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Introduction: Patient engagement is continuously being promoted by patients as well as politicians and healthcare professionals. One way of increasing patient engagement is by using shared decision-making (SDM), which is a joint effort of clinicians and patients making decisions together.When planning stereotactic body radiation therapy (SBRT) for a lung tumour located close to the thoracic wall, there are conflicting interests between (1) delivering the highest possible dose to obtain local tumour control and (2) reducing the dose to the thoracic wall to decrease the risk of chest wall pain and rib fractures following treatment. The radiation oncologist often makes the choice of dose without any engagement of the patient. We believe that the patients should be engaged in such a decision.To explore this matter, we have designed a randomised trial, 'SDM Lung SBRT', for which we present our study protocol with a special focus on a patient decision aid (PtDA), which is being tested in this trial., Methods and Analysis: This study includes patients with a lung tumour located ≤1 cm from the thoracic wall. Patients are randomised to have the primary consultation with or without use of the PtDA. Treatment options are a radiation dose of either 66 Gray (Gy) in three fractions, 45 Gy in three fractions or no treatment. The primary outcome is patient engagement in decision-making measured by the validated observer-rated OPTION-12 score. Secondary outcomes are patient-reported outcomes, quality of life and side effects following treatment., Ethics and Dissemination: All patients give informed consent to participate. According to Danish legislation, ethical approval is not required for this study as studies using questionnaires, observations or other non-biological studies are not considered interventions according to the Committee Act. Results from this study will be presented at scientific meetings and published in English peer-reviewed journals., Trial Registration Number: ClinicalTrials.gov (NCT04940936)., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.)

Background: Robust optimization has been suggested as an approach to reduce the irradiated volume in lung Stereotactic Body Radiation Therapy (SBRT). We performed a retrospective planning study to investigate the potential benefits over Planning Target Volume (PTV)-based planning., Material and Methods: Thirty-nine patients had additional plans using robust optimization with 5-mm isocenter shifts of the Gross Tumor Volume (GTV) created in addition to the PTV-based plan used for treatment. The optimization included the mid-position phase and the extreme breathing phases of the 4D-CT planning scan. The plans were compared for tumor coverage, isodose volumes, and doses to Organs At Risk (OAR). Additionally, we evaluated both plans with respect to observed tumor motion using the peak tumor motion seen on the planning scan and cone-beam CTs., Results: Statistically significant reductions in irradiated isodose volumes and doses to OAR were achieved with robust optimization, while preserving tumor dose. The reductions were largest for the low-dose volumes and reductions up to 188 ccm was observed. The robust evaluation based on observed peak tumor motion showed comparable target doses between the two planning methods. Accumulated mean GTV-dose was increased by a median of 4.46 Gy and a non-significant increase of 100 Monitor Units (MU) was seen in the robust optimized plans., Interpretation: The robust plans required more time to prepare, and while it might not be a feasible planning strategy for all lung SBRT patients, we suggest it might be useful for selected patients.

Introduction: It is an ongoing debate how much lung and heart irradiation impact overall survival (OS) after definitive radiotherapy for lung cancer. This study uses a large national cohort of patients with locally advanced NSCLC to investigate the association between OS and irradiation of lung and heart., Methods: Treatment plans were acquired from six Danish radiotherapy centers, and patient characteristics were obtained from national registries. A hybrid segmentation tool automatically delineated the heart and substructures. Dose-volume histograms for all structures were extracted and analyzed using principal component analyses (PCAs). Parameter selection for a multivariable Cox model for OS prediction was performed using cross-validation based on bootstrapping., Results: The population consisted of 644 patients with a median survival of 26 months (95% confidence interval [CI]: 24-29). The cross-validation selected two PCA variables to be included in the multivariable model. PCA1 represented irradiation of the heart and affected OS negatively (hazard ratio, 1.14; 95% CI: 1.04-1.26). PCA2 characterized the left-right balance (right atrium and left ventricle) irradiation, showing better survival for tumors near the right side (hazard ratio, 0.92; 95% CI: 0.84-1.00). Besides the two PCA variables, the multivariable model included age, sex, body-mass index, performance status, tumor dose, and tumor volume., Conclusions: Besides the classic noncardiac risk factors, lung and heart doses had a negative impact on survival, while it is suggested that the left side of the heart is a more radiation dose-sensitive region. The data indicate that overall heart irradiation should be reduced to improve the OS if possible., Competing Interests: The authors declare no conflict of interest., (© 2024 The Authors.)

Background and Purpose: Irradiation of the heart in thoracic cancers raises toxicity concerns. For accurate dose estimation, automated heart and substructure segmentation is potentially useful. In this study, a hybrid automatic segmentation is developed. The accuracy of delineation and dose predictions were evaluated, testing the method's potential within heart toxicity studies., Materials and Methods: The hybrid segmentation method delineated the heart, four chambers, three large vessels, and the coronary arteries. The method consisted of a nnU-net heart segmentation and partly atlas- and model-based segmentation of the substructures. The nnU-net training and atlas segmentation was based on lung cancer patients and was validated against a national consensus dataset of 12 patients with breast cancer. The accuracy of dose predictions between manual and auto-segmented heart and substructures was evaluated by transferring the dose distribution of 240 previously treated lung cancer patients to the consensus data set., Results: The hybrid auto-segmentation method performed well with a heart dice similarity coefficient (DSC) of 0.95, with no statistically significant difference between the automatic and manual delineations. The DSC for the chambers varied from 0.78-0.86 for the automatic segmentation and was comparable with the inter-observer variability. Most importantly, the automatic segmentation was as precise as the clinical experts in predicting the dose distribution to the heart and all substructures., Conclusion: The hybrid segmentation method performed well in delineating the heart and substructures. The prediction of dose by the automatic segmentation was aligned with the manual delineations, enabling measurement of heart and substructure dose in large cohorts. The delineation algorithm will be available for download., 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.)