The efficacy of proton beam therapy is limited by stopping power ratio (SPR) prediction uncertainties in patient tissues. This study compared image artefacts and SPR prediction accuracy across a single-energy computed tomography (SECT) and three dual-energy computed tomography (DECT) workflows: SECT with a clinical Hounsfield look-up table (HLUT), two commercial DECT algorithms (DirectSPR and MMSim), and an in-house developed model applied to material density (MD) images, called MD-SPR. SPR images of a head-sized phantom with 24 inserts of tissue surrogate and non-tissue materials were evaluated for image artefacts and compared with measured reference SPRs of the inserts. The root-mean-square SPR differences for tissue surrogates were 0.011 (HLUT), 0.005 (DirectSPR), 0.007 (MMSim), and 0.005 (MD-SPR). For non-tissue materials, the differences were 0.167, 0.028, 0.034, and 0.011, respectively. These results indicate that DECT-based SPR prediction workflows, particularly MD-SPR, can reduce both image artefacts and range uncertainties, compared with a SECT-based HLUT workflow.

Background and purpose: Volumetric modulated arc therapy (VMAT) plans often involve small and irregular beam apertures (i.e. high complexity), which can pose challenges for accurate dose calculations. The aim of this study was to investigate plausible dose calculation errors for VMAT plans of various complexity. Patient/material and methods: Twenty patient cases, each with three VMAT plans of different complexities (i.e. 60 plans in total), were included. All plans were calculated using six different dose calculation methods. It was assumed that greater differences between calculations reflect increased difficulty in accurately estimating dose. Basic performances of the six calculation methods were evaluated based on static fields. Three-dimensional distributions of voxel-wise two standard deviations (2SDs) in percent of local voxel dose were visually evaluated. 2SD volume histograms were analyzed as well as the mean (2SDMean) and maximum 2SD within 2 cm3 (2SD2cc) for different regions of interest. Results: Higher 2SD values were generally found outside the planning target volume (PTV) compared to inside, particularly in low dose and buildup regions. Average (per treatment site) 2SDMean/2SD2cc values were 0.9–1.2%/1.8–5.1% in the PTV and 1.8–3.1/8.4–18.7% for the region 1 cm outside the PTV. For organs of interest, 2SDMean/2SD2cc values were larger than the equivalent values in the PTV, with highest values up to 5/15% observed for the prostate cases. Interpretation: Variations between dose calculation methods were larger in organs of interest than in the PTVs. Differences in 2SD distributions between the patient cases were generally larger than differences between the complexity levels.

ABSTRACT Background A previously published failure‐type specific risk model showed good performance in the original cohort. Aim: to validate the model and separate patients with high‐ and low‐risk loco‐regional failure (LRF). Goal: to identify patients potentially suitable for treatment intensification trials. Methods Validation data: 756 patients from two institutions (different countries). Predictive performance was evaluated by Brier scores and AUCs. Discriminatory performance was compared to Union for International Cancer Control (UICC) staging (versions 7 and 8). Results The model's 3‐year AUC for LRF was 65%, significantly better than UICC7 staging, but no significant difference to UICC8. Model‐based risk stratification and UICC8 both identified high‐risk patient groups with 3‐year LRF ≈30%. The population mean was 18%. Conclusions The model performed well on a group level. UICC8 staging performed equally well. Although developed for the endpoint of OS, an improvement from UICC version 7 to version 8 was evident also for the prediction of LRF.

BACKGROUND:Preparing retrospective dose data for risk modeling using large study cohorts can be time consuming as it often requires patient-wise manual interventions. This is especially the case when considering organs at risk (OARs) not systematically delineated historically. Therefore, we aimed to develop and test a semi-automated workflow for cohort-wise preparation of radiotherapy data from the oncology information system (OIS), including OAR autosegmentation, for risk modeling purposes. METHODS:A semi-automated workflow, including cohort-wise data extraction from a clinical OIS, cleanup, autosegmentation, quality controls (QCs), and data injection into a research OIS was iteratively developed using 106 patient cases. We evaluated two deep learning (DL)-based methods and compared with four atlas-based methods for autosegmentation of the proximal bronchial tree (PBT), the heart, and the esophagus that were possible to integrate into the workflow. One method was an in-house DL-based model using OARs manually contoured by experts for 100 cases. Geometric and dosimetric agreements with manually contoured OARs were evaluated for 20 independent cases. The final workflow was tested on 50 independent cases. RESULTS:The DL-based methods were better than the atlas-based at segmenting the PBT (mean Dice similarity coefficient (DSC) 0.81-0.83 versus 0.59-0.80) and the esophagus (mean DSC 0.76-0.77 versus 0.39-0.46). The methods performed similarly for the heart (mean DSC 0.90-0.95 (DL-based) and 0.84-0.90 (atlas-based)). Our in-house autosegmentation model had the highest mean DSC for all OARs. The final version of the workflow successfully prepared data for 80% of the test cases without case-specific manual interventions. CONCLUSIONS:The semi-automated workflow enabled efficient cohort-wise preparation of OIS data for risk modeling purposes. Our in-house DL-based segmentation model outperformed the other methods.

Background and purpose Absorbed dose uncertainties in radiotherapy plans are generally larger near field edges compared to the center of the field. The aim of this study was to investigate dose uncertainties related to the field edge in 3D for plans of varying complexities. Materials and methods A method was developed for calculation of the fraction of field edge dose (FED), that could be visualized as a 3D uncertainty map (3DUMFED). Twelve clinical treatment plans were included for four different treatment sites that were reoptimized to create one plan with reduced complexity and one of increased complexity. 3DUMFED was calculated for all 36 plans. The highest FED for a 2 cm3 volume (FED2cm3) and average FED (FEDmean) were calculated for the planning target volumes (PTV) and organs at risk (OAR) and compared with the edge area metric (EAM). Results High FED (above 20 %) were mainly found just outside the PTV border. FEDmean in PTV was highest for the plans of increased complexity. The FEDmean for PTVs and OARs, as well as the FED2cm3 for PTVs, correlated with ρ ≥ 0.81 to EAM. The FED2cm3 for OARs had a weaker correlation with EAM (ρ = 0.55). 3DUMFED analysis revealed that plan complexity affects different parts of the patient volume in different ways. Conclusions 3DUMFED offers a way to estimate dose uncertainties related to the field edge in 3D. It also allows for separate evaluation in different regions of interest, unlike EAM, which mainly correlates with the dose uncertainty related to the PTV.

BackgroundProton radiotherapy treatment plans are currently restricted by the range uncertainties originating from the stopping power ratio (SPR) prediction based on single-energy computed tomography (SECT). Various studies have shown that multi-energy CT (MECT) can reduce the range uncertainties due to medical implant materials and age-related variations in tissue composition. None of these has directly applied the basis material density (MD) images produced by projection-based MECT systems for SPR prediction.PurposeTo present and evaluate a novel proton SPR prediction method based on MD images from dual-energy CT (DECT), which could reduce the range uncertainties currently associated with proton radiotherapy.MethodsA theoretical basis material decomposition into water and iodine material densities was performed for various pediatric and adult human reference tissues, as well as other non-tissue materials, by minimizing the root-mean-square relative attenuation error in the energy interval from 40 to 140 keV. A model (here called MD-SPR) mapping predicted MDs to theoretically calculated reference SPRs was created with locally weighted scatterplot smoothing (LOWESS) data-fitting. The goodness of fit of the MD-SPR model was evaluated for the included reference tissues. MD images of two electron density phantoms, combined to form a head- and an abdomen-sized phantom setup, were acquired with a clinical projection-based fast-kV switching DECT scanner. The MD images were compared to the theoretically predicted MDs of the tissue surrogates and other non-tissue materials in the phantoms, as well as used for input to the MD-SPR model for generation of SPR images. The SPR images were subsequently compared to theoretical reference SPRs of the materials in the phantoms, as well as to SPR images from a commercial algorithm (DirectSPR, Siemens Healthineers, Forchheim, Germany) using image-based consecutive scan DECT for the same phantom setups.ResultsThe predicted SPRs of the tissue surrogates were similar for MD-SPR and DirectSPR, where the adipose and bone tissue surrogates were within 1% difference to the reference SPRs, while other non-adipose soft tissue surrogates (breast, brain, liver, muscle) were all underestimated by between -0.7% and -1.8%. The SPRs of the non-tissue materials (polymethyl methacrylate (PMMA), polyether ether ketone (PEEK), graphite and Teflon) were within 2.8% for MD-SPR images, compared to 6.8% for DirectSPR.ConclusionsThe MD-SPR model performed similar compared to other published methods for the human reference tissues. The SPR prediction for tissue surrogates was similar to DirectSPR and showed potential to improve SPR prediction for non-tissue materials.

BACKGROUND:Delivery variations during radiotherapy can cause discrepancies between planned and delivered dose distribution. These variations could arise from random and systematic offsets in certain machine parameters or systematic offsets related to the calibration process of the treatment unit. PURPOSE:The aim of this study was to present a novel simulation-based methodology to evaluate realistic delivery variations in three dimensions (3D). Additionally, we investigated the dosimetric impact of delivery variations for volumetric modulated arc therapy (VMAT) plans for different treatment sites and complexities. METHODS:Twelve VMAT plans for different treatment sites (prostate-, head & neck-, lung-, and gynecological cancer) were selected. The clinical plan used for the treatment of each patient was reoptimized to create one plan with reduced complexity (i.e., simple plan) and one of higher complexity (i.e., complex plan). This resulted in a total of 36 plans. Delivery variations were simulated by randomly introducing offsets in multi-leaf collimator position, jaw position, gantry angle and collimator angle simultaneously. Twenty simulations were carried out for each of the 36 plans, yielding 720 simulated deliveries. To explore the impact of individual offsets, additional simulations were conducted for each type of offset separately. A 3D dose calculation was performed for each simulation using the same calculation engine as for the clinical plan. Two standard deviations (2SD) of dose were determined for every voxel for 3D-spatial evaluations. The dose variation in certain DVH metrics, that is, D2% and D98% for the clinical target volume and five different DVH metrics for selected organs at risk, was calculated for the twenty simulated deliveries of each plan. For comparison, the effect of delivery variations was assessed by conducting measurements with the Delta4 phantom. RESULTS:The volume of voxels with 2SD above 1% of the prescribed dose was consistently larger for the complex plans in comparison to their corresponding simple and clinical plans. 2SDs larger than 1% were in many cases, found to accumulate outside the planning target volume. For complex plans, regions with 2SDs larger than 1% were detected also inside the high dose region, exhibiting, on average, a size six times larger volume, than those observed in simple plans. Similar results were found for all treatment sites. Variation in the selected DVH metrics for the simulated deliveries was generally largest for the complex plans with few exceptions. When comparing the 2SD distribution of the measurements with the 2SD distribution from the simulations, the spatial information showed deviations outside the PTV in both simulations and measurements. However, the measured values were, on average, 35% higher for the prostate plans and 10% higher for the head & neck plans compared to the simulated values. CONCLUSIONS:The presented methodology effectively quantified and localized dose deviations due to delivery offsets. The 3D analysis provided information that was undetectable using the analysis based on DVH metrics. Dosimetric uncertainties due to delivery variations were prominent at the edge of the high-dose region irrespective of treatment site and plan complexity. Dosimetric uncertainties inside the high-dose region was more profound for plans of higher complexity.

Purpose: Radiation pneumonitis (RP) is a dose-limiting toxicity associated with increased mortality for patients with non-small cell lung cancer (NSCLC) treated with chemoradiotherapy (CRT). This study aims to assess the incidence of symptomatic RP (grade 2–5), rate of recovery and associated predictive factors. Material and methods: We performed a retrospective population-based study including 602 patients with NSCLC who were treated with CRT between 2002 and 2016. RP and rate of recovery were analysed using Common Terminology Criteria for Adverse Events version 4.0. Stepwise logistic regression was performed to analyse potential predictive factors for the two endpoints RP grade ≥ 2 and RP grade ≥ 3. Results: A total of 136 (23%) patients developed symptomatic RP and 37 (6%) developed RP grade ≥ 3. A total of 67 (71%) recovered, whereas the remaining 27 (29%), with the major proportion of patients belonging to the RP grade ≥ 3 group, suffered from prevailing sequelae. On multivariable analysis, the selected model for predicting RP grade ≥ 2 included the factors V20, smoking status, average fractions per week and chemotherapy agent. V20 and age were selected factors for RP grade ≥ 3. Interpretation: The results suggest that regardless of all proposed factors predictive for RP, the most important influenceable significant factor still is dose to the lung. The main aim should be to avoid RP grade ≥ 3, where a substantial proportion of patients suffer from prevailing sequalae. Consequently, the technical improvement and precision of radiotherapy delivery should continue to focus on lung sparing techniques also in the ongoing immunotherapy-containing schedules where the risk of pneumonitis may be increased. e factor still is dose to the lung. Consequently, the technical improvement and precision of radiotherapy delivery should continue to focus on lung sparing techniques also in the ongoing immunotherapy-containing schedules where the risk of pneumonitis may be increased.

Background: Directive feedback manikins in resuscitation training evolved faster than the pedagogical evidence. Educators and learning systems must seek clarification on the efficacy of this technology to have evidence-based practices. This project explores directive feedback device use in cardiopulmonary resuscitation (CPR) education for laypersons. Methods: A prospective nonrandomized-controlled design assessed two pedagogical approaches of directive feedback manikins in adult CPR lessons. The 230 participants were distributed between three groups: a control group without directive feedback manikins (no lights, NL), an expert coaching (EC) group with directive feedback and educator interpretation, and a peer coaching (PC) group with directive feedback, peer interpretation, and expert quality assurance. Results: From the 25 courses observed, average compression depth (mm) did not differ between groups (p p = .498), average compression rate (compressions: minute) significantly differed between groups (p p = .004), and correct hand placement did not differ between groups (p p = .249). A chi-square test showed no significant association between groups and CPR skill feedback, or between groups and "recommending the course to a friend or family member." The PC group was more likely to agree that they could "coach someone to do CPR skills" than the NL or EC. Conclusions: This study expands the knowledge base of directive feedback manikins in a pedagogical setting to improve CPR competencies. Training organizations may consider any of these practices effective, choosing those that align with desired outcomes. CPR educators need orientation to feedback devices as well as professional development on educational options for their use. Considerations for further research include technology costs, access, and cultural aspects of implementing these tools.

BACKGROUND:Normal tissue complication probability (NTCP) models are often based on doses retrieved from delineated volumes. For retrospective dose-response studies focusing on organs that have not been delineated historically, automatic segmentation might be considered. However, automatic segmentation risks generating considerable delineation errors and knowledge regarding how these errors impact the estimated organ dose is important. Furthermore, organ-at-risk (OAR) dose uncertainties cannot be eliminated and might affect the resulting NTCP model. Therefore, it is also of interest to study how OAR dose errors impact the NTCP modeling results.PURPOSE:To investigate how random delineation errors of the proximal bronchial tree, heart, and esophagus impact the estimated OAR dose, and to investigate how random errors in the doses used for dose-response modeling affect the estimated NTCPs.METHODS:We investigated the impact of random delineation errors on the estimated OAR dose using the treatment plans of 39 patients treated with conventionally fractionated radiation therapy of non-small-cell lung cancer. Study-specific reference structures were defined by manually contouring the proximal bronchial tree, heart and esophagus. For each patient and organ, 120 reshaped structures were created by introducing random shifts and margins to the entire reference structure. The mean and near-maximum dose to the reference and reshaped structures were compared. In a separate investigation, the impact of random dose errors on the NTCP model was studied performing dose-response modeling with study sets containing treatment outcomes and OAR doses with and without introduced errors. Universal patient populations with defined population risks, dose-response relationships and distributions of OAR doses were used as ground truth. From such a universal population, we randomly sampled data sets consisting of OAR dose and treatment outcome into reference populations. Study sets of different sizes were created by repeatedly introducing errors to the OAR doses of each reference population. The NTCP models generated with dose errors were compared to the reference NTCP model of the corresponding reference population.RESULTS:A total of 14 040 reshaped structures with random delineation errors were created. The delineation errors resulted in systematic mean dose errors of less than 1% of the prescribed dose (PD). Mean dose differences above 15% of PD and near-maximum doses differences above 25% of PD were observed for 211 and 457 reshaped structures, respectively. Introducing random errors to OAR doses used for dose-response modeling resulted in systematic underestimations of the median NTCP. For all investigated scenarios, the median differences in NTCP were within 0.1 percentage points (p.p.) when comparing different study sizes.CONCLUSIONS:Introducing random delineation errors to the proximal bronchial tree, heart and esophagus resulted in mean dose and near-maximum dose differences above 15% and 25% of PD, respectively. We did not observe an association between the dose level and the magnitude of the dose errors. For the scenarios investigated in this study, introducing random errors to OAR doses used for dose-response modeling resulted in systematic underestimations of the median NTCP for reference risks higher than the universal population risk. The median NTCP underestimation was similar for different study sizes, all within 0.1 p.p.

In this work we evaluated VMAT plan complexity by using different methods and approaches: complexity related to the aperture shape, the nature of the dynamic delivery, the impact of delivery variations as well as complexity analyses based on measurements. Prostate cancer treatment plans with different levels of complexity were created for three different patient cases. The plans intended to be most complex were also scored to be the most complex according to the evaluation methods used. The results for the other plans were more diverse. The methods that included 3D spatial information on complexity gave additional information important for the analysis of clinical relevance. In order to use specific complexity estimation methods for taking clinical decisions it is essential to understand how different aspects of complexity are incorporated in each method.