant disease during follow-up. Among those, 69 patients with loco-regional recurrences had affirmative CT or PET/CT scan. Possible points of origin (POs) of the recurrences were identified on scans by two independent observers, estimated as center of mass and as maximum surface distance. The recurrence position was analyzed in relation to high-dose treatment volume (CTV1) and 95% of prescription dose. Results:In total, 104 loco-regional recurrences (54 in T-site and 50 in N-site) were identified in 69 patients. Median time to recurrence for the 69 patients was 10 months. No clinically relevant difference was found between the four POs, with standard deviation between POs inx,yandzaxes of 3, 3 and 6 mm. For recurrences inside CTV1, 0–5 mm and 5–10 mm outside CTV1 the standard deviation of dose differences between the POs were 1, 1.4 and 1 Gy, respectively. 56% and 25% of T-site and N-site recurrences were inside CTV1, respectively. Coverage by 95% prescription dose to high-dose treatment volume was achieved in 78% of T-site and 39% of N-site recurrences. Conclusions:For recurrences identified by possible points of recurrence origin, no significant difference between observer-based or mathematically estimated methods was found. More than half of T-site recurrences were inside high-dose treatment volume, whereas N-site recurrences were mainly outside. [ABSTRACT FROM AUTHOR]

Background:The quality of radiotherapy planning has improved substantially in the last decade with the introduction of intensity modulated radiotherapy. The purpose of this study was to analyze the plan quality and efficacy of automatically (AU) generated VMAT plans for inoperable esophageal cancer patients. Material and Methods:Thirty-two consecutive inoperable patients with esophageal cancer originally treated with manually (MA) generated volumetric modulated arc therapy (VMAT) plans were retrospectively replanned using an auto-planning engine. All plans were optimized with one full 6MV VMAT arc giving 60 Gy to the primary target and 50 Gy to the elective target. The planning techniques were blinded before clinical evaluation by three specialized oncologists. To supplement the clinical evaluation, the optimization time for the AU plan was recorded along with DVH parameters for all plans. Results:Upon clinical evaluation, the AU plan was preferred for 31/32 patients, and for one patient, there was no difference in the plans. In terms of DVH parameters, similar target coverage was obtained between the two planning methods. The mean dose for the spinal cord increased by 1.8 Gy using AU (p = .002), whereas the mean lung dose decreased by 1.9 Gy (p < .001). The AU plans were more modulated as seen by the increase of 12% in mean MUs (p = .001). The median optimization time for AU plans was 117 min. Conclusions:The AU plans were in general preferred and showed a lower mean dose to the lungs. The automation of the planning process generated esophageal cancer treatment plans quickly and with high quality. [ABSTRACT FROM AUTHOR]

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Purpose Radiation-induced mucositis is a serious side effect, which can jeopardize treatment compliance and influence patient weight during treatment. The aim of this study was to develop a mathematical model to predict the risk of severe mucositis. Methods 535 curative radiotherapy H&N cancer patients from one institution between 2011 and 2015 were included. Doses were 66, 68 or 76 Gy in 33, 34 or 56fx. Patients were treated with IMRT/VMAT and mucosal reactions were scored weekly during radiotherapy. The highest observed score was used as endpoint and dichotomised in stage two stages: milder than confluent mucositis vs. confluent mucositis and worse. DVH of the extended oral cavity (Brower et al.) was extracted from the TPS. Principal component analysis was used to uncouple the highly correlated dose metrics ( V 5 , … , V 75 ) . Predictors available for the logistic model were the first 5 principal dose components, gender, weekly low dose chemotherapy, radiosensitizer, treatment acceleration, age, smoking status, tumour site, and volume of extended oral cavity. Parameter selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) within the statistical package R. The LASSO tuning parameter was chosen using 10-fold cross validation and 95% confidence interval found with 2000 bootstraps. Results Gender, acceleration, current smoker, tumour in the vicinity of the oral cavity, radiosensitizer, and the two first principal dose components were selected as predictors using Lasso. Acceleration is a well-known risk factor while the tumour position indicates an increased risk beyond the prediction related to the oral cavity dose. The protective value of being male and current smoker is in line with previous findings of toxicity in oesophagus and lung. The risk related to dose is dominated by the PC1. The model calibration plot (predicted vs observed risk) show good agreement with the line of identity. The bootstrap adjusted area under the curve (AUC) was 0.77 (95% CI 0.73–0.81). Conclusions A robust logistic regression model for prediction of radiation induced mucositis of H&N has been developed, which can be used as risk assessment of mucosal toxicity during treatment plan optimisation. The AUC value of 0.77 is significantly larger than previous published models on mucositis. [ABSTRACT FROM AUTHOR]

The aim in radiotherapy treatment planning is to have sufficient target coverage and as low a dose to the Organs at Risk (OARs) as possible, adhering to the relevant guidelines. A high and consistent radiotherapy plan quality is vital when treatment plans are used as the foundation for patient selection in clinical trials. Proton therapy, being a substantially newer treatment modality than conventional photon therapy, is at risk of having a steeper learning curve in treatment planning. This inequality is important to investigate in a clinical study comparing the two, as this could influence the trial results. This study aims to evaluate the development of radiotherapy treatment plan quality for head and neck cancer patients receiving photon and proton therapy over time in the context of the DAHANCA 35 trial. From May 2019 to June 2023,189 patients were included in the ongoing DAHANCA 35 trial, with 63 patients in the pilot phase and 126 in the subsequent randomisation phase. In the pilot phase, all included patients were offered proton treatment, and in the randomisation phase, patients were randomised 1:2 (photon:proton). Patients were first seen at a local treatment centre, where a photon and comparative proton plan were prepared. If patients were offered proton treatment, a new clinical proton plan was made at the proton treatment centre and subsequently used for treatment. This study analysed 189 photon plans, 189 comparative proton plans, and 140 clinical proton plans. The treatment plans were prepared conforming to the DAHANCA guidelines [1] to ensure the clinical relevance of all treatment plans The plan quality was assessed separately for photon plans, comparative proton plans, and clinical proton plans in three time intervals. The mean dose was investigated individually for 13 OARs relevant for head and neck cancer: oesophagus, glottic larynx, supraglottic larynx, mandible, extended oral cavity, left and right parotid glands, upper-, middle-, and lower pharyngeal constrictor muscles, left and right submandibular glands, and thyroid gland. Furthermore, treatment plan quality was analysed using a new metric called Normalised Toxicity Index (NTI), calculated as a normalised average of the mean dose to the OARs compared to the threshold mean dose recommended by the DAHANCA guidelines. An NTI > 1 indicated that the OARs, on average, received a dose higher than the recommended thresholds, and an NTI < 1 indicated that the OARs received a dose below the thresholds. Hence, a lower NTI indicated better plan quality concerning OAR doses. The Kruskal-Wallis test was used to investigate a potential difference in the intervals for mean dose and NTI for each treatment type. The significance level was Bonferroni adjusted to account for multiple testing. The three time intervals were defined with 63 patients in the pilot phase constituting one interval (Pilot phase), the subsequent 64 patients from the randomisation phase in the next interval (Randomisation 1), and the remaining 62 patients from the randomisation phase in the third interval (Randomisation 2). The periods were 22 months for the Pilot phase, 19 months for Randomisation 1, and 14 months for Randomisation 2. Across the 13 OARs, the mean dose to individual OARs did not show a general time-dependent change, except for the right parotid gland in the clinical proton plans. Figure 1 shows a box plot with samples overlaid for the mean dose to the extended oral cavity as an example of the OARs. [Display omitted] The NTI was not significantly different for the photon plans, comparative proton plans, and clinical proton plans in the three consecutive intervals, as shown in Figure 2. The median NTI for the clinical proton plans was 0.88 (interquartile range [0.70,1.00]) for the Pilot phase, 0.83 [0.75,0.89] for Randomization 1, and 0.79 [0.67,0.98] for Randomization 2. The plan quality of the clinical proton plans appears stable from this new NTI metric. [Display omitted] The analyses conducted in this study did not show a general time-dependent change in plan quality in any of the three types of plans. This could be caused by the nationally developed proton treatment planning template. A stable treatment plan quality can help ensure a consistent selection for clinical trials, thus providing transparency for analysis of the outcome of the trials. The plan quality will continuously be followed to ensure consistency. [ABSTRACT FROM AUTHOR]

• OS of larynx cancer patients treated at Odense DK, Manchester UK and Liverpool AUS is driven by tumour volume and PS. • Federated learning can be used to create survival models without patient-sensitive data leaving the individual institutions. • The baseline hazards of the three institutions are similar, indicating the GTV volume and PS explain the cohort differences. • Smoking during treatment has the same risk profile as a ten year older patient not smoking. Federated learning has the potential to perfrom analysis on decentralised data; however, there are some obstacles to survival analyses as there is a risk of data leakage. This study demonstrates how to perform a stratified Cox regression survival analysis specifically designed to avoid data leakage using federated learning on larynx cancer patients from centres in three different countries. Data were obtained from 1821 larynx cancer patients treated with radiotherapy in three centres. Tumour volume was available for all 786 of the included patients. Parameter selection among eleven clinical and radiotherapy parameters were performed using best subset selection and cross-validation through the federated learning system, AusCAT. After parameter selection, β regression coefficients were estimated using bootstrap. Calibration plots were generated at 2 and 5-years survival, and inner and outer risk groups' Kaplan-Meier curves were compared to the Cox model prediction. The best performing Cox model included log(GTV), performance status, age, smoking, haemoglobin and N-classification; however, the simplest model with similar statistical prediction power included log(GTV) and performance status only. The Harrell C-indices for the simplest model were for Odense, Christie and Liverpool 0.75[0.71–0.78], 0.65[0.59–0.71], and 0.69[0.59–0.77], respectively. The values are slightly higher for the full model with C-index 0.77[0.74–0.80], 0.67[0.62–0.73] and 0.71[0.61–0.80], respectively. Smoking during treatment has the same hazard as a ten-years older nonsmoking patient. Without any patient-specific data leaving the hospitals, a stratified Cox regression model based on data from centres in three countries was developed without data leakage risks. The overall survival model is primarily driven by tumour volume and performance status. [ABSTRACT FROM AUTHOR]

• Open-source distributed learning can validate survival models without sharing patient-sensitive data. • The MAASTRO model can predict the survival of larynx cancer patients treated at Odense and The Christie. • The absolute survival prediction requires a recalibration factor of 0.78 to predict survival accurately. • The baseline hazard between the two institutions is significantly different. • Additional parameters are needed to improve survival prediction. Prediction models are useful to design personalised treatment. However, safe and effective implementation relies on external validation. Retrospective data are available in many institutions, but sharing between institutions can be challenging due to patient data sensitivity and governance or legal barriers. This study validates a larynx cancer survival model performed using distributed learning without any sensitive data leaving the institution. Open-source distributed learning software based on a stratified Cox proportional hazard model was developed and used to validate the Egelmeer et al. MAASTRO survival model across two hospitals in two countries. The validation optimised a single scaling parameter multiplied by the original predicted prognostic index. All analyses and figures were based on the distributed system, ensuring no information leakage from the individual centres. All applied software is provided as freeware to facilitate distributed learning in other institutions. 1745 patients received radiotherapy for larynx cancer in the two centres from Jan 2005 to Dec 2018. Limiting to a maximum of one missing value in the parameters of the survival model reduced the cohort to 1095 patients. The Harrell C-index was 0.74 (CI95%, 0.71–0.76) and 0.70 (0.66–0.75) for the two centres. However, the model needed a scaling update. In addition, it was found that survival predictions of patients undergoing hypofractionation were less precise. Open-source distributed learning software was able to validate, and suggest a minor update to the original survival model without central access to patient sensitive information. Even without the update, the original MAASTRO survival model of Egelmeer et al. performed reasonably well, providing similar results in this validation as in its original validation [ABSTRACT FROM AUTHOR]