The Assessment of the Long-Term Impact of Radiotherapy on Biophysical Skin Properties in Patients after Head and Neck Cancer.

Background and Objectives: Chronic radiotherapy-induced skin injury (cRISI) is an irreversible and progressive condition that can significantly impact a patient's quality of life. Despite the limited literature available on the assessment of the epidermal barrier in cRISI, there is a consensus that appropriate skincare, including the use of emollients, is the primary therapeutic approach for this group of patients. The aim of this study was to evaluate the biophysical properties of the skin during the late period (at least 90 days) following radiation therapy (RT) for head and neck cancer. Materials and Methods: This was a single-center prospective non-randomized study. It involved the analysis of 16 adult patients with head and neck cancer who underwent RT at the Greater Poland Cancer Center, along with 15 healthy volunteers. The study and control groups were matched for gender and age (p = 0.51). Clinical assessment, based on the LENT-SOMA scale, was conducted for all patients. Evaluation of the skin's biophysical properties included: an analysis of transepidermal water loss (TEWL), stratum corneum hydration (SCH), and skin visualization using high-frequency ultrasonography (HF-USG). Results: A significantly higher TEWL was observed in the irradiated area compared to the control area in the study group (p = 0.004). However, there was no statistically significant difference in SCH (p = 0.073). Additionally, no significant difference was observed in the values of TEWL and SCH in the irradiated area between the group of patients with and without clinically obvious RISI (p = 0.192 and p = 0.415, respectively). The skin thickness of the irradiated area, assessed by HF-USG, did not differ significantly from the skin thickness of the control area (p = 0.638). Furthermore, no difference in skin thickness was observed in patients with clinical features of cRISI in the irradiated and control areas (p = 0.345). The mean time after RT was 6.1 years. Conclusions: This study marks the first demonstration of epidermal barrier damage in patients in the long term following RT for head and neck cancer. The impairment of the epidermal barrier was observed independently of evident cRISI features. This observation underscores the necessity to recommend appropriate skin care, including the use of emollients, for all patients following RT. We also suggest that HF-USG examination is generally inconclusive in determining the degree of skin damage in the late period after RT.

Significance of neutrophil to lymphocyte ratio as a predictor of outcome in head and neck cancer treated with definitive chemoradiation.

Background: The role of host immune system in carcinogenesis and response to treatment is increasingly studied, including predictive potential of circulating neutrophils and lymphocytes. The objective of the study was to evaluate the prognostic value of pre- and post-treatment neutrophil-to-lymphocyte (NLR) for treatment outcome in patients diagnosed with squamous cell carcinoma of head and neck (HNSCC) treated with definitive chemoradiation. Materials and methods: Electronic medical records of patients were evaluated and NLR was calculated. Cox regression was used to assess the impact of selected variables on overall survival (OS), disease specific survival (DSS), progression free survival (PFS) and distant failure free survival (DFFS). Logistic regression was used to estimate odds ratios of complete response with NLR. Results: 317 patients' records were included in the study. Increases in both pre-and post-NLR were associated with decreased OS in univariable analysis [hazard ratio (HR): 2.26 (1.25-4.07), p = 0.0068 and HR: 1.57 (1.03-2.37), p = 0.035 respectively). Post-NLR remained significant for OS in multivariable analysis [HR: 1.93 (1.22-3.1), p = 0.005] as well as for unfavorable DSS [HR: 2.31 (1.22-4.4), p = 0.01]. Pre-treatment NLR and nodal status correlated with shorter DFFS in multivariable analysis [HR 4.1 (1.14-14), p = 0.03 and HR 5.3: (1.62-18), p = 0.0062, respectively]. Strong correlation of increased both pre- and post-NLR with probability of clinical tumor response (CR) was found [odds ratio (OR): 0.23 (0.08-0.6), p = 0.003, and OR: 0.39 (0.2-0.8), p = 0.01 respectively]. Conclusion: NLR evaluated before and post treatment was a strong predictor of unfavorable treatment outcome and can be used for risk evaluation and clinical decision about treatment and post-treatment surveillance.

Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics.

Artificial intelligence (AI) and machine learning (ML) are becoming critical in developing and deploying personalized medicine and targeted clinical trials. Recent advances in ML have enabled the integration of wider ranges of data including both medical records and imaging (radiomics). However, the development of prognostic models is complex as no modeling strategy is universally superior to others and validation of developed models requires large and diverse datasets to demonstrate that prognostic models developed (regardless of method) from one dataset are applicable to other datasets both internally and externally. Using a retrospective dataset of 2,552 patients from a single institution and a strict evaluation framework that included external validation on three external patient cohorts (873 patients), we crowdsourced the development of ML models to predict overall survival in head and neck cancer (HNC) using electronic medical records (EMR) and pretreatment radiological images. To assess the relative contributions of radiomics in predicting HNC prognosis, we compared 12 different models using imaging and/or EMR data. The model with the highest accuracy used multitask learning on clinical data and tumor volume, achieving high prognostic accuracy for 2-year and lifetime survival prediction, outperforming models relying on clinical data only, engineered radiomics, or complex deep neural network architecture. However, when we attempted to extend the best performing models from this large training dataset to other institutions, we observed significant reductions in the performance of the model in those datasets, highlighting the importance of detailed population-based reporting for AI/ML model utility and stronger validation frameworks. We have developed highly prognostic models for overall survival in HNC using EMRs and pretreatment radiological images based on a large, retrospective dataset of 2,552 patients from our institution.Diverse ML approaches were used by independent investigators. The model with the highest accuracy used multitask learning on clinical data and tumor volume.External validation of the top three performing models on three datasets (873 patients) with significant differences in the distributions of clinical and demographic variables demonstrated significant decreases in model performance. Significance: ML combined with simple prognostic factors outperformed multiple advanced CT radiomics and deep learning methods. ML models provided diverse solutions for prognosis of patients with HNC but their prognostic value is affected by differences in patient populations and require extensive validation.

A critical appraisal of the four systematic reviews and meta-analysis on stereotactic body radiation therapy versus external beam radiotherapy for painful bone metastases and where we go from here.

Radiotherapy is an important treatment modality for pain control in patients with bone metastases. Stereotactic body radiation therapy (SBRT), which allows delivering a much higher dose per fraction while sparing critical structures compared to conventional external beam radiotherapy (cEBRT), has become more widely used, especially in the oligometastatic setting. Randomized controlled trials (RCTs) comparing the pain response rate of SBRT and cEBRT for bone metastases have shown conflicting results, as have four recent systematic reviews with meta-analyses of these trials. Possible reasons for the different outcomes between these reviews include differences in methodology, which trials were included, and the endpoints examined and how they were defined. We suggest ways to improve analysis of these RCTs, particularly performing an individual patient-level meta-analysis since the trials included heterogeneous populations. The results of such studies will help guide future investigations needed to validate patient selection criteria, optimize SBRT dose schedules, include additional endpoints (such as the time to onset of pain response, durability of pain response, quality of life (QOL), and side effects of SBRT), and better assess the cost-effectiveness and trade-offs of SBRT compared to cEBRT. An international Delphi consensus to guide selection of optimal candidates for SBRT is warranted before more prospective data is available.

Appropriateness of the 30-day expected mortality metric in palliative radiation treatment: a narrative review.

BACKGROUND AND OBJECTIVE: The 30-day expected mortality rate is frequently used as a metric to determine which patients benefit from palliative radiation treatment (RT). We conducted a narrative review to examine whether its use as a metric might be appropriate for patient selection. METHODS: A literature review was conducted to identify relevant studies that highlight the benefits of palliative RT in timely symptom management among patients with a poor performance status, the accuracy of predicting survival near the end of life and ways to speed up the process of RT administration through rapid response clinics. KEY CONTENT AND FINDINGS: Several trials have demonstrated substantial response rates for pain and/or bleeding by four weeks and sometimes within the first two weeks after RT. Models of patient survival have limited accuracy, particularly for predicting whether patients will die within the next 30 days. Dedicated Rapid Access Palliative RT (RAPRT) clinics, in which patients are assessed, simulated and treated on the same day, reduce the number of patient visits to the radiation oncology department and hence the burden on the patient as well as costs. CONCLUSIONS: Single-fraction palliative RT should be offered to eligible patients if they are able to attend treatment and could potentially benefit from symptom palliation, irrespective of predicted life expectancy. We discourage the routine use of the 30-day mortality as the only metric to decide whether to offer RT. More common implementation of RAPRT clinics could result in a significant benefit for patients of all life expectancies, but particularly those having short ones.

A convolutional neural network with self-attention for fully automated metabolic tumor volume delineation of head and neck cancer in [Formula: see text]F]FDG PET/CT.

PURPOSE: PET-derived metabolic tumor volume (MTV) and total lesion glycolysis of the primary tumor are known to be prognostic of clinical outcome in head and neck cancer (HNC). Including evaluation of lymph node metastases can further increase the prognostic value of PET but accurate manual delineation and classification of all lesions is time-consuming and prone to interobserver variability. Our goal, therefore, was development and evaluation of an automated tool for MTV delineation/classification of primary tumor and lymph node metastases in PET/CT investigations of HNC patients. METHODS: Automated lesion delineation was performed with a residual 3D U-Net convolutional neural network (CNN) incorporating a multi-head self-attention block. 698 [Formula: see text]F]FDG PET/CT scans from 3 different sites and 5 public databases were used for network training and testing. An external dataset of 181 [Formula: see text]F]FDG PET/CT scans from 2 additional sites was employed to assess the generalizability of the network. In these data, primary tumor and lymph node (LN) metastases were interactively delineated and labeled by two experienced physicians. Performance of the trained network models was assessed by 5-fold cross-validation in the main dataset and by pooling results from the 5 developed models in the external dataset. The Dice similarity coefficient (DSC) for individual delineation tasks and the primary tumor/metastasis classification accuracy were used as evaluation metrics. Additionally, a survival analysis using univariate Cox regression was performed comparing achieved group separation for manual and automated delineation, respectively. RESULTS: In the cross-validation experiment, delineation of all malignant lesions with the trained U-Net models achieves DSC of 0.885, 0.805, and 0.870 for primary tumor, LN metastases, and the union of both, respectively. In external testing, the DSC reaches 0.850, 0.724, and 0.823 for primary tumor, LN metastases, and the union of both, respectively. The voxel classification accuracy was 98.0% and 97.9% in cross-validation and external data, respectively. Univariate Cox analysis in the cross-validation and the external testing reveals that manually and automatically derived total MTVs are both highly prognostic with respect to overall survival, yielding essentially identical hazard ratios (HR) ([Formula: see text]; [Formula: see text] vs. [Formula: see text]; [Formula: see text] in cross-validation and [Formula: see text]; [Formula: see text] vs. [Formula: see text]; [Formula: see text] in external testing). CONCLUSION: To the best of our knowledge, this work presents the first CNN model for successful MTV delineation and lesion classification in HNC. In the vast majority of patients, the network performs satisfactory delineation and classification of primary tumor and lymph node metastases and only rarely requires more than minimal manual correction. It is thus able to massively facilitate study data evaluation in large patient groups and also does have clear potential for supervised clinical application.

Prediction of Incomplete Response of Primary Tumour Based on Clinical and Radiomics Features in Inoperable Head and Neck Cancers after Definitive Treatment.

Radical treatment of patients diagnosed with inoperable and locally advanced head and neck cancers (LAHNC) is still a challenge for clinicians. Prediction of incomplete response (IR) of primary tumour would be of value to the treatment optimization for patients with LAHNC. Aim of this study was to develop and evaluate models based on clinical and radiomics features for prediction of IR in patients diagnosed with LAHNC and treated with definitive chemoradiation or radiotherapy. Clinical and imaging data of 290 patients were included into this retrospective study. Clinical model was built based on tumour and patient related features. Radiomics features were extracted based on imaging data, consisting of contrast- and non-contrast-enhanced pre-treatment CT images, obtained in process of diagnosis and radiotherapy planning. Performance of clinical and combined models were evaluated with area under the ROC curve (AUROC). Classification performance was evaluated using 5-fold cross validation. Model based on selected clinical features including ECOG performance, tumour stage T3/4, primary site: oral cavity and tumour volume were significantly predictive for IR, with AUROC of 0.78. Combining clinical and radiomics features did not improve model's performance, achieving AUROC 0.77 and 0.68 for non-contrast enhanced and contrast-enhanced images respectively. The model based on clinical features showed good performance in IR prediction. Combined model performance suggests that real-world imaging data might not yet be ready for use in predictive models.

ESTRO ACROP guidelines for external beam radiotherapy of patients with complicated bone metastases.

This is the second part of the guidelines on the management of bone metastases. In the first part, the diagnosis and management of uncomplicated bone metastases have been addressed. Bone metastases may significantly reduce quality of life due to related symptoms and possible complications. The most common symptoms include pain and neurologic deficits. The most serious complications of bone metastases are skeletal-related events (SRE), defined as pathologic fracture, spinal cord compression, pain, or other symptoms requiring an urgent intervention such as surgery or radiotherapy. Diffuse bone metastases may lead to hypercalcaemia that can be fatal if untreated. The growing access to modern diagnostic tools allows early detection of asymptomatic bone metastases that could be successfully managed with local treatment if oligometastatic or systemic treatment for diffuse bone metastases to try to avoid the development of SRE.

ESTRO ACROP guidelines for external beam radiotherapy of patients with uncomplicated bone metastases.

After liver and lungs, bone is the third most common metastatic site (Nystrom et al., 1977). Almost all malignancies can metastasize to the skeleton but 80% of bone metastases originate from breast, prostate, lung, kidney and thyroid cancer (Mundy, 2002). Introduction of effective systemic treatment in many cancers has prolonged patients' survival, including those with bone metastases. Bone metastases may significantly reduce quality of life due to related symptoms and possible complications, such as pain and neurologic compromise. The most serious complications of bone metastases are skeletal-related events (SRE), defined as pathologic fracture, spinal cord compression, pain, or other symptoms requiring an urgent intervention such as surgery or radiotherapy. In turn, growing access to modern diagnostic tools allows early detection of asymptomatic bone metastases that could be successfully managed with local treatment avoiding development of SRE. The treatment for bone metastases should focus on relieving existing symptoms and preventing new ones. Radiotherapy is the standard of care for patients with symptomatic bone metastases, providing durable pain relief with minimal toxicity and reasonable cost-effectiveness. Historically, the dose was prescribed in one to five fractions and delivered using simple planning techniques. While 3D-conformal radiotherapy is still widely used for treating bone metastases, introduction of highlyconformal radiotherapy techniques such as stereotactic body radiotherapy (SBRT) have opened new therapeutic possibilities that should be considered in selected patients with bone metastases.

18F-Fluorodeoxyglucose Positron Emission Tomography of Head and Neck Cancer: Location and HPV Specific Parameters for Potential Treatment Individualization.

Purpose: 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is utilized for staging and treatment planning of head and neck squamous cell carcinomas (HNSCC). Some older publications on the prognostic relevance showed inconclusive results, most probably due to small study sizes. This study evaluates the prognostic and potentially predictive value of FDG-PET in a large multi-center analysis. Methods: Original analysis of individual FDG-PET and patient data from 16 international centers (8 institutional datasets, 8 public repositories) with 1104 patients. All patients received curative intent radiotherapy/chemoradiation (CRT) and pre-treatment FDG-PET imaging. Primary tumors were semi-automatically delineated for calculation of SUVmax, SUVmean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Cox regression analyses were performed for event-free survival (EFS), overall survival (OS), loco-regional control (LRC) and freedom from distant metastases (FFDM). Results: FDG-PET parameters were associated with patient outcome in the whole cohort regarding clinical endpoints (EFS, OS, LRC, FFDM), in uni- and multivariate Cox regression analyses. Several previously published cut-off values were successfully validated. Subgroup analyses identified tumor- and human papillomavirus (HPV) specific parameters. In HPV positive oropharynx cancer (OPC) SUVmax was well suited to identify patients with excellent LRC for organ preservation. Patients with SUVmax of 14 or less were unlikely to develop loco-regional recurrence after definitive CRT. In contrast FDG PET parameters deliver only limited prognostic information in laryngeal cancer. Conclusion: FDG-PET parameters bear considerable prognostic value in HNSCC and potential predictive value in subgroups of patients, especially regarding treatment de-intensification and organ-preservation. The potential predictive value needs further validation in appropriate control groups. Further research on advanced imaging approaches including radiomics or artificial intelligence methods should implement the identified cut-off values as benchmark routine imaging parameters.

Criteria for Verification and Replanning Based on the Adaptive Radiotherapy Protocol "Best for Adaptive Radiotherapy" in Head and Neck Cancer.

No clear criteria have yet been established to guide decision-making for patient selection and the optimal timing of adaptive radiotherapy (ART) based on image-guided radiotherapy (IGRT). We have developed a novel protocol­the Best for Adaptive Radiotherapy (B-ART) protocol­to guide patient selection for ART. The aim of the present study is to describe this protocol, to evaluate its validity in patients with head and neck (HN) cancer, and to identify the anatomical and clinical predictors of the need for replanning. We retrospectively evaluated 82 patients with HN cancer who underwent helical tomotherapy (HT) and subsequently required replanning due to soft tissue changes upon daily MVCT. Under the proposed criteria, patients with anatomical changes >3 mm on three to four consecutive scans are candidates for ART. We compared the volumes on the initial CT scan (iCT) and the replanning CT (rCT) scan for the clinical target volumes (CTV1, referring to primary tumor or tumor bed and CTV2, metastatic lymph nodes) and for the parotid glands (PG) and body contour (B-body). The patients were stratified by primary tumor localization, clinical stage, and treatment scheme. The main reasons for replanning were: (1) a planning target volume (PTV) outside the body contour (n = 70; 85.4%), (2) PG shrinkage (n = 69; 84.1%), (3) B-body deviations (n = 69; 84.1%), and (4) setup deviations (n = 40; 48.8%). The replanning decision was made, on average, during the fourth week of treatment (n = 47; 57.3%). The mean reductions in the size of the right and left PG volumes were 6.31 cc (20.9%) and 5.98 cc (20.5%), respectively (p < 0.001). The reduction in PG volume was ≥30% in 30 patients (36.6%). The volume reduction in all of the anatomical structures was statistically significant. Four variables­advanced stage disease (T3−T4), chemoradiation, increased weight loss, and oropharyngeal localization­were significantly associated with the need for ART. The B-ART protocol provides clear criteria to eliminate random errors, and to allow for an early response to relevant changes in target volumes.

Influence of Semiquantitative [18F]FDG PET and Hematological Parameters on Survival in HNSCC Patients Using Neural Network Analysis.

The aim of this study is to assess the influence of semiquantitative PET-derived parameters as well as hematological parameters in overall survival in HNSCC patients using neural network analysis. Retrospective analysis was performed on 106 previously untreated HNSCC patients. Several PET-derived parameters (SUVmax, SUVmean, TotalSUV, MTV, TLG, TLRmax, TLRmean, TLRTLG, and HI) for primary tumor and lymph node with highest activity were assessed. Additionally, hematological parameters (LEU, LEU%, NEU, NEU%, MON, MON%, PLT, PLT%, NRL, and LMR) were also assessed. Patients were divided according to the diagnosis into the good and bad group. The data were evaluated using an artificial neural network (Neural Analyzer version 2.9.5) and conventional statistic. Statistically significant differences in PET-derived parameters in 5-year survival rate between group of patients with worse prognosis and good prognosis were shown in primary tumor SUVmax (10.0 vs. 7.7; p = 0.040), SUVmean (5.4 vs. 4.4; p = 0.047), MTV (23.2 vs. 14.5; p = 0.010), and TLG (155.0 vs. 87.5; p = 0.05), and mean liver TLG (27.8 vs. 30.4; p = 0.031), TLRmax (3.8 vs. 2.6; p = 0.019), TLRmean (2.8 vs. 1.9; p = 0.018), and in TLRTLG (5.6 vs. 2.3; p = 0.042). From hematological parameters, only LMR showed significant differences (2.5 vs. 3.2; p = 0.009). Final neural network showed that for ages above 60, primary tumors SUVmax, TotalSUV, MTV, TLG, TLRmax, and TLRmean over (9.7, 2255, 20.6, 145, 3.6, 2.6, respectively) are associated with worse survival. Our study shows that the neural network could serve as a supplement to PET-derived parameters and is helpful in finding prognostic parameters for overall survival in HNSCC.

Radiotherapy in the Management of Pediatric and Adult Osteosarcomas: A Multi-Institutional Cohort Analysis.

BACKGROUND: Due to the rarity of osteosarcoma and limited indications for radiotherapy (RT), data on RT for this tumor are scarce. This study aimed to investigate the utilization of RT for osteosarcomas in the recent 20 years and to identify factors related to patients' response to radiation. METHODS: We performed a retrospective analysis of patients irradiated for osteosarcoma treatment. We planned to assess differences in the utilization of RT between the periods of 2000-2010 and 2011-2020, identify the risk factors associated with local progression (LP), determine whether RT-related parameters are associated with LP, and calculate patients' survival. RESULTS: A total of 126 patients with osteosarcoma who received 181 RT treatments were identified. We found a difference in RT techniques between RT performed in the years 2000-2010 and that performed in the years 2011-2020. LP was observed after 37 (20.4%) RT treatments. Intent of RT, distant metastases, and concomitant systemic treatment affected the risk of LP. Five-year overall survival was 33% (95% confidence interval (26%-43%)). CONCLUSIONS: RT for osteosarcoma treatment has evolved from simple two-dimensional palliative irradiation into more conformal RT applied for new indications including oligometastatic and oligoprogressive disease. RT may be a valuable treatment modality for selected patients with osteosarcoma.

From multisource data to clinical decision aids in radiation oncology: The need for a clinical data science community.

Big data are no longer an obstacle; now, by using artificial intelligence (AI), previously undiscovered knowledge can be found in massive data collections. The radiation oncology clinic daily produces a large amount of multisource data and metadata during its routine clinical and research activities. These data involve multiple stakeholders and users. Because of a lack of interoperability, most of these data remain unused, and powerful insights that could improve patient care are lost. Changing the paradigm by introducing powerful AI analytics and a common vision for empowering big data in radiation oncology is imperative. However, this can only be achieved by creating a clinical data science community in radiation oncology. In this work, we present why such a community is needed to translate multisource data into clinical decision aids.

Dual-time-point PET/CT study protocol can improve the larynx cancer diagnosis.

AIM: To evaluate whether the sequential dual-time-point fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography (DTP 18F-FDG PET/CT) study improves the differential diagnosis in the larynx. BACKGROUND: In some cases, the clinical and metabolic similarity of laryngitis and larynx cancer make differential diagnostics difficult when performing standard 18F-FDG PET/CT examinations; therefore, an additional study protocol performance seems to be of reasonable value. MATERIALS AND METHODS: 90 patients (mean age: 61 ± 11 years, range: 41-84 years): 23 women (mean age: 63 ± 10 years, range: 51-84 years) and 67 men (mean age: 61 ± 11 years, range: 41-80 years) underwent delayed 18F-FDG PET/CT examinations at 60 and 90 min post intravenous injection (p.i.) of the radiopharmaceutical 18F-FDG. We compared the metabolic activity of 90 structures divided into following groups: normal larynx (30 patients), laryngitis (30 lesions) and larynx cancer (30 tumors) with maximal and mean standardized uptake value (SUVmax, SUVmean) and the retention index (RI-SUVmax). We used the receiver operating characteristics (ROC) curve to evaluate the SUVmax cut-off values. RESULTS: The SUVmax cut-off value at 60 and 90 min p.i. of 2.3 (sensitivity/specificity: 96.4%/100%) and 2.4 (94.2%/100%), respectively, distinguished normal and abnormal metabolic activity in the larynx. When laryngitis and tumors were compared, the SUVmax cut-off values obtained after initial and delayed imaging were 3.6 (87.5%/52.0%) and 6.1 (58.3%/84%), respectively. The RI-SUVmax of 1.3% (71.4%/88.1%) suggested abnormality, while RI-SUVmax of 6.6%, malignant etiology (75.0%/80.0%). CONCLUSIONS: In this study, the sequential DTP scanning protocol improved the sensitivity and specificity of the PET/CT method in terms of differential diagnosis within the larynx.

Empowering patients in decision-making in radiation oncology - can we do better?

The decision as to whether or not a patient should receive radiation therapy as part of their cancer treatment is based on evidence-based practice and on recommended international consensus treatment guidelines. However, the merit of involving the patients' individual preferences and values in the treatment decision is frequently overlooked. Here, we review the current literature pertaining to shared decision-making (SDM) in the field of radiation oncology, including discussion of the patient's perception of radiation therapy as a treatment option and patient involvement in clinical trials. The merit of decision aids during the SDM process in radiation oncology is considered, as are patient preferences for active or passive involvement in decisions about their treatment. Clarity of terminology, a better understanding of effective strategies and increased resources will be needed to ensure SDM in radiation oncology becomes a reality.