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Background and purpose: To improve cone-beam computed tomography (CBCT), deep-learning (DL)-models are being explored to generate synthetic CTs (sCT). The sCT evaluation is mainly focused on image quality and CT number accuracy. However, correct representation of daily anatomy of the CBCT is also important for sCTs in adaptive radiotherapy. The aim of this study was to emphasize the importance of anatomical correctness by quantitatively assessing sCT scans generated from CBCT scans using different paired and unpaired DL-models. Materials and methods: Planning CTs (pCT) and CBCTs of 56 prostate cancer patients were included to generate sCTs. Three different DL-models, Dual-UNet, Single-UNet and Cycle-consistent Generative Adversarial Network (CycleGAN), were evaluated on image quality and anatomical correctness. The image quality was assessed using image metrics, such as Mean Absolute Error (MAE). The anatomical correctness between sCT and CBCT was quantified using organs-at-risk volumes and average surface distances (ASD). Results: MAE was 24 Hounsfield Unit (HU) [range:19-30 HU] for Dual-UNet, 40 HU [range:34-56 HU] for Single-UNet and 41HU [range:37-46 HU] for CycleGAN. Bladder ASD was 4.5 mm [range:1.6–12.3 mm] for Dual-UNet, 0.7 mm [range:0.4–1.2 mm] for Single-UNet and 0.9 mm [range:0.4–1.1 mm] CycleGAN. Conclusions: Although Dual-UNet performed best in standard image quality measures, such as MAE, the contour based anatomical feature comparison with the CBCT showed that Dual-UNet performed worst on anatomical comparison. This emphasizes the importance of adding anatomy based evaluation of sCTs generated by DL-models. For applications in the pelvic area, direct anatomical comparison with the CBCT may provide a useful method to assess the clinical applicability of DL-based sCT generation methods.

Background and purpose: The Global Quality Assurance of Radiation Therapy Clinical Trials Harmonization Group (GHG) is a collaborative group of Radiation Therapy Quality Assurance (RTQA) Groups harmonizing and improving RTQA for multi-institutional clinical trials. The objective of the GHG OAR Working Group was to unify OAR contouring guidance across RTQA groups by compiling a single reference list of OARs in line with AAPM TG 263 and ASTRO, together with peer-reviewed, anatomically defined contouring guidance for integration into clinical trial protocols independent of the radiation therapy delivery technique. Materials and methods: The GHG OAR Working Group comprised of 22 multi-professional members from 6 international RTQA Groups and affiliated organizations conducted the work in 3 stages: (1) Clinical trial documentation review and identification of structures of interest (2) Review of existing contouring guidance and survey of proposed OAR contouring guidance (3) Review of survey feedback with recommendations for contouring guidance with standardized OAR nomenclature. Results: 157 clinical trials were examined; 222 OAR structures were identified. Duplicates, non-anatomical, non-specific, structures with more specific alternative nomenclature, and structures identified by one RTQA group were excluded leaving 58 structures of interest. 6 OAR descriptions were accepted with no amendments, 41 required minor amendments, 6 major amendments, 20 developed as a result of feedback, and 5 structures excluded in response to feedback. The final GHG consensus guidance includes 73 OARs with peer-reviewed descriptions (Appendix A). Conclusion: We provide OAR descriptions with standardized nomenclature for use in clinical trials. A more uniform dataset supports the delivery of clinically relevant and valid conclusions from clinical trials.

Introduction: Commonly used clinical models for survival prediction after stereotactic radiosurgery (SRS) for brain metastases (BMs) are limited by the lack of individual risk scores and disproportionate prognostic groups. In this study, two nomograms were developed to overcome these limitations.Methods: 495 patients with BMs of NSCLC treated with SRS for a limited number of BMs in four Dutch radiation oncology centers were identified and divided in a training cohort (n = 214, patients treated in one hospital) and an external validation cohort n = 281, patients treated in three other hospitals). Using the training cohort, nomograms were developed for prediction of early death (12 months) with prognostic factors for survival. Accuracy of prediction was defined as the area under the curve (AUC) by receiver operating characteristics analysis for prediction of early death and long term survival. The accuracy of the nomograms was also tested in the external validation cohort.Results: Prognostic factors for survival were: WHO performance status, presence of extracranial metastases, age, GTV largest BM, and gender. Number of brain metastases and primary tumor control were not prognostic factors for survival. In the external validation cohort, the nomogram predicted early death statistically significantly better (p <0.05) than the unfavorable groups of the RPA, DS-GPA, GGS, SIR, and Rades 2015 (AUC = 0.70 versus range AUCs = 0.51-0.60 respectively). With an AUC of 0.67, the other nomogram predicted 1 year survival statistically significantly better (p <0.05) than the unfavorable groups of four models (range AUCs = 0.57-0.61), except for the SIR (AUC = 0.64, p = 0.34). The models are available on www.predictcancer.org.Conclusion: The nomograms predicted early death and long-term survival more accurately than commonly used prognostic scores after SRS for a limited number of BMs of NSCLC. Moreover these nomograms enable individualized probability

Introduction: Commonly used clinical models for survival prediction after stereotactic radiosurgery (SRS) for brain metastases (BMs) are limited by the lack of individual risk scores and disproportionate prognostic groups. In this study, two nomograms were developed to overcome these limitations.Methods: 495 patients with BMs of NSCLC treated with SRS for a limited number of BMs in four Dutch radiation oncology centers were identified and divided in a training cohort (n = 214, patients treated in one hospital) and an external validation cohort n = 281, patients treated in three other hospitals). Using the training cohort, nomograms were developed for prediction of early death (12 months) with prognostic factors for survival. Accuracy of prediction was defined as the area under the curve (AUC) by receiver operating characteristics analysis for prediction of early death and long term survival. The accuracy of the nomograms was also tested in the external validation cohort.Results: Prognostic factors for survival were: WHO performance status, presence of extracranial metastases, age, GTV largest BM, and gender. Number of brain metastases and primary tumor control were not prognostic factors for survival. In the external validation cohort, the nomogram predicted early death statistically significantly better (p <0.05) than the unfavorable groups of the RPA, DS-GPA, GGS, SIR, and Rades 2015 (AUC = 0.70 versus range AUCs = 0.51-0.60 respectively). With an AUC of 0.67, the other nomogram predicted 1 year survival statistically significantly better (p <0.05) than the unfavorable groups of four models (range AUCs = 0.57-0.61), except for the SIR (AUC = 0.64, p = 0.34). The models are available on www.predictcancer.org.Conclusion: The nomograms predicted early death and long-term survival more accurately than commonly used prognostic scores after SRS for a limited number of BMs of NSCLC. Moreover these nomograms enable individualized probability

Introduction One of the goals of Quality Assurance in Radiotherapy (QART) is to reduce the variability and uncertainties related to treatment planning and beam delivery. The purpose of this study was to assess the outcome impact and cost-effectiveness (CE) of various QART levels for a head and neck (H&N) cancer study. Materials and methods QART levels were defined as: basic QART with a dummy run (level 2), level 2 plus prospective Individual Case Reviews (ICRs) for 15% of patients (level 3) and level 2 plus prospective ICRs for all patients (level 4). The follow-up of patients was modeled using a multi-state model with parameters derived from EORTC, TROG and RTOG prospective studies. Individual patient data, linking QART results with outcome, were retrieved from the TROG database. Results for each QART level were expressed as percentage of mortality and local failure at 5 years. Results Quality-of-life-adjusted and recurrence-free survival increased with increasing QART levels. The increase of all these metrics was more sizeable with an increased QART level from 2 or 3 to 4. The estimated quality-adjusted-life-years (QALYs) for an increase of QART levels of 3-4 and 2-4 were 0.09 and 0.15, respectively. The incremental CE ratio was €5525 and €3659 Euros per QALY for these QART levels. Compared to QART level 2 or 3, level 4 was cost-effective. Conclusions Increasing QART levels resulted in better patient outcome in this simulated study. The increased complexity of the QART program was also cost-effective. © 2014 Elsevier Ireland Ltd. All rights reserved.

Objective: Quality assurance (QA) in clinical trials is essential to ensure treatment is safely and effectively delivered. As QA requirements have increased in complexity in parallel with evolution of radiation therapy (RT) delivery, a need to facilitate digital data exchange emerged. Our objective is to present the platform developed for the integration and standardization of QART activities across all EORTC trials involving RT. Methods: The following essential requirements were identified: secure and easy access without on-site software installation; integration within the existing EORTC clinical remote data capture system; and the ability to both customize the platform to specific studies and adapt to future needs. After retrospective testing within several clinical trials, the platform was introduced in phases to participating sites and QART study reviewers. Results: The resulting QA platform, integrating RT analysis software installed at EORTC Headquarters, permits timely, secure, and fully digital central DICOM-RT based data review. Participating sites submit data through a standard secure upload webpage. Supplemental information is submitted in parallel through web-based forms. An internal quality check by the QART office verifies data consistency, formatting, and anonymization. QART reviewers have remote access through a terminal server. Reviewers evaluate submissions for protocol compliance through an online evaluation matrix. Comments are collected by the coordinating centre and institutions are informed of the results. Conclusions: This web-based central review platform facilitates rapid, extensive, and prospective QART review. This reduces the risk that trial outcomes are compromised through inadequate radiotherapy and facilitates correlation of results with clinical outcomes. © 2012 Elsevier Ireland Ltd. All rights reserved.

After the foundation of the EORTC in 1962, the Radio-Chemotherapy Group within this organization split in 1973 into two groups. One of these groups, concentrating on Hodgkin's and Non-Hodgkin's Lymphoma, later became the Lymphoma Group while the other became the Radiotherapy Group. During the 1990's the latter changed its name to the Radiation Oncology Group (ROG), underscoring its position within the field of multidisciplinary oncology research. By 2011 the ROG had initiated or participated in 83 clinical studies, of which more than 73% were randomized phase III trials. It has concentrated on almost every disease site from brain to gynecological tumors with emphasis on brain, head and neck, breast, prostate, and lower gastro-intestinal tumours. The ROG has published several hundreds peer-reviewed articles, including publications in prestigious journals such as the New England Journal of Medicine or Lancet Oncology. Since its foundation, the ROG has understood the importance of conducting a proper Radiotherapy Quality Assurance (RT-QA) program for every clinical trial aiming at guaranteeing the quality of radiotherapy, i.e. minimizing any uncertainties in the conduction of trials. As radiotherapy evolved from two-dimensional to Intensity Modulated Radio Therapy, this program has progressively matured with time to be part of a worldwide RT-QA consortium in 2012. © 2012 European Organisation for Research and Treatment of Cancer.

Objective: Quality assurance (QA) in clinical trials is essential to ensure treatment is safely and effectively delivered. As QA requirements have increased in complexity in parallel with evolution of radiation therapy (RT) delivery, a need to facilitate digital data exchange emerged. Our objective is to present the platform developed for the integration and standardization of QART activities across all EORTC trials involving RT. Methods: The following essential requirements were identified: secure and easy access without on-site software installation; integration within the existing EORTC clinical remote data capture system; and the ability to both customize the platform to specific studies and adapt to future needs. After retrospective testing within several clinical trials, the platform was introduced in phases to participating sites and QART study reviewers. Results: The resulting QA platform, integrating RT analysis software installed at EORTC Headquarters, permits timely, secure, and fully digital central DICOM-RT based data review. Participating sites submit data through a standard secure upload webpage. Supplemental information is submitted in parallel through web-based forms. An internal quality check by the QART office verifies data consistency, formatting, and anonymization. QART reviewers have remote access through a terminal server. Reviewers evaluate submissions for protocol compliance through an online evaluation matrix. Comments are collected by the coordinating centre and institutions are informed of the results. Conclusions: This web-based central review platform facilitates rapid, extensive, and prospective QART review. This reduces the risk that trial outcomes are compromised through inadequate radiotherapy and facilitates correlation of results with clinical outcomes. © 2012 Elsevier Ireland Ltd. All rights reserved.