Clinical validation of the LKB model and parameter sets for predicting radiation-induced pneumonitis from breast cancer radiotherapy

The choice of the appropriate model and parameter set in determining the relation between the incidence of radiation pneumonitis and dose distribution in the lung is of great importance, especially in the case of breast radiotherapy where the observed incidence is fairly low. From our previous study based on 150 breast cancer patients, where the fits of dose-volume models to clinical data were estimated (Tsougos et al 2005 Evaluation of dose-response models and parameters predicting radiation induced pneumonitis using clinical data from breast cancer radiotherapy Phys. Med. Biol. 50 3535-54), one could get the impression that the relative seriality is significantly better than the LKB NTCP model. However, the estimation of the different NTCP models was based on their goodness-of-fit on clinical data, using various sets of published parameters from other groups, and this fact may provisionally justify the results. Hence, we sought to investigate further the LKB model, by applying different published parameter sets for the very same group of patients, in order to be able to compare the results. It was shown that, depending on the parameter set applied, the LKB model is able to predict the incidence of radiation pneumonitis with acceptable accuracy, especially when implemented on a sub-group of patients (120) receiving [see text]|EUD higher than 8 Gy. In conclusion, the goodness-of-fit of a certain radiobiological model on a given clinical case is closely related to the selection of the proper scoring criteria and parameter set as well as to the compatibility of the clinical case from which the data were derived.     

NTCP modelling and pulmonary function tests evaluation for the prediction of radiation induced pneumonitis in non-small-cell lung cancer radiotherapy

This work aims to evaluate the predictive strength of the relative seriality, parallel and Lyman-Kutcher-Burman (LKB) normal tissue complication probability (NTCP) models regarding the incidence of radiation pneumonitis (RP), in a group of patients following lung cancer radiotherapy and also to examine their correlation with pulmonary function tests (PFTs). The study was based on 47 patients who received radiation therapy for stage III non-small-cell lung cancer. For each patient, lung dose volume histograms (DVHs) and the clinical treatment outcome were available. Clinical symptoms, radiological findings and pulmonary function tests incorporated in a post-treatment follow-up period of 18 months were used to assess the manifestation of radiation induced complications. Thirteen of the 47 patients were scored as having radiation induced pneumonitis, with RTOG criteria grade 3 and 28 of the 47 with RTOG criteria grade 2. Using this material, different methods of estimating the likelihood of radiation effects were evaluated, by analysing patient data based on their full dose distributions and associating the calculated complication rates with the clinical follow-up records. Lungs were evaluated as a paired organ as well as individual lungs. Of the NTCP models examined in the overall group considering the dose distribution in the ipsilateral lung, all models were able to predict radiation induced pneumonitis only in the case of grade 2 radiation pneumonitis score, with the LKB model giving the best results (chi2-test: probability of agreement between the observed and predicted results Pchi(chi2)=0.524 using the 0.05 significance level). The NTCP modelling considering lungs as a paired organ did not give statistically acceptable results. In the case of lung cancer radiotherapy, the application of different published radiobiological parameters alters the NTCP results, but not excessively as in the case of breast cancer radiotherapy. In this relatively small group of lung cancer patients, no positive statistical correlation could be established between the incidence of radiation pneumonitis as estimated by NTCP models and the pulmonary function test evaluation. However, the use of PFTs as markers or predictors for the incidence or severity of radiation induced pneumonitis must be investigated further.     

Lyman-Kutcher-Burman NTCP model parameters for radiation pneumonitis and xerostomia based on combined analysis of published clinical data

Knowledge of accurate parameter estimates is essential for incorporating normal tissue complication probability (NTCP) models into biologically based treatment planning. The purpose of this work is to derive parameter estimates for the Lyman-Kutcher-Burman (LKB) NTCP model using a combined analysis of multi-institutional toxicity data for the lung (radiation pneumonitis) and parotid gland (xerostomia). A series of published clinical datasets describing dose response for radiation pneumonitis (RP) and xerostomia were identified for this analysis. The data support the notion of large volume effect for the lung and parotid gland with the estimates of the n parameter being close to unity. Assuming that n = 1, the m and TD(50) parameters of the LKB model were estimated by the maximum likelihood method from plots of complication rate as a function of mean organ dose. Ninety five percent confidence intervals for parameter estimates were obtained by the profile likelihood method. If daily fractions other than 2 Gy had been used in a published report, mean organ doses were converted to 2 Gy/fraction-equivalent doses using the linear-quadratic (LQ) formula with alpha/beta = 3 Gy. The following parameter estimates were obtained for the endpoint of symptomatic RP when the lung is considered a paired organ: m = 0.41 (95% CI 0.38, 0.45) and TD(50) = 29.9 Gy (95% CI 28.2, 31.8). When RP incidence was evaluated as a function of dose to the ipsilateral lung rather than total lung, estimates were m = 0.35 (95% CI 0.29, 0.43) and TD(50) = 37.6 Gy (95% CI 34.6, 41.4). For xerostomia expressed as reduction in stimulated salivary flow below 25% within six months after radiotherapy, the following values were obtained: m = 0.53 (95% CI 0.45, 0.65) and TD(50) = 31.4 Gy (95% CI 29.1, 34.0). Although a large number of parameter estimates for different NTCP models and critical structures exist and continue to appear in the literature, it is hard to justify the use of any single parameter set obtained at a selected institution for the purposes of biologically based treatment planning. Our expectation is that the proposed model parameters based on cumulative experience at various institutions are more representative of the overall practice of radiation therapy than any single-institution data, and could be more readily incorporated into clinical use.     

Sensitivity of NTCP parameter values against a change of dose calculation algorithm

Optimization of radiation treatment planning requires estimations of the normal tissue complication probability (NTCP). A number of models exist that estimate NTCP from a calculated dose distribution. Since different dose calculation algorithms use different approximations the dose distributions predicted for a given treatment will in general depend on the algorithm. The purpose of this work is to test whether the optimal NTCP parameter values change significantly when the dose calculation algorithm is changed. The treatment plans for 17 breast cancer patients have retrospectively been recalculated with a collapsed cone algorithm (CC) to compare the NTCP estimates for radiation pneumonitis with those obtained from the clinically used pencil beam algorithm (PB). For the PB calculations the NTCP parameters were taken from previously published values for three different models. For the CC calculations the parameters were fitted to give the same NTCP as for the PB calculations. This paper demonstrates that significant shifts of the NTCP parameter values are observed for three models, comparable in magnitude to the uncertainties of the published parameter values. Thus, it is important to quote the applied dose calculation algorithm when reporting estimates of NTCP parameters in order to ensure correct use of the models.     

Convex reformulation of biologically-based multi-criteria intensity-modulated radiation therapy optimization including fractionation effects

Finding fluence maps for intensity-modulated radiation therapy (IMRT) can be formulated as a multi-criteria optimization problem for which Pareto optimal treatment plans exist. To account for the dose-per-fraction effect of fractionated IMRT, it is desirable to exploit radiobiological treatment plan evaluation criteria based on the linear-quadratic (LQ) cell survival model as a means to balance the radiation benefits and risks in terms of biologic response. Unfortunately, the LQ-model-based radiobiological criteria are nonconvex functions, which make the optimization problem hard to solve. We apply the framework proposed by Romeijn et al (2004 Phys. Med. Biol. 49 1991-2013) to find transformations of LQ-model-based radiobiological functions and establish conditions under which transformed functions result in equivalent convex criteria that do not change the set of Pareto optimal treatment plans. The functions analysed are: the LQ-Poisson-based model for tumour control probability (TCP) with and without inter-patient heterogeneity in radiation sensitivity, the LQ-Poisson-based relative seriality s-model for normal tissue complication probability (NTCP), the equivalent uniform dose (EUD) under the LQ-Poisson model and the fractionation-corrected Probit-based model for NTCP according to Lyman, Kutcher and Burman. These functions differ from those analysed before in that they cannot be decomposed into elementary EUD or generalized-EUD functions. In addition, we show that applying increasing and concave transformations to the convexified functions is beneficial for the piecewise approximation of the Pareto efficient frontier.     

Application of constrained optimization to radiotherapy planning

Essential for the calculation of photon fluence distributions for intensity modulated radiotherapy (IMRT) is the use of a suitable objective function. The objective function should reflect the clinical aims of tumor control and low side effect probability. Individual radiobiological parameters for patient organs are not yet available with sufficient accuracy. Some of the major drawbacks of some current optimization methods include an inability to converge to a solution for arbitrary input parameters, and/or a need for intensive user input in order to guide the optimization. In this work, a constrained optimization method was implemented and tested. It is closely related to the demanded clinical aims, avoiding the drawbacks mentioned above. In a prototype treatment planning system for IMRT, tumor control was guaranteed by setting a lower boundary for target dose. The aim of low complication is fulfilled by minimizing the dose to organs at risk. If only one type of tissue is involved, there is no absolute need for radiobiological parameters. For different organs, threshold dose, relative seriality of the organs or an upper dose limit could be set. All parameters, however, were optional, and could be omitted. Dose-volume constraints were not used, avoiding the possibility of local minima in the objective function. The approach was benchmarked through the simulation of both a head and neck and a lung case. A cylinder phantom with precalculated dose distributions of individual pencil beams was used. The dose to regions at risk could be significantly reduced using at least seven ports of beam incidence. Increasing the number of ports beyond seven produced only minor further gain. The relative seriality of organs was modeled through the use of an added exponent to the dose. This approach however increased calculation time significantly. The alternative of setting an upper limit is much faster and allows direct control of the maximum dose. Constrained optimization guarantees high tumor control probability, it is computationally more efficient than adding penalty terms to the objective function, and the input parameters are dose limits known in clinical practice.     

Monte Carlo role in radiobiological modelling of radiotherapy outcomes

Radiobiological models are essential components of modern radiotherapy. They are increasingly applied to optimize and evaluate the quality of different treatment planning modalities. They are frequently used in designing new radiotherapy clinical trials by estimating the expected therapeutic ratio of new protocols. In radiobiology, the therapeutic ratio is estimated from the expected gain in tumour control probability (TCP) to the risk of normal tissue complication probability (NTCP). However, estimates of TCP/NTCP are currently based on the deterministic and simplistic linear-quadratic formalism with limited prediction power when applied prospectively. Given the complex and stochastic nature of the physical, chemical and biological interactions associated with spatial and temporal radiation induced effects in living tissues, it is conjectured that methods based on Monte Carlo (MC) analysis may provide better estimates of TCP/NTCP for radiotherapy treatment planning and trial design. Indeed, over the past few decades, methods based on MC have demonstrated superior performance for accurate simulation of radiation transport, tumour growth and particle track structures; however, successful application of modelling radiobiological response and outcomes in radiotherapy is still hampered with several challenges. In this review, we provide an overview of some of the main techniques used in radiobiological modelling for radiotherapy, with focus on the MC role as a promising computational vehicle. We highlight the current challenges, issues and future potentials of the MC approach towards a comprehensive systems-based framework in radiobiological modelling for radiotherapy.     

Statistical methods for clinical verification of dose-response parameters related to esophageal stricture and AVM obliteration from radiotherapy

The purpose of this work is to provide some statistical methods for evaluating the predictive strength of radiobiological models and the validity of dose-response parameters for tumour control and normal tissue complications. This is accomplished by associating the expected complication rates, which are calculated using different models, with the clinical follow-up records. These methods are applied to 77 patients who received radiation treatment for head and neck cancer and 85 patients who were treated for arteriovenous malformation (AVM). The three-dimensional dose distribution delivered to esophagus and AVM nidus and the clinical follow-up results were available for each patient. Dose-response parameters derived by a maximum likelihood fitting were used as a reference to evaluate their compatibility with the examined treatment methodologies. The impact of the parameter uncertainties on the dose-response curves is demonstrated. The clinical utilization of the radiobiological parameters is illustrated. The radiobiological models (relative seriality and linear Poisson) and the reference parameters are validated to prove their suitability in reproducing the treatment outcome pattern of the patient material studied (through the probability of finding a worse fit, area under the ROC curve and chi2 test). The analysis was carried out for the upper 5 cm of the esophagus (proximal esophagus) where all the strictures are formed, and the total volume of AVM. The estimated confidence intervals of the dose-response curves appear to have a significant supporting role on their clinical implementation and use.     

Biological optimization of heterogeneous dose distributions in systemic radiotherapy

The standard computational method developed for internal radiation dosimetry is the MIRD (medical internal radiation dose) formalism, based on the assumption that tumor control is given by uniform dose and activity distributions. In modern systemic radiotherapy, however, the need for full 3D dose calculations that take into account the heterogeneous distribution of activity in the patient is now understood. When information on nonuniform distribution of activity becomes available from functional imaging, a more patient specific 3D dosimetry can be performed. Application of radiobiological models can be useful to correlate the calculated heterogeneous dose distributions to the current knowledge on tumor control probability of a homogeneous dose distribution. Our contribution to this field is the introduction of a parameter, the F factor, already used by our group in studying external beam radiotherapy treatments. This parameter allows one to write a simplified expression for tumor control probability (TCP) based on the standard linear quadratic (LQ) model and Poisson statistics. The LQ model was extended to include different treatment regimes involving source decay, incorporating the repair "micro" of sublethal radiation damage, the relative biological effectiveness and the effective "waste" of dose delivered when repopulation occurs. The sensitivity of the F factor against radiobiological parameters (alpha, beta, micro) and the influence of the dose volume distribution was evaluated. Some test examples for 131I and 90Y labeled pharmaceuticals are described to further explain the properties of the F factor and its potential applications. To demonstrate dosimetric feasibility and advantages of the proposed F factor formalism in systemic radiotherapy, we have performed a retrospective planning study on selected patient case. F factor formalism helps to assess the total activity to be administered to the patient taking into account the heterogeneity in activity uptake and dose distribution, giving the same TCP of a homogeneous prescribed dose distribution. Animal studies and collection of standardized clinical data are needed to ascertain the effects of nonuniform dose distributions and to better assess the radiobiological input parameters of the model based on LQ model.     

利用放射生物学模型比较周围型肺癌立体定向放射治疗中的3种剂量方案

目的:利用放射生物学模型比较周围型肺癌立体定向放射治疗（SBRT）中3种常见剂量方案3×15 Gy、4×12 Gy和3×18 Gy在肿瘤局部控制率（TCP）、放射性肺炎（RIP）、胸壁疼痛（CWP）和肋骨骨折（RIRF）发生概率方面的差异。方法:收集20例周围型早期肺癌的CT图像资料，分别采用3种剂量方案设计放射治疗计划，利用放射生物学模型计算每种剂量方案的TCP、RIP、CWP和RIRF概率值。结果:3种剂量方案对TCP没有显著影响；4×12 Gy剂量方案的放疗毒性最低，3×15 Gy放疗毒性稍有增高，而3×18 Gy的毒性最高；3×18 Gy剂量方案的RIP、CWP和RIRF发生概率均明显高于其它两种剂量方案。结论:3种剂量方案的TCP数值没有显著差别，4×12 Gy剂量方案的放疗毒性最低，3×18 Gy剂量方案显著增加RIP、CWP和RIRF的发生概率，建议临床治疗前应根据肿瘤与危及器官的毗邻关系选择合适的剂量方案，实施个体化治疗策略。     

肺癌放射治疗后放射性肺炎与剂量体积直方图相关性分析

目的 分析接受图像引导放射治疗（IGRT）的肺癌患者其剂量体积直方图（DVH）的参数设定与放射性肺炎（RP）发生情况的相关性。方法 选取我科行放射治疗的肺癌患者50例,分析DVH中接受5、10、20、30 Gy照射的肺体积占全肺体积的百分比（V5、V10、V20、V30）和平均照射肺体积（MLD）,以及年龄、性别、吸烟史、化疗史、肺部疾病史、肿瘤位置、病理类型、总剂量、射野个数等临床指标。根据随访结果及临床表现,把患者分为无RP组（0级）和有RP组（1~4级）,比较所有因素与放射性肺炎的相关性。结果 放射性肺炎发生率为58.00%（29/50）,其中无RP组（0级）21例（42.00%）,有RP组（1~4级）中1级 19例,2级8例,3级2例,无4级放射性肺炎发生,分析显示所有剂量学参数以及临床指标中吸烟史和化疗史与放射性肺炎发生有统计学意义（P＜0.05）,而其他临床指标如年龄、性别、肿瘤位置及病理分型,放疗总剂量及射野个数与放射性肺炎发生无明显相关。结论 DVH上所有参数均可以作为评估及预测放射性肺炎发生的指标,同时有化疗史和吸烟史的患者在治疗时尤其需要控制剂量。     

Analysis of a large number of clinical studies for breast cancer radiotherapy: estimation of radiobiological parameters for treatment planning

Numerous studies of early-stage breast cancer treated with breast conserving surgery (BCS) and radiotherapy (RT) have been published in recent years. Both external beam radiotherapy (EBRT) and/or brachytherapy (BT) with different fractionation schemes are currently used. The present RT practice is largely based on empirical experience and it lacks a reliable modelling tool to compare different RT modalities or to design new treatment strategies. The purpose of this work is to derive a plausible set of radiobiological parameters that can be used for RT treatment planning. The derivation is based on existing clinical data and is consistent with the analysis of a large number of published clinical studies on early-stage breast cancer. A large number of published clinical studies on the treatment of early breast cancer with BCS plus RT (including whole breast EBRT with or without a boost to the tumour bed, whole breast EBRT alone, brachytherapy alone) and RT alone are compiled and analysed. The linear quadratic (LQ) model is used in the analysis. Three of these clinical studies are selected to derive a plausible set of LQ parameters. The potential doubling time is set a priori in the derivation according to in vitro measurements from the literature. The impact of considering lower or higher T(pot) is investigated. The effects of inhomogeneous dose distributions are considered using clinically representative dose volume histograms. The derived LQ parameters are used to compare a large number of clinical studies using different regimes (e.g., RT modality and/or different fractionation schemes with different prescribed dose) in order to validate their applicability. The values of the equivalent uniform dose (EUD) and biologically effective dose (BED) are used as a common metric to compare the biological effectiveness of each treatment regime. We have obtained a plausible set of radiobiological parameters for breast cancer: alpha = 0.3 Gy(-1), alpha/beta = 10 Gy and sub-lethal damage repair time T(rep) = 1 h (mono-exponential behaviour is assumed). This set of parameters is consistent with in vitro experiments and with previously reported analyses. Using this set of parameters, we have found that most of the studies, using BCS plus whole breast RT and a boost to the tumour bed, have EUDs ranging from 60-70 Gy. No correlation is found between BED and the local recurrence rate. The treatments of BCS plus brachytherapy alone have a wide range of EUD (30-50 Gy), which is significantly lower than the treatments with whole breast EBRT plus a boost of the tumour bed. The studies with different fractionation schemes for whole breast EBRT also show a significant variation of EUD. Carefully designed clinical studies with large numbers of patients are required to determine clinically the relative effectiveness of these treatment variations. The derived LQ parameter set based on clinical data is consistent with in vitro experiments and previous studies. As demonstrated in the present work, these radiobiological parameters can be potentially useful in radiotherapy treatment planning for early breast cancer, e.g., in comparing biological effectiveness of different radiotherapy modalities, different fractionation schemes and in designing new treatment strategies.     

基于自动勾画和快速蒙特卡罗计算的放射治疗全身器官剂量数据库平台的可行性研究

目的:探讨建立一种放射治疗全身器官剂量数据库平台的可行性。方法:使用基于深度学习的自动勾画软件DeepViewer?1例食管癌患者的全身CT上勾画全身器官，然后利用基于GPU加速的蒙特卡罗软件ARCHER计算相应的器官剂量分布，最后利用Lyman-Kutcher-Burman（LKB）模型评估放疗患者正常组织并发症概率（NTCP）。结果:针对该病例，成功建立基于DeepViewer?ARCHER和LKB模型的全身器官剂量数据库，发现距离靶区越近的器官剂量越大，其中心脏与靶区间距离最小，剂量为14.11 Gy，但因其模型参数特殊，通过LKB模型计算的NTCP为0.00%；左、右肺的剂量分别为3.19和1.16 Gy，但是NTCP值却很大，分别为2.13%和1.60%。对于距离靶区较远的头颈部器官（视交叉、视神经和眼）和腹部器官（直肠、膀胱和股骨头）剂量分别约为9和2 mGy，并且NTCP均近似为0.00%。结论:研究结果证明通过自动勾画软件DeepViewer?蒙特卡罗软件ARCHER和LKB模型建立全身器官剂量数据库的可行性。     

Radiobiological characterization of post-lumpectomy focal brachytherapy with lipid nanoparticle-carried radionuclides

Post-operative radiotherapy has commonly been used for early stage breast cancer to treat residual disease. The primary objective of this work was to characterize, through dosimetric and radiobiological modeling, a novel focal brachytherapy technique which uses direct intracavitary infusion of β-emitting radionuclides (186Re/188Re) carried by lipid nanoparticles (liposomes). Absorbed dose calculations were performed for a spherical lumpectomy cavity with a uniformly injected activity distribution using a dose point kernel convolution technique. Radiobiological indices were used to relate predicted therapy outcome and normal tissue complication of this technique with equivalent external beam radiotherapy treatment regimens. Modeled stromal damage was used as a measure of the inhibition of the stimulatory effect on tumor growth driven by the wound healing response. A sample treatment plan delivering 50 Gy at a therapeutic range of 2.0 mm for 186Re-liposomes and 5.0 mm for 188Re-liposomes takes advantage of the dose delivery characteristics of the β-emissions, providing significant EUD (58.2 Gy and 72.5 Gy for 186Re and 188Re, respectively) with a minimal NTCP (0.046%) of the healthy ipsilateral breast. Modeling of kidney BED and ipsilateral breast NTCP showed that large injected activity concentrations of both radionuclides could be safely administered without significant complications.     

Cancer in the contralateral breast after radiotherapy for breast cancer.

BACKGROUND. Patients with breast cancer have a threefold increase in the risk that a second breast cancer will develop. Radiation treatment for the initial cancer can result in moderately high doses to the contralateral breast, possibly contributing to this heightened risk. METHODS. We conducted a case-control study in a cohort of 41,109 women diagnosed with breast cancer between 1935 and 1982 in Connecticut. We reviewed the medical records of 655 women in whom a second breast cancer developed five or more years after the initial tumor and compared their radiation exposure with that of 1189 matched controls from the cohort who did not have a second cancer. The dose of radiation to the contralateral breast was estimated from the original radiotherapy records. Among the exposed women, the average radiation dose to the contralateral breast was 2.82 Gy (maximum, 7.10). RESULTS. Overall, 23 percent of the women who had a second breast cancer and 20 percent of the controls had received radiotherapy (relative risk of a second breast cancer associated with radiotherapy, 1.19). Among women who survived for at least 10 years, radiation treatment was associated with a small but marginally significant elevation in the risk of a second breast cancer (relative risk, 1.33); the risk increased significantly with the dose of radiation. An increase in risk in association with radiotherapy was evident only among women who were under 45 years of age when they were treated (relative risk, 1.59) and not among older women (relative risk, 1.01). CONCLUSIONS. Radiotherapy for breast cancer contributes little to the already high risk of a second cancer in the opposite breast. Fewer than 3 percent of all second breast cancers in this study could be attributed to previous radiation treatment; the risk, however, was significantly increased among women who underwent irradiation at a relatively young age (less than 45 years). Radiation exposure after the age of 45 entails little, if any, risk of radiation-induced breast cancer.     

Decomposition analysis of differential dose volume histograms

Dose volume histograms are a common tool to assess the value of a treatment plan for various forms of radiation therapy treatment. The purpose of this work is to introduce, validate, and apply a set of tools to analyze differential dose volume histograms by decomposing them into physically and clinically meaningful normal distributions. A weighted sum of the decomposed normal distributions (e.g., weighted dose) is proposed as a new measure of target dose, rather than the more unstable point dose. The method and its theory are presented and validated using simulated distributions. Additional validation is performed by analyzing simple four field box techniques encompassing a predefined target, using different treatment energies inside a water phantom. Furthermore, two clinical situations are analyzed using this methodology to illustrate practical usefulness. A comparison of a treatment plan for a breast patient using a tangential field setup with wedges is compared to a comparable geometry using dose compensators. Finally, a normal tissue complication probability (NTCP) calculation is refined using this decomposition. The NTCP calculation is performed on a liver as organ at risk in a treatment of a mesothelioma patient with involvement of the right lung. The comparison of the wedged breast treatment versus the compensator technique yields comparable classical dose parameters (e.g., conformity index approximately = 1 and equal dose at the ICRU dose point). The methodology proposed here shows a 4% difference in weighted dose outlining the difference in treatment using a single parameter instead of at least two in a classical analysis (e.g., mean dose, and maximal dose, or total dose variance). NTCP-calculations for the mesothelioma case are generated automatically and show a 3% decrease with respect to the classical calculation. The decrease is slightly dependant on the fractionation and on the alpha/beta-value utilized. In conclusion, this method is able to distinguish clinically important differences between treatment plans using a single parameter. This methodology shows promise as an objective tool for analyzing NTCP and doses in larger studies, as the only information needed is the dose volume histogram.     

Three-dimensional heart dose reconstruction to estimate normal tissue complication probability after breast irradiation using portal dosimetry

Irradiation of the heart is one of the major concerns during radiotherapy of breast cancer. Three-dimensional (3D) treatment planning would therefore be useful but cannot always be performed for left-sided breast treatments, because CT data may not be available. However, even if 3D dose calculations are available and an estimate of the normal tissue damage can be made, uncertainties in patient positioning may significantly influence the heart dose during treatment. Therefore, 3D reconstruction of the actual heart dose during breast cancer treatment using electronic imaging portal device (EPID) dosimetry has been investigated. A previously described method to reconstruct the dose in the patient from treatment portal images at the radiological midsurface was used in combination with a simple geometrical model of the irradiated heart volume to enable calculation of dose-volume histograms (DVHs), to independently verify this aspect of the treatment without using 3D data from a planning CT scan. To investigate the accuracy of our method, the DVHs obtained with full 3D treatment planning system (TPS) calculations and those obtained after resampling the TPS dose in the radiological midsurface were compared for fifteen breast cancer patients for whom CT data were available. In addition, EPID dosimetry as well as 3D dose calculations using our TPS, film dosimetry, and ionization chamber measurements were performed in an anthropomorphic phantom. It was found that the dose reconstructed using EPID dosimetry and the dose calculated with the TPS agreed within 1.5% in the lung/heart region. The dose-volume histograms obtained with EPID dosimetry were used to estimate the normal tissue complication probability (NTCP) for late excess cardiac mortality. Although the accuracy of these NTCP calculations might be limited due to the uncertainty in the NTCP model, in combination with our portal dosimetry approach it allows incorporation of the actual heart dose. For the anthropomorphic phantom, and for fifteen patients for whom CT data were available to test our method, the average difference between the NTCP values obtained with our method and those resulting from the dose distributions calculated with the TPS was 0.1% +/- 0.3% (1 SD). Most NTCP values were 1%-2% lower than those obtained using the method described by Hurkmans et al. [Radiother. Oncol. 62, 163-171 (2002)], using the maximum heart distance determined from a simulator image as a single pre-treatment parameter. A similar difference between the two methods was found for twelve patients using in vivo EPID dosimetry; the average NTCP value obtained with EPID dosimetry was 0.9%, whereas an average NTCP value of 2.2% was derived using the method of Hurkmans et al. The results obtained in this study show that EPID dosimetry is well suited for in vivo verification of the heart dose during breast cancer treatment, and can be used to estimate the NTCP for late excess cardiac mortality. To the best of our knowledge, this is the first study using portal dosimetry to calculate a DVH and NTCP of an organ at risk.     

Analytical modelling of regional radiotherapy dose response of lung

Knowledge of the dose-response of radiation-induced lung disease (RILD) is necessary for optimization of radiotherapy (RT) treatment plans involving thoracic cavity irradiation. This study models the time-dependent relationship between local radiation dose and post-treatment lung tissue damage measured by computed tomography (CT) imaging. Fifty-eight follow-up diagnostic CT scans from 21 non-small-cell lung cancer patients were examined. The extent of RILD was segmented on the follow-up CT images based on the increase of physical density relative to the pre-treatment CT image. The segmented RILD was locally correlated with dose distribution calculated by analytical anisotropic algorithm and the Monte Carlo method to generate the corresponding dose-response curves. The Lyman-Kutcher-Burman (LKB) model was fit to the dose-response curves at six post-RT time periods, and temporal change in the LKB parameters was recorded. In this study, we observed significant correlation between the probability of lung tissue damage and the local dose for 96% of the follow-up studies. Dose-injury correlation at the first three months after RT was significantly different from later follow-up periods in terms of steepness and threshold dose as estimated from the LKB model. Dependence of dose response on superior-inferior tumour position was also observed. The time-dependent analytical modelling of RILD might provide better understanding of the long-term behaviour of the disease and could potentially be applied to improve inverse treatment planning optimization.