Towards a European prospective data registry for particle therapy.

The evidence for the value of particle therapy (PT) is still sparse. While randomized trials remain a cornerstone for robust comparisons with photon-based radiotherapy, data registries collecting real-world data can play a crucial role in building evidence for new developments. This Perspective describes how the European Particle Therapy Network (EPTN) is actively working on establishing a prospective data registry encompassing all patients undergoing PT in European centers. Several obstacles and hurdles are discussed, for instance harmonization of nomenclature and structure of technical and dosimetric data and data protection issues. A preferred approach is the adoption of a federated data registry model with transparent and agile governance to meet European requirements for data protection, transfer, and processing. Funding of the registry, especially for operation after the initial setup process, remains a major challenge.

Doses delivered by portal imaging quality assurance in routine practice of adjuvant breast radiotherapy worth to by monitored and compensated in some cases.

BACKGROUND: Imaging, in radiotherapy, has become a routine tool for repositioning of the target volume at each session. The repositioning precision, currently infracentimetric, evolves along with the irradiation techniques. This retrospective study aimed to identify practices and doses resulting from the use of high energy planar imaging (portal imaging) in daily practice. METHODS: A retrospective survey of portal images (PIs) was carried out over 10 years for 2,403 patients and for three linacs (1 Elekta SLi, 2 Varian Clinac) for postoperative mammary irradiations. Images were taken using a standardized number of monitor units (MU) for all patients. Due to the variable sensitivities of the detectors and the possibility of adjustment of the detector-patient distance, the number of MU were 3; 2 and 1 respectively, for Elekta SLi®, Clinac 600® and Clinac 2100®. Then, a representative cumulated dose was calculated in simplified reference conditions (5 cm depth, beam of 10 cm × 10 cm, 6 MV), considering the total number of images taken during the whole treatment course. The consistency between the representative doses and the actual absorbed doses received by the patients was verified by simulating a series of typical cases with the treatment plan dose calculation system. RESULTS: The delivered doses differ significantly between the three linacs. The mean representative dose values by complete treatment were 0.695; 0.241 and 0.216 Gy, respectively, for SLi, Clinac 600 and Clinac 2100. However, 15 patients were exposed to a dose >2 Gy with a maximum dose of 5.05 Gy. The simulated doses were very similar to the representative doses. CONCLUSIONS: A significant dose delivery was highlighted by this study. These representative doses are presently communicated weekly to the radiation oncologist for the radiation protection of their patients. Moreover, they should be taken into account in a possible study of long-term stochastic risks.

Proton therapy for treatment of intracranial benign tumors in adults: A systematic review.

INTRODUCTION: The depth-dose distribution of a proton beam, materialized by the Bragg peak makes it an attractive radiation modality as it reduces exposure of healthy tissues to radiations, compared with photon therapy Prominent indications, based on a long-standing experience are: intraocular melanomas, low-grade skull-base and spinal canal malignancies. However, many others potential indications are under investigations such as the benign morbid conditions that are compatible with an extended life-expectancy: low grade meningiomas, paragangliomas, pituitary adenomas, neurinomas craniopharyngioma or recurrent pleomorphic adenomas. MATERIALS: Given the radiation-induced risk of secondary cancer and the potential neurocognitive and functional alteration with photonic radiotherapy, we systematically analyzed the existing clinical literature about the use of proton therapy as an irradiation modality for cervical or intracranial benign tumors. The aim of this review was to report clinical outcomes of adult patients with benign intracranial or cervical tumors treated with proton therapy and to discuss about potential advantages of proton therapy over intensity modulated radiotherapy or radiosurgery. RESULTS: Twenty-four studies were included. There was no randomized studies. Most studies dealt with low grade meningiomas (n = 9). Studies concerning neurinoma (n = 4), pituitary adenoma (n = 5), paraganglioma (n = 5), or craniopharyngioma (n = 1) were fewer. Whatever the indication, long term local control was systematically higher than 90% and equivalent to series with conventional radiotherapy. CONCLUSION: Proton-therapy for treatment of adult benign intracranial and cervical tumors is safe. Randomized or prospective cohorts with long term cognitive evaluations are needed to assess the real place of proton-therapy in the treatment of adults benign head and neck tumors.

Impact of the NTCP modeling on medical decision to select eligible patient for proton therapy: the usefulness of EUD as an indicator to rank modern photon vs proton treatment plans.

PURPOSE: The aim of this study is to evaluate the impact of normal tissue complication probability (NTCP)-based radiobiological models on the estimated risk for late radiation lung damages. The second goal is to propose a medical decision-making approach to select the eligible patient for particle therapy. MATERIALS AND METHODS: Fourteen pediatric patients undergoing cranio-spinal irradiation were evaluated. For each patient, two treatment plans were generated using photon and proton therapy with the same dose prescriptions. Late radiation damage to lung was estimated using three NTCP concepts: the Lyman-Kutcher-Burman, the equivalent uniform dose (EUD) and the mean lung dose according to the quantitative analysis of normal tissue effects in the clinic QUANTEC review. Wilcoxon paired test was used to calculate p-value. RESULTS: Proton therapy achieved lower lung EUD (Gy). The average NTCP values were significantly lower with proton plans, p < .05, using the three NTCP concepts. However, applying the same TD50/5 using radiobiological models to compare NTCP from proton and photon therapy, the ΔNTCP was not a convincing method to measure the potential benefit of proton therapy. Late radiation pneumonitis estimated from the mean lung dose model correlated with QUANTEC data better. CONCLUSIONS: Treatment effectiveness assessed on NTCP reduction depends on radiobiological predictions and parameters used as inputs for in silico evaluation. Because estimates of absolute NTCP values from LKB and GN models are imprecise due to EUD ≪ TD50/5, a reduction of the EUD value with proton plans would better predict a reduction of dose/toxicity. The EUD concept appears as a robust radiobiological surrogate of the dose distribution to select the optimal patient's plan.

Dosimetrical and radiobiological approach to manage the dosimetric shift in the transition of dose calculation algorithm in radiation oncology: how to improve high quality treatment and avoid unexpected outcomes?

BACKGROUND: For a given prescribed dose of radiotherapy, with the successive generations of dose calculation algorithms, more monitor units (MUs) are generally needed. This is due to the implementation of successive improvements in dose calculation: better heterogeneity correction and more accurate estimation of secondary electron transport contribution. More recently, there is the possibility to report the dose-to-medium, physically more accurate compared to the dose-to-water as the reference one. This last point is a recent concern and the main focus of this study. METHODS: In this paper, we propose steps for a general analysis procedure to estimate the dosimetric alterations, and the potential clinical changes, between a reference algorithm and a new one. This includes dosimetric parameters, gamma index, radiobiology indices based on equivalent uniform dose concept and statistics with bootstrap simulation. Finally, we provide a general recommendation on the clinical use of new algorithms regarding the dose prescription or dose limits to the organs at risks. RESULTS: The dosimetrical and radiobiological data showed a significant effect, which might exceed 5-10%, of the calculation method on the dose the distribution and clinical outcomes for lung cancer patients. Wilcoxon signed rank paired comparisons indicated that the delivered dose in MUs was significantly increased (> 2%) using more advanced dose calculation methods as compared to the reference one. CONCLUSION: This paper illustrates and explains the use of dosimetrical, radiobiologcal and statistical tests for dosimetric comparisons in radiotherapy. The change of dose calculation algorithm may induce a dosimetric shift, which has to be evaluated by the physicists and the oncologists. This includes the impact on tumor control and on the risk of toxicity based on normal tissue dose constraints. In fact, the alteration in dose distribution makes it hard to keep exactly the same tumor control probability along with the same normal tissue complication probability.

[Why proton therapy? And how?] / Apports de la protonthérapie à la radiothérapie d'aujourd'hui, pourquoi, comment ?

Proton therapy is a radiotherapy, based on the use of protons, charged subatomic particles that stop at a given depth depending on their initial energy (pristine Bragg peak), avoiding any output beam, unlike the photons used in most of the other modalities of radiotherapy. Proton therapy has been used for 60 years, but has only become ubiquitous in the last decade because of recent major advances in particle accelerator technology. This article reviews the history of clinical implementation of protons, the nature of the technological advances that now allows its expansion at a lower cost. It also addresses the technical and physical specificities of proton therapy and the clinical situations for which proton therapy may be relevant but requires evidence. Different proton therapy techniques are possible. These are explained in terms of their clinical potential by explaining the current terminology (such as cyclotrons, synchrotrons or synchrocyclotrons, using superconducting magnets, fixed line or arm rotary with passive diffusion delivery or active by scanning) in basic words. The requirements associated with proton therapy are increased due to the precision of the depth dose deposit. The learning curve of proton therapy requires that clinical indications be prioritized according to their associated uncertainties (such as range uncertainties and movement in lung tumors). Many clinical indications potentially fall under proton therapy ultimately. Clinical strategies are explained in a paralleled manuscript.

Proton therapy for locally advanced breast cancer: A systematic review of the literature.

BACKGROUND: Radiation therapy plays a major role in the management of adjuvant breast cancer with nodal involvement, with an iatrogenic increase of cardio-vascular risk. Photon therapy, even with intensity modulation, has the downsides of high mean heart dose and heterogeneous target coverage, particularly in the case of internal mammary irradiation. This systematic review of the literature aims to evaluate proton therapy in locally advanced breast cancer. MATERIAL AND METHODS: PubMed was searched for original full-text articles with the following search terms: «Proton Therapy¼ and «Breast Cancer¼. On-going trials were collected using the words "Breast Cancer" and "Protons". RESULTS: 13 articles met the criteria: 6 with passive proton therapy (Double Scattering), 5 with Pencil Beam Scanning (PBS) and 2 with a combination of both. Proton therapy offered a better target coverage than photons, even compared with intensity modulation radiation therapy (including static or rotational IMRT or tomotherapy). With proton therapy, volumes receiving 95% of the dose were around 98%, with low volumes receiving 105% of the dose. Proton therapy often decreased mean heart dose by a factor of 2 or 3, i.e. 1 Gy with proton therapy versus 3 Gy with conventional 3D, and 6 Gy for IMRT. Lungs were better spared with proton therapy than with photon therapy. Cutaneous toxicity observed with double scattering is improved with PBS. CONCLUSION: Proton therapy reduces mean heart dose in breast cancer irradiation, probably reducing late cardio-vascular toxicity. Large clinical studies will likely confirm a clinical benefit of proton therapy.

Agreement between gamma passing rates using computed tomography in radiotherapy and secondary cancer risk prediction from more advanced dose calculated models.

BACKGROUND: During the past decades, in radiotherapy, the dose distributions were calculated using density correction methods with pencil beam as type 'a' algorithm. The objectives of this study are to assess and evaluate the impact of dose distribution shift on the predicted secondary cancer risk (SCR), using modern advanced dose calculation algorithms, point kernel, as type 'b', which consider change in lateral electrons transport. METHODS: Clinical examples of pediatric cranio-spinal irradiation patients were evaluated. For each case, two radiotherapy treatment plans with were generated using the same prescribed dose to the target resulting in different number of monitor units (MUs) per field. The dose distributions were calculated, respectively, using both algorithms types. A gamma index (γ) analysis was used to compare dose distribution in the lung. The organ equivalent dose (OED) has been calculated with three different models, the linear, the linear-exponential and the plateau dose response curves. The excess absolute risk ratio (EAR) was also evaluated as (EAR = OED type 'b' / OED type 'a'). RESULTS: The γ analysis results indicated an acceptable dose distribution agreement of 95% with 3%/3 mm. Although, the γ-maps displayed dose displacement >1 mm around the healthy lungs. Compared to type 'a', the OED values from type 'b' dose distributions' were about 8% to 16% higher, leading to an EAR ratio >1, ranged from 1.08 to 1.13 depending on SCR models. CONCLUSIONS: The shift of dose calculation in radiotherapy, according to the algorithm, can significantly influence the SCR prediction and the plan optimization, since OEDs are calculated from DVH for a specific treatment. The agreement between dose distribution and SCR prediction depends on dose response models and epidemiological data. In addition, the γ passing rates of 3%/3 mm does not translate the difference, up to 15%, in the predictions of SCR resulting from alternative algorithms. Considering that modern algorithms are more accurate, showing more precisely the dose distributions, but that the prediction of absolute SCR is still very imprecise, only the EAR ratio could be used to rank radiotherapy plans.

The use of TCP based EUD to rank and compare lung radiotherapy plans: in-silico study to evaluate the correlation between TCP with physical quality indices.

BACKGROUND: To apply the equivalent uniform dose (EUD) radiobiological model to estimate the tumor control probability (TCP) scores for treatment plans using different radiobiological parameter settings, and to evaluate the correlation between TCP and physical quality indices of the treatment plans. METHODS: Ten radiotherapy treatment plans for lung cancer were generated. The dose distributions were calculated using anisotropic analytical algorithm (AAA). Dose parameters and quality indices derived from dose volume histograms (DVH) for target volumes were evaluated. The predicted TCP was computed using EUD model with tissue-specific parameter (a=-10). The assumed radiobiological parameter setting for adjuvant therapy [tumor dose to control 50% of the tumor (TCD50) =36.5 Gy and γ50=0.72] and curative intent (TCD50=51.24 Gy and γ50=0.83) were used. The bootstrap method was used to estimate the 95% confidence interval (95% CI). The coefficients (ρ) from Spearman's rank test were calculated to assess the correlation between quality indices with TCP. Wilcoxon paired test was used to calculate P value. RESULTS: The 95% CI of TCP were 70.6-81.5 and 46.6-64.7, respectively, for adjuvant radiotherapy and curative intent. The TCP outcome showed a positive and good correlation with calculated dose to 95% of the target volume (D95%) and minimum dose (Dmin). Consistently, TCP correlate negatively with heterogeneity indices. CONCLUSIONS: This study confirms that more relevant and robust radiobiological parameters setting should be integrated according to cancer type. The positive correlation with quality indices gives chance to improve the clinical out-come by optimizing the treatment plans to maximize the Dmin and D95%. This attempt to increase the TCP should be carried out with the respect of dose constraints for organs at risks. However, the negative correlation with heterogeneity indices shows that the optimization of beam arrangements could be also useful. Attention should be paid to obtain an appropriate optimization of initial plans, when comparing and ranking radiotherapy plans using TCP models, to avoid over or underestimated for TCP outcome.

Correlation between pneumonitis risk in radiation oncology and lung density measured with X-ray computed tomography.

BACKGROUND: The risk of toxicity with radiation oncology for lung cancer limits the maximal radiation dose that can be delivered to thoracic tumors. This study aims at investigating the correlation between normal tissue complication probability (NTCP) and physical lung density by analyzing the computed tomography (CT) scan imaging used for radiotherapy dose planning. METHODS: Data from CT of lung cancer patients (n=10), treated with three dimensional radiotherapy, were selected for this study. The dose was calculated using analytical anisotropic algorithm (AAA). Dose volume histograms (DVH) for healthy lung (lung excluding targets) were calculated. The NTCP for lung radiation induced pneumonitis was computed using initial radiobiological parameters from Lyman-Kutcher and Burman (LKB) model and readjusted parameters for AAA, with α/ß=3. The correlation coefficient "rho" was calculated using Spearman's rank test. The bootstrap method was used to estimate the 95% confidence interval (95% CI). Wilcoxon paired test was used to calculate P values. RESULTS: Bootstrapping simulation revealed significant difference between NTCP computed with the initial radiobiological parameters and that computed with the parameters readjusted for AAA (P=0.03). The results of simulations based on 1,000 replications showed no correlation for NTCP with density, with "rho" <0.3. CONCLUSIONS: For a given set of patients, we assessed the correlation between NTCP and lung density using bootstrap analysis. The lack of correlation could result either from a very accurate dose calculation, by AAA, whatever the lung density yielding a NTCP result only dependant of the dose and not any more of the density; or to the very limited range of natural variation of relative electronic density (0.15 to 0.20) observed in this small series of patients. Another important parameter is the bootstrap simulation with 1,000 random samplings may have underestimated the correlation, since the initial data (n=10) showed a weak correlation.

Assessing the shift of radiobiological metrics in lung radiotherapy plans using 2D gamma index.

BACKGROUND: The purpose of this work is to investigate the 2D gamma (γ) maps to illustrate the change of radiobiological outcomes for lung radiotherapy plans and evaluate the correlation between tumor control probability (TCP), normal tissue complication probability (NTCP) with γ passing rates (γ-rates). METHODS: Nine patients with lung cancer were used. The doses were calculated using Modified Batho method integrated with pencil beam convolution (MB-PBC) and anisotropic analytical algorithm (AAA) using the same beam arrangements and prescription dose. The TCP and NTCP were estimated, respectively, using equivalent uniform dose (EUD) model and Lyman-Kutcher-Burman (LKB) model. The correlation between ΔTCP or ΔNTCP with γ-rates, from 2%/2 and 3%/3 mm, were tested to explore the best correlation predicting the relevant γ criteria using Spearman's rank test (ρ). Wilcoxon paired test was used to calculate P value. RESULTS: TCP value was significantly lower in the recalculated AAA plans as compared to MB plans. However, AAA predicted more NTCP on lung pneumonitis according to the LKB model and using relevant radiobiological parameters (n, m and TD50) for MB-PBC and AAA, with P=0.03. The data showed a weak correlation between radiobiological metrics with γ-rates or γ-mean, ρ<0.3. CONCLUSIONS: AAA and MB yield different TCP values as well as NTCP for lung pneumonitis based on the LKB model parameters. Therefore, 2D γ-maps, generated with 2%/2 or 3%/3 mm, could illustrate visual information about the radiobiological changes. The information is useful to evaluate the clinical outcome of a radiotherapy treatment and to approve the treatment plan of the patient if the dose constraints are respected. On the other hand, the γ-maps tool can be used as quality assurance (QA) process to check the predicted TCP and NTCP from radiobiological models.

Quantitative comparison of dose distribution in radiotherapy plans using 2D gamma maps and X-ray computed tomography.

BACKGROUND: The advanced dose calculation algorithms implemented in treatment planning system (TPS) have remarkably improved the accuracy of dose calculation especially the modeling of electrons transport in the low density medium. The purpose of this study is to evaluate the use of 2D gamma (γ) index to quantify and evaluate the impact of the calculation of electrons transport on dose distribution for lung radiotherapy. METHODS: X-ray computed tomography images were used to calculate the dose for twelve radiotherapy treatment plans. The doses were originally calculated with Modified Batho (MB) 1D density correction method, and recalculated with anisotropic analytical algorithm (AAA), using the same prescribed dose. Dose parameters derived from dose volume histograms (DVH) and target coverage indices were compared. To compare dose distribution, 2D γ-index was applied, ranging from 1%/1 mm to 6%/6 mm. The results were displayed using γ-maps in 2D. Correlation between DVH metrics and γ passing rates was tested using Spearman's rank test and Wilcoxon paired test to calculate P values. RESULTS: the plans generated with AAA predicted more heterogeneous dose distribution inside the target, with P<0.05. However, MB overestimated the dose predicting more coverage of the target by the prescribed dose. The γ analysis showed that the difference between MB and AAA could reach up to ±10%. The 2D γ-maps illustrated that AAA predicted more dose to organs at risks, as well as lower dose to the target compared to MB. CONCLUSIONS: Taking into account of the electrons transport on radiotherapy plans showed a significant impact on delivered dose and dose distribution. When considering the AAA represent the true cumulative dose, a readjusting of the prescribed dose and an optimization to protect the organs at risks should be taken in consideration in order to obtain the better clinical outcome.

Impact of dose calculation models on radiotherapy outcomes and quality adjusted life years for lung cancer treatment: do we need to measure radiotherapy outcomes to tune the radiobiological parameters of a normal tissue complication probability model?

BACKGROUND: The equivalent uniform dose (EUD) radiobiological model can be applied for lung cancer treatment plans to estimate the tumor control probability (TCP) and the normal tissue complication probability (NTCP) using different dose calculation models. Then, based on the different calculated doses, the quality adjusted life years (QALY) score can be assessed versus the uncomplicated tumor control probability (UTCP) concept in order to predict the overall outcome of the different treatment plans. METHODS: Nine lung cancer cases were included in this study. For the each patient, two treatments plans were generated. The doses were calculated respectively from pencil beam model, as pencil beam convolution (PBC) turning on 1D density correction with Modified Batho's (MB) method, and point kernel model as anisotropic analytical algorithm (AAA) using exactly the same prescribed dose, normalized to 100% at isocentre point inside the target and beam arrangements. The radiotherapy outcomes and QALY were compared. The bootstrap method was used to improve the 95% confidence intervals (95% CI) estimation. Wilcoxon paired test was used to calculate P value. RESULTS: Compared to AAA considered as more realistic, the PBCMB overestimated the TCP while underestimating NTCP, P<0.05. Thus the UTCP and the QALY score were also overestimated. CONCLUSIONS: To correlate measured QALY's obtained from the follow-up of the patients with calculated QALY from DVH metrics, the more accurate dose calculation models should be first integrated in clinical use. Second, clinically measured outcomes are necessary to tune the parameters of the NTCP model used to link the treatment outcome with the QALY. Only after these two steps, the comparison and the ranking of different radiotherapy plans would be possible, avoiding over/under estimation of QALY and any other clinic-biological estimates.

Statistic and dosimetric criteria to assess the shift of the prescribed dose for lung radiotherapy plans when integrating point kernel models in medical physics: are we ready?

BACKGROUND: To apply the statistical bootstrap analysis and dosimetric criteria's to assess the change of prescribed dose (PD) for lung cancer to maintain the same clinical results when using new generations of dose calculation algorithms. METHODS: Nine lung cancer cases were studied. For each patient, three treatment plans were generated using exactly the same beams arrangements. In plan 1, the dose was calculated using pencil beam convolution (PBC) algorithm turning on heterogeneity correction with modified batho (PBC-MB). In plan 2, the dose was calculated using anisotropic analytical algorithm (AAA) and the same PD, as plan 1. In plan 3, the dose was calculated using AAA with monitor units (MUs) obtained from PBC-MB, as input. The dosimetric criteria's include MUs, delivered dose at isocentre (Diso) and calculated dose to 95% of the target volume (D95). The bootstrap method was used to assess the significance of the dose differences and to accurately estimate the 95% confidence interval (95% CI). Wilcoxon and Spearman's rank tests were used to calculate P values and the correlation coefficient (ρ). RESULTS: Statistically significant for dose difference was found using point kernel model. A good correlation was observed between both algorithms types, with ρ>0.9. Using AAA instead of PBC-MB, an adjustment of the PD in the isocentre is suggested. CONCLUSIONS: For a given set of patients, we assessed the need to readjust the PD for lung cancer using dosimetric indices and bootstrap statistical method. Thus, if the goal is to keep on with the same clinical results, the PD for lung tumors has to be adjusted with AAA. According to our simulation we suggest to readjust the PD by 5% and an optimization for beam arrangements to better protect the organs at risks (OARs).

Statistical control process to compare and rank treatment plans in radiation oncology: impact of heterogeneity correction on treatment planning in lung cancer.

BACKGROUND: This study proposes a statistical process to compare different treatment plans issued from different irradiation techniques or different treatment phases. This approach aims to provide arguments for discussion about the impact on clinical results of any condition able to significantly alter dosimetric or ballistic related data. METHODS: The principles of the statistical investigation are presented in the framework of a clinical example based on 40 fields of radiotherapy for lung cancers. Two treatment plans were generated for each patient making a change of dose distribution due to variation of lung density correction. The data from 2D gamma index (γ) including the pixels having γ≤1 were used to determine the capability index (Cp) and the acceptability index (Cpk) of the process. To measure the strength of the relationship between the γ passing rates and the Cp and Cpk indices, the Spearman's rank non-parametric test was used to calculate P values. RESULTS: The comparison between reference and tested plans showed that 95% of pixels have γ≤1 with criteria (6%, 6 mm). The values of the Cp and Cpk indices were lower than one showing a significant dose difference. The data showed a strong correlation between γ passing rates and the indices with P>0.8. CONCLUSIONS: The statistical analysis using Cp and Cpk, show the significance of dose differences resulting from two plans in radiotherapy. These indices can be used for adaptive radiotherapy to measure the difference between initial plan and daily delivered plan. The significant changes of dose distribution could raise the question about the continuity to treat the patient with the initial plan or the need for adjustments.

The choice of statistical methods for comparisons of dosimetric data in radiotherapy.

PURPOSE: Novel irradiation techniques are continuously introduced in radiotherapy to optimize the accuracy, the security and the clinical outcome of treatments. These changes could raise the question of discontinuity in dosimetric presentation and the subsequent need for practice adjustments in case of significant modifications. This study proposes a comprehensive approach to compare different techniques and tests whether their respective dose calculation algorithms give rise to statistically significant differences in the treatment doses for the patient. METHODS: Statistical investigation principles are presented in the framework of a clinical example based on 62 fields of radiotherapy for lung cancer. The delivered doses in monitor units were calculated using three different dose calculation methods: the reference method accounts the dose without tissues density corrections using Pencil Beam Convolution (PBC) algorithm, whereas new methods calculate the dose with tissues density correction for 1D and 3D using Modified Batho (MB) method and Equivalent Tissue air ratio (ETAR) method, respectively. The normality of the data and the homogeneity of variance between groups were tested using Shapiro-Wilks and Levene test, respectively, then non-parametric statistical tests were performed. Specifically, the dose means estimated by the different calculation methods were compared using Friedman's test and Wilcoxon signed-rank test. In addition, the correlation between the doses calculated by the three methods was assessed using Spearman's rank and Kendall's rank tests. RESULTS: The Friedman's test showed a significant effect on the calculation method for the delivered dose of lung cancer patients (p <0.001). The density correction methods yielded to lower doses as compared to PBC by on average (-5 ± 4.4 SD) for MB and (-4.7 ± 5 SD) for ETAR. Post-hoc Wilcoxon signed-rank test of paired comparisons indicated that the delivered dose was significantly reduced using density-corrected methods as compared to the reference method. Spearman's and Kendall's rank tests indicated a positive correlation between the doses calculated with the different methods. CONCLUSION: This paper illustrates and justifies the use of statistical tests and graphical representations for dosimetric comparisons in radiotherapy. The statistical analysis shows the significance of dose differences resulting from two or more techniques in radiotherapy.

Implementation and validation of a fluence pencil kernels model for GaN-based dosimetry in photon beam radiotherapy.

Gallium nitride (GaN), a direct-gap semiconductor that is radioluminescent, can be used as a transducer yielding a high signal from a small detecting volume and thus potentially suitable for use in small fields and for high dose gradients. A common drawback of semiconductor dosimeters with effective atomic numbers higher than soft tissues is that their responses depend on the presence of low energy photons for which the photoelectric cross section varies strongly with atomic number, which may affect the accuracy of dosimetric measurements. To tackle this 'over-response' issue, we propose a model for GaN-based dosimetry with readout correction. The local photon spectrum is calculated by convolving fluence pencil kernel spectra with the beam aperture fluence distribution. The response of a GaN detector is modelled by combining large cavity theory and small cavity theory for the low and high energy components of the local spectrum. Monte Carlo simulations are employed for determination of specific correction factors for different GaN transducer sizes and irradiation conditions. Some model parameters such as the cut-off energy and partitioning energy are discussed. The accuracy of the GaN dosimetric response model has been evaluated for tissue phantom ratio experiments along the central axis. These experiments have shown that calculated and measured GaN responses stay within ±3% at all depths beyond the build-up depth. The calculated GaN response factor is also in good agreement with measured data (±2.5%). The validated model with response compensation improves significantly the accuracy of dosimetric measurements: below 2.5% deviation as compared to 13% without compensation, for a 10 × 10 cm(2) field, at depth from 1.5 to 22 cm.