Correlation of clinical outcome, radiobiological modeling of tumor control, normal tissue complication probability in lung cancer patients treated with SBRT using Monte Carlo calculation algorithm

Purpose/Background

We analyzed the predictive value of non‐x‐ray voxel Monte Carlo (XVMC)‐based modeling of tumor control probability (TCP) and normal tissue complication probability (NTCP) in patients treated with stereotactic body radiotherapy (SBRT) using the XVMC dose calculation algorithm.

Materials/Methods

We conducted an IRB‐approved retrospective analysis in patients with lung tumors treated with XVMC‐based lung SBRT. For TCP, we utilized tumor size‐adjusted biological effective dose (s‐BED) TCP modeling validated in non‐MC dose calculated SBRT to: (1) verify modeling as a function of s‐BED in patients treated with XVMC‐based SBRT; and (2) evaluate the predictive potential of different PTV dosimetric parameters (mean dose, minimum dose, max dose, prescription dose, D95, D98, and D99) for incorporation into the TCP model. Correlation between observed local control and TCPs was assessed by Pearson's correlation coefficient. For NTCP, Lyman NTCP Model was utilized to predict grade 2 pneumonitis and rib fracture.

Results

Eighty‐four patients with 109 lung tumors were treated with XVMC‐based SBRT to total doses of 40 to 60 Gy in 3 to 5 fractions. Median follow‐up was 17 months. The 2‐year local and local‐regional control rates were 91% and and 78%, respectievly. All estimated TCPs correlated significantly with 2‐year actuarial local control rates (P < 0.05). Significant corelations between TCPs and tumor control rate according to PTV dosimetric parameters were observed. D99 parameterization demonstrated the most robust correlation between observed and predicted tumor control. The incidences of grade 2 pneumonitis and rib fracture vs. predicted were 1% vs. 3% and 10% vs. 13%, respectively.

Conclusion

Our TCP results using a XVMC‐based dose calculation algorithm are encouraging and yield validation to previously described TCP models using non‐XVMC dose methods. Furthermore, D99 as potential predictive parameter in the TCP model demonstrated better correlation with clinical outcome.

Prescription Value-Based Automatic Optimization of Importance Factors in Inverse Planning

Objective:

An automatic method for the optimization of importance factors was proposed to improve the efficiency of inverse planning.

Methods:

The automatic method consists of 3 steps: (1) First, the importance factors are automatically and iteratively adjusted based on our proposed penalty strategies. (2) Then, plan evaluation is performed to determine whether the obtained plan is acceptable. (3) If not, a higher penalty is assigned to the unsatisfied objective by multiplying it by a compensation coefficient. The optimization processes are performed alternately until an acceptable plan is obtained or the maximum iteration N_max of step (3) is reached.

Results:

Tested on 2 kinds of clinical cases and compared with manual method, the results showed that the quality of the proposed automatic plan was comparable to, or even better than, the manual plan in terms of the dose–volume histogram and dose distributions.

Conclusions:

The proposed algorithm has potential to significantly improve the efficiency of the existing manual adjustment methods for importance factors and contributes to the development of fully automated planning. Especially, the more the subobjective functions, the more obvious the advantage of our algorithm.

Recent advances in radiation oncology

Radiotherapy (RT) is very much a technology-driven treatment modality in the management of cancer. RT techniques have changed significantly over the past few decades, thanks to improvements in engineering and computing. We aim to highlight the recent developments in radiation oncology, focusing on the technological and biological advances. We will present state-of-the-art treatment techniques, employing photon beams, such as intensity-modulated RT, volumetric-modulated arc therapy, stereotactic body RT and adaptive RT, which make possible a highly tailored dose distribution with maximum normal tissue sparing. We will analyse all the steps involved in the treatment: imaging, delineation of the tumour and organs at risk, treatment planning and finally image-guidance for accurate tumour localisation before and during treatment delivery. Particular attention will be given to the crucial role that imaging plays throughout the entire process. In the case of adaptive RT, the precise identification of target volumes as well as the monitoring of tumour response/modification during the course of treatment is mainly based on multimodality imaging that integrates morphological, functional and metabolic information. Moreover, real-time imaging of the tumour is essential in breathing adaptive techniques to compensate for tumour motion due to respiration. Brief reference will be made to the recent spread of particle beam therapy, in particular to the use of protons, but also to the yet limited experience of using heavy particles such as carbon ions. Finally, we will analyse the latest biological advances in tumour targeting. Indeed, the effectiveness of RT has been improved not only by technological developments but also through the integration of radiobiological knowledge to produce more efficient and personalised treatment strategies.

An integrated strategy of biological and physical constraints in biological optimization for cervical carcinoma

Background

For cervical carcinoma cases, this study aimed to evaluate the quality of intensity-modulated radiation therapy (IMRT) plans optimized by biological constraints. Furthermore, a new integrated strategy in biological planning module was proposed and verified.

Methods

Twenty patients of advanced stage cervical carcinoma were enrolled in this study. For each patient, dose volume optimization (DVO), biological model optimization (BMO) and integrated strategy optimization (ISO) plans were created using same treatment parameters. Different biological models were also used for organ at risk (OAR) in BMO plans, which include the LKB and Poisson models. Next, BMO plans were compared with their corresponding DVO plans, in order to evaluate BMO plan quality. ISO plans were also compared with DVO and BMO plans, in order to verify the performance of the integrated strategy.

Results

BMO plans produced slightly inhomogeneity and less coverage of planning target volume (PTV) (V95=96.79, HI = 0.10: p < 0.01). However, the tumor control probability (TCP) value, both from DVO and BMO plans, were comparable. For the OARs, BMO plans produced lower normal tissue complication probability (NTCP) of rectum (NTCP = 0.11) and bladder (NTCP = 0.14) than in the corresponding DVO plans (NTCP = 0.19 and 0.18 for rectum and bladder; p < 0.01 for rectum and p = 0.03 for bladder). V95, D98, CI and HI values that were produced by ISO plans (V95 = 98.31, D98 = 54.18Gy, CI = 0.76, HI = 0.09) were greatly better than BMO plans (V95 = 96.79, D98 = 53.42Gy, CI = 0.71, HI = 0.10) with significant differences. Furthermore, ISO plans produced lower NTCP values of rectum (NTCP = 0.14) and bladder (NTCP = 0.16) than DVO plans (NTCP = 0.19 and 0.18 for rectum and bladder, respectively) with significant differences.

Conclusions

BMO plans produced lower NTCP values of OARs compared to DVO plans for cervical carcinoma cases, and resulted in slightly less target coverage and homogeneity. The integrated strategy, proposed in this study, could improve the coverage, conformity and homogeneity of PTV greater than the BMO plans, as well as reduce the NTCP values of OARs greater than the DVO plans.

Integral Dose-Based Inverse Optimization May Reduce Side Effects in Radiotherapy of Prostate Carcinoma

PURPOSE

The purpose of this work is to apply a novel inverse optimization approach, based on utilization of quantitative imaging information in the optimization function, to prostate carcinoma.

MATERIALS AND METHODS

This new inverse optimization algorithm relies upon quantitative information derived from computed tomography (CT) imaging studies. The Hounsfield numbers of the CT voxels are converted to physical density, which in turn is used to calculate voxel mass and the corresponding integral dose, by summation over the product of dose and mass in each dose voxel. This integral dose is used for plan optimization through its global minimization. The optimization results are compared to the optimization results derived from most commonly used dose-volume-based inverse optimization, where objective functions are formed as summation over all dose voxels of the squared differences between voxel doses and user specified doses. The data from 25 prostate plans were optimized with dose-volume histogram (DVH) and integral dose (energy) minimization objective functions. The results obtained with the energy- and DVH-based optimization schemes were studied through commonly used dosimetric indices (DIs). Statistical equivalence tests were further performed to establish population-based significance results.

RESULTS

Both DVH- and energy-based plans for each case were normalized so that 95% of the planning target volume receives the prescription dose. The average differences for the rectum and bladder DIs ranged from 1.6 to 25%, where the energy-based quantities were lower. For both femoral heads, the energy-based optimization-derived doses were lower on average by 32%. The statistical tests demonstrated that the significant differences in the tallied dose indices range from 2.7% to more than 50% for rectum, bladder, and femoral heads.

CONCLUSION

For majority of the clinically relevant dosimetric quantities, energy-based inverse optimization performs better than the standard of care DVH-based optimization in prostate carcinoma. The population averaged statistically significant differences range from ~3 to ~50%. Therefore, this newly proposed optimization approach, incorporating explicitly quantitative imaging information in the inverse optimization function, holds potential for further reduction of complication rates in prostate cancer.

Modeling Radiotherapy Induced Normal Tissue Complications: An Overview beyond Phenomenological Models

An overview of radiotherapy (RT) induced normal tissue complication probability (NTCP) models is presented. NTCP models based on empirical and mechanistic approaches that describe a specific radiation induced late effect proposed over time for conventional RT are reviewed with particular emphasis on their basic assumptions and related mathematical translation and their weak and strong points.

Multivariable normal tissue complication probability model-based treatment plan optimization for grade 2-4 dysphagia and tube feeding dependence in head and neck radiotherapy

BACKGROUND AND PURPOSE

Radiotherapy of the head and neck is challenged by the relatively large number of organs-at-risk close to the tumor. Biologically-oriented objective functions (OF) could optimally distribute the dose among the organs-at-risk. We aimed to explore OFs based on multivariable normal tissue complication probability (NTCP) models for grade 2-4 dysphagia (DYS) and tube feeding dependence (TFD).

MATERIALS AND METHODS

One hundred head and neck cancer patients were studied. Additional to the clinical plan, two more plans (an OF_DYS and OF_TFD-plan) were optimized per patient. The NTCP models included up to four dose-volume parameters and other non-dosimetric factors. A fully automatic plan optimization framework was used to optimize the OF_NTCP-based plans.

RESULTS

All OF_NTCP-based plans were reviewed and classified as clinically acceptable. On average, the Δdose and ΔNTCP were small comparing the OF_DYS-plan, OF_TFD-plan, and clinical plan. For 5% of patients NTCP_TFD reduced >5% using OF_TFD-based planning compared to the OF_DYS-plans.

CONCLUSIONS

Plan optimization using NTCP_DYS- and NTCP_TFD-based objective functions resulted in clinically acceptable plans. For patients with considerable risk factors of TFD, the OF_TFD steered the optimizer to dose distributions which directly led to slightly lower predicted NTCP_TFD values as compared to the other studied plans.

Radiobiological impact of dose calculation algorithms on biologically optimized IMRT lung stereotactic body radiation therapy plans

Background

The aim of this study is to evaluate the radiobiological impact of Acuros XB (AXB) vs. Anisotropic Analytic Algorithm (AAA) dose calculation algorithms in combined dose-volume and biological optimized IMRT plans of SBRT treatments for non-small-cell lung cancer (NSCLC) patients.

Methods

Twenty eight patients with NSCLC previously treated SBRT were re-planned using Varian Eclipse (V11) with combined dose-volume and biological optimization IMRT sliding window technique. The total dose prescribed to the PTV was 60 Gy with 12 Gy per fraction. The plans were initially optimized using AAA algorithm, and then were recomputed using AXB using the same MUs and MLC files to compare with the dose distribution of the original plans and assess the radiobiological as well as dosimetric impact of the two different dose algorithms. The Poisson Linear-Quadatric (PLQ) and Lyman-Kutcher-Burman (LKB) models were used for estimating the tumor control probability (TCP) and normal tissue complication probability (NTCP), respectively. The influence of the model parameter uncertainties on the TCP differences and the NTCP differences between AAA and AXB plans were studied by applying different sets of published model parameters. Patients were grouped into peripheral and centrally-located tumors to evaluate the impact of tumor location.

Results

PTV dose was lower in the re-calculated AXB plans, as compared to AAA plans. The median differences of PTV(D_95%) were 1.7 Gy (range: 0.3, 6.5 Gy) and 1.0 Gy (range: 0.6, 4.4 Gy) for peripheral tumors and centrally-located tumors, respectively. The median differences of PTV(mean) were 0.4 Gy (range: 0.0, 1.9 Gy) and 0.9 Gy (range: 0.0, 4.3 Gy) for peripheral tumors and centrally-located tumors, respectively. TCP was also found lower in AXB-recalculated plans compared with the AAA plans. The median (range) of the TCP differences for 30 month local control were 1.6 % (0.3 %, 5.8 %) for peripheral tumors and 1.3 % (0.5 %, 3.4 %) for centrally located tumors. The lower TCP is associated with the lower PTV coverage in AXB-recalculated plans. No obvious trend was observed between the calculation-resulted TCP differences and tumor size or location. AAA and AXB yield very similar NTCP on lung pneumonitis according to the LKB model estimation in the present study.

Conclusion

AAA apparently overestimates the PTV dose; the magnitude of resulting difference in calculated TCP was up to 5.8 % in our study. AAA and AXB yield very similar NTCP on lung pneumonitis based on the LKB model parameter sets we used in the present study.

Mathematical Formulation of DMH-Based Inverse Optimization

Purpose: To introduce the concept of dose–mass-based inverse optimization for radiotherapy applications.

Materials and Methods: Mathematical derivation of the dose–mass-based formalism is presented. This mathematical representation is compared to the most commonly used dose–volume-based formulation used in inverse optimization. A simple example on digitally created phantom is presented. The phantom consists of three regions: a target surrounded by high- and low-density regions. The target is irradiated with two beams through those regions and inverse optimization with dose–volume and dose–mass-based objective functions is performed. The basic properties of the two optimization types are demonstrated on the phantom.

Results: It is demonstrated that dose–volume optimization is a special case of dose–mass optimization. In a homogenous media, dose–mass optimization turns into dose–volume optimization. The dose calculations performed on the digital phantom show that in this very simple case dose–mass optimization tends to penalize more the dose delivery through the high-density region and therefore it results in delivering more dose through the low-density region.

Conclusion: It was demonstrated that dose–mass-based optimization is mathematically more general than dose–volume-based optimization. In the case of constant density media, dose–mass optimization transforms into dose–volume optimization.

The use of biologically related model (Eclipse) for the intensity-modulated radiation therapy planning of nasopharyngeal carcinomas

Purpose

Intensity-modulated radiation therapy (IMRT) is the most common treatment technique for nasopharyngeal carcinoma (NPC). Physical quantities such as dose/dose-volume parameters are used conventionally for IMRT optimization. The use of biological related models has been proposed and can be a new trend. This work was to assess the performance of the biologically based IMRT optimization model installed in a popular commercial treatment planning system (Eclipse) as compared to its dose/dose volume optimization model when employed in the clinical environment for NPC cases.

Methods

Ten patients of early stage NPC and ten of advanced stage NPC were selected for this study. IMRT plans optimized using biological related approach (BBTP) were compared to their corresponding plans optimized using the dose/dose volume based approach (DVTP). Plan evaluation was performed using both biological indices and physical dose indices such as tumor control probability (TCP), normal tissue complication probability (NTCP), target coverage, conformity, dose homogeneity and doses to organs at risk. The comparison results of the more complex advanced stage cases were reported separately from those of the simpler early stage cases.

Results

The target coverage and conformity were comparable between the two approaches, with BBTP plans producing more hot spots. For the primary targets, BBTP plans produced comparable TCP for the early stage cases and higher TCP for the advanced stage cases. BBTP plans reduced the volume of parotid glands receiving doses of above 40 Gy compared to DVTP plans. The NTCP of parotid glands produced by BBTP were 8.0±5.8 and 7.9±8.7 for early and advanced stage cases, respectively, while those of DVTP were 21.3±8.3 and 24.4±12.8, respectively. There were no significant differences in the NTCP values between the two approaches for the serial organs.

Conclusions

Our results showed that the BBTP approach could be a potential alternative approach to the DVTP approach for NPC.

Mathematical formulation of energy minimization - based inverse optimization

Purpose: To introduce the concept of energy minimization-based inverse optimization for external beam radiotherapy.

Materials and Methods: Mathematical formulation of energy minimization-based inverse optimization is presented. This mathematical representation is compared to the most commonly used dose–volume based formulation used in inverse optimization. A simple example on digitally created phantom is demonstrated. The phantom consists of three sections: a target surrounded by high and low density regions. The target is irradiated with two beams passing through those regions. Inverse optimization with dose–volume and energy minimization-based objective functions is performed. The dosimetric properties of the two optimization results are evaluated.

Results: Dose–volume histograms for all the volumes of interest used for dose optimization are compared. Energy-based optimization results in higher maximum dose to the volumes that are used as dose-limiting structures. However, the average and the integral doses delivered for the volumes outside of the target are larger with dose–volume optimization.

Conclusion: Mathematical formulation of energy minimization-based inverse optimization is derived. The optimization applied on the digital phantom shows that energy minimization-based approach tends to deliver somewhat higher maximum doses compared to standard of care, realized with dose–volume based optimization. At the same time, however, the energy minimization-based optimization reduces much more significantly the average and the integral doses.

The use and QA of biologically related models for treatment planning: short report of the TG-166 of the therapy physics committee of the AAPM

Treatment planning tools that use biologically related models for plan optimization and/or evaluation are being introduced for clinical use. A variety of dose-response models and quantities along with a series of organ-specific model parameters are included in these tools. However, due to various limitations, such as the limitations of models and available model parameters, the incomplete understanding of dose responses, and the inadequate clinical data, the use of biologically based treatment planning system (BBTPS) represents a paradigm shift and can be potentially dangerous. There will be a steep learning curve for most planners. The purpose of this task group is to address some of these relevant issues before the use of BBTPS becomes widely spread. In this report, the authors (1) discuss strategies, limitations, conditions, and cautions for using biologically based models and parameters in clinical treatment planning; (2) demonstrate the practical use of the three most commonly used commercially available BBTPS and potential dosimetric differences between biologically model based and dose-volume based treatment plan optimization and evaluation; (3) identify the desirable features and future directions in developing BBTPS; and (4) provide general guidelines and methodology for the acceptance testing, commissioning, and routine quality assurance (QA) of BBTPS.

Quantitative metrics for assessing plan quality

Despite many studies over the last 3 decades that have attempted to explicitly quantify the decision-making process for radiotherapy treatment plan evaluation, judgments of an individual plan's degree of quality are still largely subjective and can show inter- and intra-practitioner variability even if the clinical treatment goals are the same. Several factors conspire to confound the full quantification of treatment plan quality, including uncertainties in dose response of cancerous and normal tissue, the rapid pace of new technology adoption, and the human component of treatment planning. However, new developments in clinical informatics and automation are lowering the bar for developing and implementing quantitative metrics into the treatment planning process. This review discusses general strategies for using quantitative metrics in the treatment planning process and presents a case study in intensity-modulated radiation therapy planning whereby control was established on a variable system via such techniques.

Biological optimization in volumetric modulated arc radiotherapy for prostate carcinoma

PURPOSE

To investigate the potential benefits achievable with biological optimization for modulated volumetric arc (VMAT) treatments of prostate carcinoma.

METHODS AND MATERIALS

Fifteen prostate patient plans were studied retrospectively. For each case, planning target volume, rectum, and bladder were considered. Three optimization schemes were used: dose-volume histogram (DVH) based, generalized equivalent uniform dose (gEUD) based, and mixed DVH/gEUD based. For each scheme, a single or dual 6-MV, 356° VMAT arc was used. The plans were optimized with Pinnacle(3) (v. 9.0 beta) treatment planning system. For each patient, the optimized dose distributions were normalized to deliver the same prescription dose. The quality of the plans was evaluated by dose indices (DIs) and gEUDs for rectum and bladder. The tallied DIs were D(1%), D(15%), D(25%), and D(40%), and the tallied gEUDs were for a values of 1 and 6. Statistical tests were used to quantify the magnitude and the significance of the observed differences. Monitor units and treatment times for each optimization scheme were also assessed.

RESULTS

All optimization schemes generated clinically acceptable plans. The statistical tests indicated that biological optimization yielded increased organs-at-risk sparing, ranging from ~1% to more than ~27% depending on the tallied DI, gEUD, and anatomical structure. The increased sparing was at the expense of longer treatment times and increased number of monitor units.

CONCLUSIONS

Biological optimization can significantly increase the organs-at-risk sparing in VMAT optimization for prostate carcinoma. In some particular cases, however, the DVH-based optimization resulted in superior treatment plans.

The ESTRO Breur Lecture 2009. From population to voxel-based radiotherapy: exploiting intra-tumour and intra-organ heterogeneity for advanced treatment of non-small cell lung cancer

Evidence is accumulating that radiotherapy of non-small cell lung cancer patients can be optimized by escalating the tumour dose until the normal tissue tolerances are met. To further improve the therapeutic ratio between tumour control probability and the risk of normal tissue complications, we firstly need to exploit inter patient variation. This variation arises, e.g. from differences in tumour shape and size, lung function and genetic factors. Secondly improvement is achieved by taking into account intra-tumour and intra-organ heterogeneity derived from molecular and functional imaging. Additional radiation dose must be delivered to those parts of the tumour that need it the most, e.g. because of increased radio-resistance or reduced therapeutic drug uptake, and away from regions inside the lung that are most prone to complication. As the delivery of these treatments plans is very sensitive for geometrical uncertainties, probabilistic treatment planning is needed to generate robust treatment plans. The administration of these complicated dose distributions requires a quality assurance procedure that can evaluate the treatment delivery and, if necessary, adapt the treatment plan during radiotherapy.