Real-time beam monitoring for error detection in IMRT plans and impact on dose-volume histograms : A multi-center study.

PURPOSE: This study aimed to test the sensitivity of a transmission detector for online dose monitoring of intensity-modulated radiation therapy (IMRT) for detecting small delivery errors. Furthermore, the correlation of changes in detector output induced by small delivery errors with other metrics commonly employed to quantify the deviations between calculated and delivered dose distributions was investigated. METHODS: Transmission detector measurements were performed at three institutions. Seven types of errors were induced in nine clinical step-and-shoot (S&S) IMRT plans by modifying the number of monitor units (MU) and introducing small deviations in leaf positions. Signal reproducibility was investigated for short- and long-term stability. Calculated dose distributions were compared in terms of γ passing rates and dose-volume histogram (DVH) metrics (e.g., Dmean, Dx%, Vx%). The correlation between detector signal variations, γ passing rates, and DVH parameters was investigated. RESULTS: Both short- and long-term reproducibility was within 1%. Dose variations down to 1 MU (∆signal 1.1 ± 0.4%) as well as changes in field size and positions down to 1 mm (∆signal 2.6 ± 1.0%) were detected, thus indicating high error-detection sensitivity. A moderate correlation of detector signal was observed with γ passing rates (R2 = 0.57-0.70), while a good correlation was observed with DVH metrics (R2 = 0.75-0.98). CONCLUSION: The detector is capable of detecting small delivery errors in MU and leaf positions, and is thus a highly sensitive dose monitoring device for S&S IMRT for clinical practice. The results of this study indicate a good correlation of detector signal with DVH metrics; therefore, clinical action levels can be defined based on the presented data.

Robustness of intensity modulated proton treatment of esophageal cancer for anatomical changes and breathing motion.

BACKGROUND AND PURPOSE: In this study, we assessed the robustness of intensity modulated proton therapy (IMPT) in esophageal cancer for anatomical variations during treatment. METHODS: The first sixty esophageal cancer patients, treated clinically with chemoradiotherapy were included. The treatment planning strategy was based on an internal target volume (ITV) approach, where the ITV was created from the clinical target volumes (CTVs) delineated on all phases of a 4DCT. For optimization, a 3 mm isotropic margin was added to the ITV, combined with robust optimization using 5 mm setup and 3 % range uncertainty. Each patient received weekly repeat CTs (reCTs). Robust plan re-evaluation on all reCTs, and a robust dose summation was performed. To assess the factors influencing ITV coverage, a multivariate linear regression analysis was performed. Additionally, clinical adaptations were evaluated. RESULTS: The target coverage was adequate (ITV V94%>98 % on the robust voxel-wise minimum dose) on most reCTs (91 %), and on the summed dose in 92 % of patients. Significant predictors for ITV coverage in the multivariate analysis were diaphragm baseline shift and water equivalent depth (WED) of the ITV in the beam direction. Underdosage of the ITV mainly occurred in week 1 and 4, leading to treatment adaptation of eight patients, all on the first reCT. CONCLUSION: Our IMPT treatment of esophageal cancer is robust for anatomical variations. Adaptation appears to be most effective in the first week of treatment. Diaphragm baseline shifts and WED are predictive factors for ITV underdosage, and should be incorporated in an adaptation protocol.

Proton and photon radiotherapy in stage III NSCLC: Effects on hematological toxicity and adjuvant immune therapy.

BACKGROUND AND PURPOSE: Concurrent chemo-radiotherapy (CCRT) followed by adjuvant durvalumab is standard-of-care for fit patients with unresectable stage III NSCLC. Intensity modulated proton therapy (IMPT) results in different doses to organs than intensity modulated photon therapy (IMRT). We investigated whether IMPT compared to IMRT reduce hematological toxicity and whether it affects durvalumab treatment. MATERIALS AND METHODS: Prospectively collected series of consecutive patients with stage III NSCLC receiving CCRT between 06.16 and 12.22 (staged with FDG-PET-CT and brain imaging) were retrospectively analyzed. The primary endpoint was the incidence of lymphopenia grade ≥ 3 in IMPT vs IMRT treated patients. RESULTS: 271 patients were enrolled (IMPT: n = 71, IMRT: n = 200) in four centers. All patients received platinum-based chemotherapy. Median age: 66 years, 58 % were male, 36 % had squamous NSCLC. The incidence of lymphopenia grade ≥ 3 during CCRT was 67 % and 47 % in the IMRT and IMPT group, respectively (OR 2.2, 95 % CI: 1.0-4.9, P = 0.03). The incidence of anemia grade ≥ 3 during CCRT was 26 % and 9 % in the IMRT and IMPT group respectively (OR = 4.9, 95 % CI: 1.9-12.6, P = 0.001). IMPT was associated with a lower rate of Performance Status (PS) ≥ 2 at day 21 and 42 after CCRT (13 % vs. 26 %, P = 0.04, and 24 % vs. 39 %, P = 0.02). Patients treated with IMPT had a higher probability of receiving adjuvant durvalumab (74 % vs. 52 %, OR 0.35, 95 % CI: 0.16-0.79, P = 0.01). CONCLUSION: IMPT was associated with a lower incidence of severe lymphopenia and anemia, better PS after CCRT and a higher probability of receiving adjuvant durvalumab.

Clinical integration of software tool VEDO for adaptive and quantitative application of phased array hyperthermia in the head and neck.

BACKGROUND AND PURPOSE: In Rotterdam, patient-specific hyperthermia (HT) treatment planning (HTP) is applied for all deep head and neck (H&N) HT treatments. In this paper we introduce VEDO (the Visualisation Tool for Electromagnetic Dosimetry and Optimisation), the software tool required, and demonstrate its value for HTP-guided online complaint-adaptive (CA) steering based on specific absorption rate (SAR) optimisation during a H&N HT treatment. MATERIALS AND METHODS: VEDO integrates CA steering, visualisation of the SAR patterns and mean tumour SAR (SAR(target)) optimisation in a single screen. The pre-calculated electromagnetic fields are loaded into VEDO. During treatment, VEDO shows the SAR pattern, overlaid on the patients' CT-scan, corresponding to the actually applied power settings and it can (re-)optimise the SAR pattern to minimise SAR at regions where the patient senses discomfort while maintaining a high SAR(target). RESULTS: The potential of the quantitative SAR steering approach using VEDO is demonstrated by analysis of the first treatment in which VEDO was used for two patients using the HYPERcollar. These cases show that VEDO allows response to power-related complaints of the patient and to quantify the change in absolute SAR: increasing either SAR(target) from 96 to 178 W/kg (case 1); or show that the first SAR distribution was already optimum (case 2). CONCLUSION: This analysis shows that VEDO facilitates a quantitative treatment strategy allowing standardised application of HT by technicians of different HT centres, which will potentially lead to improved treatment quality and the possibility of tracking the effectiveness of different treatment strategies.

Simulation techniques in hyperthermia treatment planning.

Abstract Clinical trials have shown that hyperthermia (HT), i.e. an increase of tissue temperature to 39-44 °C, significantly enhance radiotherapy and chemotherapy effectiveness [1]. Driven by the developments in computational techniques and computing power, personalised hyperthermia treatment planning (HTP) has matured and has become a powerful tool for optimising treatment quality. Electromagnetic, ultrasound, and thermal simulations using realistic clinical set-ups are now being performed to achieve patient-specific treatment optimisation. In addition, extensive studies aimed to properly implement novel HT tools and techniques, and to assess the quality of HT, are becoming more common. In this paper, we review the simulation tools and techniques developed for clinical hyperthermia, and evaluate their current status on the path from 'model' to 'clinic'. In addition, we illustrate the major techniques employed for validation and optimisation. HTP has become an essential tool for improvement, control, and assessment of HT treatment quality. As such, it plays a pivotal role in the quest to establish HT as an efficacious addition to multi-modality treatment of cancer.

Radiotherapy for mediastinal lymphoma in breath hold using surface monitoring and nasal high flow oxygen: Clinical experiences and breath hold stability.

PURPOSE: In this study we describe the clinical introduction and evaluation of radiotherapy in mediastinal lymphoma in breath hold using surface monitoring combined with nasal high flow therapy (NHFT) to prolong breath hold duration. MATERIALS AND METHODS: 11 Patients with mediastinal lymphoma were evaluated. 6 Patients received NHFT, 5 patients were treated in breath hold without NHFT. Breath hold stability as measured by a surface scanning system was evaluated, as well as internal movement based on cone beam computed tomography (CBCT) before and after treatment. Based on internal movement, margins were determined. In a parallel planning study we compared free breathing plans with breath hold plans using the determined margins. RESULTS: Average inter breath hold stability was 0.6 mm for NHFT treatments, and 0.5 mm for non-NHFT treatments (p > 0.1). Intra breath hold stability was 0.8 vs. 0.6 mm (p > 0.1) on average. Using NHFT, average breath hold duration increased from 34 s to 60 s (p < 0.01). Residual CTV motion derived from CBCTs before and after each fraction was 2.0 mm for NHFT vs 2.2 mm for non-NHFT (p > 0.1). Combined with inter-fraction motion, a uniform mediastinal margin of 5 mm appears to be sufficient. In breath hold, mean lung dose is reduced by 2.6 Gy (p < 0.001), while mean heart dose is reduced by 2.0 Gy (p < 0.001). CONCLUSION: Treatment of mediastinal lymphoma in breath hold is feasible and safe. The addition of NHFT approximately increases breath hold durations with a factor two while stability is maintained. By reducing breathing motion, margins can be decreased to 5 mm. A considerable dose reduction in heart, lungs, esophagus, and breasts can be achieved with this method.

Comparison of photon intensity modulated, hybrid and volumetric modulated arc radiation treatment techniques in locally advanced non-small cell lung cancer.

Background and purpose: There is no consensus on the best photon radiation technique for non-small cell lung cancer (NSCLC). This study quantified the differences between commonly used treatment techniques in NSCLC to find the optimal technique. Materials and methods: Treatment plans were retrospectively generated according to clinical guidelines for 26 stage III NSCLC patients using intensity modulated radiation therapy (IMRT), hybrid, and volumetric modulated arc therapy (VMATC, and VMATV5 optimized for lower lung and heart dose). Plans were evaluated for target coverage, organs at risk dose (including heart substructures) and normal tissue complication probabilities (NTCP). Results: The comparison showed significant and largest median differences (>1 Gy or >5%) in favor of IMRT for the mediastinal envelope and heart (maximum dose), in favor of the hybrid technique for the lungs (V5Gy of the total lungs and V5Gy of the contralateral lung) and in favor of VMATC for the heart (Dmean), most of the substructures of the heart, and the spinal cord (maximum dose). The VMATV5 technique had significantly lower heart dose compared to the hybrid technique and significantly lower lung dose compared to the VMATC, combining both advantages in one technique. The mean ΔNTCP did not exceed the 2 percent point (pp) for grade 5 (mortality), and 10 pp for grade ≥2 toxicities (radiation pneumonitis and acute esophageal toxicity), but ΔNTCP was mostly in favor of VMATC/V5 for individual patients. Conclusion: This planning study showed that VMATV5 was preferred as it achieved low lung and heart doses, as well as low NTCPs, simultaneously.

Machine learning for normal tissue complication probability prediction: Predictive power with versatility and easy implementation.

Background and purpose: A popular Normal tissue Complication (NTCP) model deployed to predict radiotherapy (RT) toxicity is the Lyman-Burman Kutcher (LKB) model of tissue complication. Despite the LKB model's popularity, it can suffer from numerical instability and considers only the generalized mean dose (GMD) to an organ. Machine learning (ML) algorithms can potentially offer superior predictive power of the LKB model, and with fewer drawbacks. Here we examine the numerical characteristics and predictive power of the LKB model and compare these with those of ML. Materials and methods: Both an LKB model and ML models were used to predict G2 Xerostomia on patients following RT for head and neck cancer, using the dose volume histogram of parotid glands as the input feature. Model speed, convergence characteristics and predictive power was evaluated on an independent training set. Results: We found that only global optimization algorithms could guarantee a convergent and predictive LKB model. At the same time our results showed that ML models remained unconditionally convergent and predictive, while staying robust to gradient descent optimization. ML models outperform LKB in Brier score and accuracy but compare to LKB in ROC-AUC. Conclusion: We have demonstrated that ML models can quantify NTCP better than or as well as LKB models, even for a toxicity that the LKB model is particularly well suited to predict. ML models can offer this performance while offering fundamental advantages in model convergence, speed, and flexibility, and so could offer an alternative to the LKB model that could potentially be used in clinical RT planning decisions.

BioXmark® liquid fiducials to enable radiotherapy tumor boosting in rectal cancer, a feasibility trial.

Background and purpose: Dose-escalation in rectal cancer (RCa) may result in an increased complete response rate and thereby enable omission of surgery and organ preservation. In order to implement dose-escalation, it is crucial to develop a technique that allows for accurate image-guided radiotherapy. The aim of the current study was to determine the performance of a novel liquid fiducial marker (BioXmark®) in RCa patients during the radiotherapy course by assessing its positional stability on daily cone-beam CT (CBCT), technical feasibility, visibility on different imaging modalities and safety. Materials and methods: Prospective, non-randomized, single-arm feasibility trial with inclusion of twenty patients referred for neoadjuvant chemoradiotherapy for locally advanced RCa. Primary study endpoint was positional stability on CBCT. Furthermore, technical aspects, safety and clinical performance of the marker, such as visibility on different imaging modalities, were evaluated. Results: Seventy-four markers from twenty patients were available for analysis. The marker was stable in 96% of the cases. One marker showed clinically relevant migration, one marker was lost before start of treatment and one marker was lost during treatment. Marker visibility was good on computed tomography (CT) and CBCT, and moderate on electronic portal imaging (EPI). Marker visibility on magnetic resonance imaging (MRI) was poor during response evaluation. Conclusion: The novel liquid fiducial marker demonstrated positional stability. We provide evidence of the feasibility of the novel fiducial marker for image-guided radiotherapy on daily cone beam CT for RCa patients.

Clinical evaluation of synthetic computed tomography methods in adaptive proton therapy of lung cancer patients.

Background and purpose: Efficient workflows for adaptive proton therapy are of high importance. This study evaluated the possibility to replace repeat-CTs (reCTs) with synthetic CTs (sCTs), created based on cone-beam CTs (CBCTs), for flagging the need of plan adaptations in intensity-modulated proton therapy (IMPT) treatment of lung cancer patients. Materials and methods: Forty-two IMPT patients were retrospectively included. For each patient, one CBCT and a same-day reCT were included. Two commercial sCT methods were applied; one based on CBCT number correction (Cor-sCT), and one based on deformable image registration (DIR-sCT). The clinical reCT workflow (deformable contour propagation and robust dose re-computation) was performed on the reCT as well as the two sCTs. The deformed target contours on the reCT/sCTs were checked by radiation oncologists and edited if needed. A dose-volume-histogram triggered plan adaptation method was compared between the reCT and the sCTs; patients needing a plan adaptation on the reCT but not on the sCT were denoted false negatives. As secondary evaluation, dose-volume-histogram comparison and gamma analysis (2%/2mm) were performed between the reCT and sCTs. Results: There were five false negatives, two for Cor-sCT and three for DIR-sCT. However, three of these were only minor, and one was caused by tumour position differences between the reCT and CBCT and not by sCT quality issues. An average gamma pass rate of 93% was obtained for both sCT methods. Conclusion: Both sCT methods were judged to be of clinical quality and valuable for reducing the amount of reCT acquisitions.

Automatic quality assurance of radiotherapy treatment plans using Bayesian networks: A multi-institutional study.

Purpose: Artificial intelligence applications in radiation oncology have been the focus of study in the last decade. The introduction of automated and intelligent solutions for routine clinical tasks, such as treatment planning and quality assurance, has the potential to increase safety and efficiency of radiotherapy. In this work, we present a multi-institutional study across three different institutions internationally on a Bayesian network (BN)-based initial plan review assistive tool that alerts radiotherapy professionals for potential erroneous or suboptimal treatment plans. Methods: Clinical data were collected from the oncology information systems in three institutes in Europe (Maastro clinic - 8753 patients treated between 2012 and 2020) and the United States of America (University of Vermont Medical Center [UVMMC] - 2733 patients, University of Washington [UW] - 6180 patients, treated between 2018 and 2021). We trained the BN model to detect potential errors in radiotherapy treatment plans using different combinations of institutional data and performed single-site and cross-site validation with simulated plans with embedded errors. The simulated errors consisted of three different categories: i) patient setup, ii) treatment planning and iii) prescription. We also compared the strategy of using only diagnostic parameters or all variables as evidence for the BN. We evaluated the model performance utilizing the area under the receiver-operating characteristic curve (AUC). Results: The best network performance was observed when the BN model is trained and validated using the dataset in the same center. In particular, the testing and validation using UVMMC data has achieved an AUC of 0.92 with all parameters used as evidence. In cross-validation studies, we observed that the BN model performed better when it was trained and validated in institutes with similar technology and treatment protocols (for instance, when testing on UVMMC data, the model trained on UW data achieved an AUC of 0.84, compared with an AUC of 0.64 for the model trained on Maastro data). Also, combining training data from larger clinics (UW and Maastro clinic) and using it on smaller clinics (UVMMC) leads to satisfactory performance with an AUC of 0.85. Lastly, we found that in general the BN model performed better when all variables are considered as evidence. Conclusion: We have developed and validated a Bayesian network model to assist initial treatment plan review using multi-institutional data with different technology and clinical practices. The model has shown good performance even when trained on data from clinics with divergent profiles, suggesting that the model is able to adapt to different data distributions.

Geometric and dosimetric analysis of CT- and MR-based automatic contouring for the EPTN contouring atlas in neuro-oncology.

PURPOSE: Atlas-based and deep-learning contouring (DLC) are methods for automatic segmentation of organs-at-risk (OARs). The European Particle Therapy Network (EPTN) published a consensus-based atlas for delineation of OARs in neuro-oncology. In this study, geometric and dosimetric evaluation of automatically-segmented neuro-oncological OARs was performed using CT- and MR-models following the EPTN-contouring atlas. METHODS: Image and contouring data from 76 neuro-oncological patients were included. Two atlas-based models (CT-atlas and MR-atlas) and one DLC-model (MR-DLC) were created. Manual contours on registered CT-MR-images were used as ground-truth. Results were analyzed in terms of geometrical (volumetric Dice similarity coefficient (vDSC), surface DSC (sDSC), added path length (APL), and mean slice-wise Hausdorff distance (MSHD)) and dosimetrical accuracy. Distance-to-tumor analysis was performed to analyze to which extent the location of the OAR relative to planning target volume (PTV) has dosimetric impact, using Wilcoxon rank-sum tests. RESULTS: CT-atlas outperformed MR-atlas for 22/26 OARs. MR-DLC outperformed MR-atlas for all OARs. Highest median (95 %CI) vDSC and sDSC were found for the brainstem in MR-DLC: 0.92 (0.88-0.95) and 0.84 (0.77-0.89) respectively, as well as lowest MSHD: 0.27 (0.22-0.39)cm. Median dose differences (ΔD) were within ± 1 Gy for 24/26(92 %) OARs for all three models. Distance-to-tumor showed a significant correlation for ΔDmax,0.03cc-parameters when splitting the data in ≤ 4 cm and > 4 cm OAR-distance (p < 0.001). CONCLUSION: MR-based DLC and CT-based atlas-contouring enable high-quality segmentation. It was shown that a combination of both CT- and MR-autocontouring models results in the best quality.

Implementation of treatment planning in the routine clinical procedure of regional hyperthermia treatment of cervical cancer: an overview and the Rotterdam experience.

PURPOSE: This manuscript provides an overview in the field of hyperthermia treatment planning (HTP) in cervical cancer. Treatment planning techniques: The workflow of an HTP assisted treatment generally consists of patient imaging, tissue segmentation, model generation, electromagnetic (EM) and thermal calculations, optimisation, and clinical implementation. A main role in HTP is played by numerical simulations, for which currently a number of software packages are available in hyperthermia. To implement these simulations, accurate applicator models and accurate knowledge of dielectric and thermal parameters is mandatory. Model validation is necessary to check if this is implemented well. In the translation from HTP models to the clinic, the main aspect is accurate representation of the actual treatment situation in the HTP models. Accurate patient positioning and organ-specific segmentation can be helpful in minimising the differences between model and clinic. STEERING STRATEGIES: In the clinic, different approaches are possible: simple, i.e. target centre point (TCP) steering, often called 'target steering', or only pretreatment planning versus advanced, i.e. active HTP guided steering or image guided hyperthermia by non-invasive thermometry (NIT). The Rotterdam experience: To illustrate the implementation of HTP guided steering, the Rotterdam approach of complaint adaptive steering is elaborated, in which optimisation is adapted with increased constraints on tissues with heat-induced discomfort. CONCLUSIONS: Many publications on HTP show that HTP can be considered a feasible method to optimise and control a hyperthermia treatment, with the objective to enhance treatment quality and documentation. Ultimately, after overcoming the various uncertainties, this may lead to dose prescription.

Real-world analysis of manual editing of deep learning contouring in the thorax region.

Background and purpose: User-adjustments after deep-learning (DL) contouring in radiotherapy were evaluated to get insight in real-world editing during clinical practice. This study assessed the amount, type and spatial regions of editing of auto-contouring for organs-at-risk (OARs) in routine clinical workflow for patients in the thorax region. Materials and methods: A total of 350 lung cancer and 362 breast cancer patients, contoured between March 2020 and March 2021 using a commercial DL-contouring method followed by manual adjustments were retrospectively analyzed. Subsampling was performed for some OARs, using an inter-slice gap of 1-3 slices. Commonly-used whole-organ contouring assessment measures were calculated, and all cases were registered to a common reference shape per OAR to identify regions of manual adjustment. Results were expressed as the median, 10th-90th percentile of adjustment and visualized using 3D renderings. Results: Per OAR, the median amount of editing was below 1 mm. However, large adjustments were found in some locations for most OARs. In general, enlarging of the auto-contours was needed. Subsampling DL-contours showed less adjustments were made in the interpolated slices compared to simulated no-subsampling for these OARs. Conclusion: The real-world performance of automatic DL-contouring software was evaluated and proven useful in clinical practice. Specific regions-of-adjustment were identified per OAR in the thorax region, and separate models were found to be necessary for specific clinical indications different from training data. This analysis showed the need to perform routine clinical analysis especially when procedures or acquisition protocols change to have the best configuration of the workflow.

Clinical implementation and validation of an automated adaptive workflow for proton therapy.

Background and purpose: Treatment quality of proton therapy can be monitored by repeat-computed tomography scans (reCTs). However, manual re-delineation of target contours can be time-consuming. To improve the workflow, we implemented an automated reCT evaluation, and assessed if automatic target contour propagation would lead to the same clinical decision for plan adaptation as the manual workflow. Materials and methods: This study included 79 consecutive patients with a total of 250 reCTs which had been manually evaluated. To assess the feasibility of automated reCT evaluation, we propagated the clinical target volumes (CTVs) deformably from the planning-CT to the reCTs in a commercial treatment planning system. The dose-volume-histogram parameters were extracted for manually re-delineated (CTVmanual) and deformably mapped target contours (CTVauto). It was compared if CTVmanual and CTVauto both satisfied/failed the clinical constraints. Duration of the reCT workflows was also recorded. Results: In 92% (N = 229) of the reCTs correct flagging was obtained. Only 4% (N = 9) of the reCTs presented with false negatives (i.e., at least one clinical constraint failed for CTVmanual, but all constraints were satisfied for CTVauto), while 5% (N = 12) of the reCTs led to a false positive. Only for one false negative reCT a plan adaption was made in clinical practice, i.e., only one adaptation would have been missed, suggesting that automated reCT evaluation was possible. Clinical introduction hereof led to a time reduction of 49 h (from 65 to 16 h). Conclusion: Deformable target contour propagation was clinically acceptable. A script-based automatic reCT evaluation workflow has been introduced in routine clinical practice.

Plan quality assessment in clinical practice: Results of the 2020 ESTRO survey on plan complexity and robustness.

PURPOSE: Plan complexity and robustness are two essential aspects of treatment plan quality but there is a great variability in their management in clinical practice. This study reports the results of the 2020 ESTRO survey on plan complexity and robustness to identify needs and guide future discussions and consensus. METHODS: A survey was distributed online to ESTRO members. Plan complexity was defined as the modulation of machine parameters and increased uncertainty in dose calculation and delivery. Robustness was defined as a dose distribution's sensitivity towards errors stemming from treatment uncertainties, patient setup, or anatomical changes. RESULTS: A total of 126 radiotherapy centres from 33 countries participated, 95 of them (75%) from Europe and Central Asia. The majority controlled and evaluated plan complexity using monitor units (56 centres) and aperture shapes (38 centres). To control robustness, 98 (97% of question responses) photon and 5 (50%) proton centres used PTV margins for plan optimization while 75 (94%) and 5 (50%), respectively, used margins for plan evaluation. Seventeen (21%) photon and 8 (80%) proton centres used robust optimisation, while 10 (13%) and 8 (80%), respectively, used robust evaluation. Primary uncertainties considered were patient setup (photons and protons) and range calculation uncertainties (protons). Participants expressed the need for improved commercial tools to control and evaluate plan complexity and robustness. CONCLUSION: Clinical implementation of methods to control and evaluate plan complexity and robustness is very heterogeneous. Better tools are needed to manage complexity and robustness in treatment planning systems. International guidelines may promote harmonization.

Treatment planning comparison in the PROTECT-trial randomising proton versus photon beam therapy in oesophageal cancer: Results from eight European centres.

PURPOSE: To compare dose distributions and robustness in treatment plans from eight European centres in preparation for the European randomized phase-III PROTECT-trial investigating the effect of proton therapy (PT) versus photon therapy (XT) for oesophageal cancer. MATERIALS AND METHODS: All centres optimized one PT and one XT nominal plan using delineated 4DCT scans for four patients receiving 50.4 Gy (RBE) in 28 fractions. Target volume receiving 95% of prescribed dose (V95%iCTVtotal) should be >99%. Robustness towards setup, range, and respiration was evaluated. The plans were recalculated on a surveillance 4DCT (sCT) acquired at fraction ten and robustness evaluation was performed to evaluate the effect of respiration and inter-fractional anatomical changes. RESULTS: All PT and XT plans complied with V95%iCTVtotal >99% for the nominal plan and V95%iCTVtotal >97% for all respiratory and robustness scenarios. Lung and heart dose varied considerably between centres for both modalities. The difference in mean lung dose and mean heart dose between each pair of XT and PT plans was in median [range] 4.8 Gy [1.1;7.6] and 8.4 Gy [1.9;24.5], respectively. Patients B and C showed large inter-fractional anatomical changes on sCT. For patient B, the minimum V95%iCTVtotal in the worst-case robustness scenario was 45% and 94% for XT and PT, respectively. For patient C, the minimum V95%iCTVtotal was 57% and 72% for XT and PT, respectively. Patient A and D showed minor inter-fractional changes and the minimum V95%iCTVtotal was >85%. CONCLUSION: Large variability in dose to the lungs and heart was observed for both modalities. Inter-fractional anatomical changes led to larger target dose deterioration for XT than PT plans.

Quality assurance for clinical studies in regional deep hyperthermia.

BACKGROUND: A guideline is provided for the implementation of regional deep hyperthermia treatments under strict rules of quality assurance. The objective is to guarantee a comparable and comprehensible method in the treatment and scientific analysis of hyperthermia. The guideline describes regional deep hyperthermia (RHT) and MR-controlled partial body hyperthermia (PBH) of children, young and adult patients. According to this guideline, hyperthermia treatment is always applied in combination with chemotherapy and/or radiotherapy. METHODS: The guideline is based on practical experience from several hyperthermia centers. The procedure allows applying jointly coordinated standards and quality control in hyperthermia for studies. RESULTS: The guideline contains recommendations for hyperthermia treatments, including indication, preparation, treatment, and standardized analysis.

The impact of organ-at-risk contour variations on automatically generated treatment plans for NSCLC.

BACKGROUND AND PURPOSE: Quality of automatic contouring is generally assessed by comparison with manual delineations, but the effect of contour differences on the resulting dose distribution remains unknown. This study evaluated dosimetric differences between treatment plans optimized using various organ-at-risk (OAR) contouring methods. MATERIALS AND METHODS: OARs of twenty lung cancer patients were manually and automatically contoured, after which user-adjustments were made. For each contour set, an automated treatment plan was generated. The dosimetric effect of intra-observer contour variation and the influence of contour variations on treatment plan evaluation and generation were studied using dose-volume histogram (DVH)-parameters for thoracic OARs. RESULTS: Dosimetric effect of intra-observer contour variability was highest for Heart Dmax (3.4 ± 6.8 Gy) and lowest for Lungs-GTV Dmean (0.3 ± 0.4 Gy). The effect of contour variation on treatment plan evaluation was highest for Heart Dmax (6.0 ± 13.4 Gy) and Esophagus Dmax (8.7 ± 17.2 Gy). Dose differences for the various treatment plans, evaluated on the reference (manual) contour, were on average below 1 Gy/1%. For Heart Dmean, higher dose differences were found for overlap with PTV (median 0.2 Gy, 95% 1.7 Gy) vs. no PTV overlap (median 0 Gy, 95% 0.5 Gy). For Dmax-parameters, largest dose difference was found between 0-1 cm distance to PTV (median 1.5 Gy, 95% 4.7 Gy). CONCLUSION: Dose differences arising from automatic contour variations were of the same magnitude or lower than intra-observer contour variability. For Heart Dmean, we recommend delineation errors to be corrected when the heart overlaps with the PTV. For Dmax-parameters, we recommend checking contours if the distance is close to PTV (<5 cm). For the lungs, only obvious large errors need to be adjusted.

Treatment planning and 4D robust evaluation strategy for proton therapy of lung tumors with large motion amplitude.

PURPOSE: Intensity-modulated proton therapy (IMPT) for lung tumors with a large tumor movement is challenging due to loss of robustness in the target coverage. Often an upper cut-off at 5-mm tumor movement is used for proton patient selection. In this study, we propose (1) a robust and easily implementable treatment planning strategy for lung tumors with a movement larger than 5 mm, and (2) a four-dimensional computed tomography (4DCT) robust evaluation strategy for evaluating the dose distribution on the breathing phases. MATERIALS AND METHODS: We created a treatment planning strategy based on the internal target volume (ITV) concept (aim 1). The ITV was created as a union of the clinical target volumes (CTVs) on the eight 4DCT phases. The ITV expanded by 2 mm was the target during robust optimization on the average CT (avgCT). The clinical plan acceptability was judged based on a robust evaluation, computing the voxel-wise min and max (VWmin/max) doses over 28 error scenarios (range and setup errors) on the avgCT. The plans were created in RayStation (RaySearch Laboratories, Stockholm, Sweden) using a Monte Carlo dose engine, commissioned for our Mevion S250i Hyperscan system (Mevion Medical Systems, Littleton, MA, USA). We developed a new 4D robust evaluation approach (4DRobAvg; aim 2). The 28 scenario doses were computed on each individual 4DCT phase. For each scenario, the dose distributions on the individual phases were deformed to the reference phase and combined to a weighted sum, resulting in 28 weighted sum scenario dose distributions. From these 28 scenario doses, VWmin/max doses were computed. This new 4D robust evaluation was compared to two simpler 4D evaluation strategies: re-computing the nominal plan on each individual 4DCT phase (4DNom) and computing the robust VWmin/max doses on each individual phase (4DRobInd). The treatment planning and dose evaluation strategies were evaluated for 16 lung cancer patients with tumor movement of 4-26 mm. RESULTS: The ratio of the ITV and CTV volumes increased linearly with the tumor amplitude, with an average ratio of 1.4. Despite large ITV volumes, a clinically acceptable plan fulfilling all target and organ at risk (OAR) constraints was feasible for all patients. The 4DNom and 4DRobInd evaluation strategies were found to under- or overestimate the dosimetric effect of the tumor movement, respectively. 4DRobInd showed target underdosage for five patients, not observed in the robust evaluation on the avgCT or in 4DRobAvg. The accuracy of dose deformation used in 4DRobAvg was quantified and found acceptable, with differences for the dose-volume parameters below 1 Gy in most cases. CONCLUSION: The proposed ITV-based planning strategy on the avgCT was found to be a clinically feasible approach with adequate tumor coverage and no OAR overdosage even for large tumor movement. The new proposed 4D robust evaluation, 4DRobAvg, was shown to give an easily interpretable understanding of the effect of respiratory motion dose distribution, and to give an accurate estimate of the dose delivered in the different breathing phases.