Radiotherapy Treatment Plan Evaluation Research Articles

Exploration of overlap volumes for radiotherapy plan evaluation with the aim of healthy tissue sparing

Purpose:Development of a novel interactive visualization approach for the exploration of radiotherapy treatment plans with a focus on overlap volumes with the aim of healthy tissue sparing. Methods:We propose a visualization approach to include overlap volumes in the radiotherapy treatment plan evaluation process. Quantitative properties can be interactively explored to identify critical regions and used to steer the visualization for a detailed inspection of candidates. We evaluated our approach with a user study covering the individual visualizations and their interactions regarding helpfulness, comprehensibility, intuitiveness, decision-making and speed. Results:A user study with three domain experts was conducted using our software and evaluating five data sets each representing a different type of cancer and location by performing a set of tasks and filling out a questionnaire. The results show that the visualizations and interactions help to identify and evaluate overlap volumes according to their physical and dose properties. Furthermore, the task of finding dose hot spots can also benefit from our approach. Conclusions:The results indicate the potential to enhance the current treatment plan evaluation process in terms of healthy tissue sparing.

Multiscale spatial relationship-based model for predicting bladder wall dose in pelvic radiotherapy.

This research aimed to develop a prediction model to assess bladder wall dosimetry during radiotherapy for patients with pelvic tumors, thereby facilitating the refinement and evaluation of radiotherapy treatment plans to mitigate bladder toxicity. Radiotherapy treatment plans of 49 rectal cancer patients and 45 gynecologic cancer patients were collected, and multiple linear regression analyses were used to generate prediction models for bladder wall dose parameters ( , ). These models were based on the multiscale spatial relationship between the planning target volume (PTV) and the bladder or bladder wall. The proportion of bladder or bladder wall volume overlapped by the different distance expansions of the PTV was used as an indicator of the multiscale spatial relationship. The accuracy of these models was verified in a cohort of 12 new patients, with further refinement of radiotherapy treatment plans using the predicted values as optimization parameters. Model accuracy was assessed using root mean square error (RMSE) and mean percentage error (MPE). Models derived from individual disease data outperformed those derived from combined datasets. Predicted bladder wall dose parameters were accurate, with the majority of initial calculated values for new patients falling within the 95% confidence interval of the model predictions. There was a robust correlation between the predicted and actual dose metrics, with a correlation coefficient of 0.943. Using the predicted values to optimize treatment plans significantly reduced bladder wall dose (p 0.001), with bladder wall and decreasing by 2.27±0.80Gy (5.8%±1.8%) and 2.96±2.05 cm3 (7.9%±5.4%), respectively. The formulated prediction model provides a valuable tool for predicting and minimizing bladder wall dose and for optimizing and evaluating radiotherapy treatment plans for pelvic tumor patients. This approach holds promise for reducing bladder toxicity and potentially improving patient outcomes.

Determining the optimum tumor control probability model in radiotherapy of glioblastoma multiforme using magnetic resonance imaging data pre- and post- radiation therapy

Background:Glioblastoma multiforme (GBM) is the most common and malignant brain tumor. The current standard of care is surgery followed by radiation therapy (RT). Radiotherapy treatment plan evaluation relies on radiobiological models for accurate estimation of tumor control probability (TCP). This study aimed to assess the impact of obtained magnetic resonance imaging (MRI) data before and 12 weeks after RT to achieve the optimum TCP model to improve dose prescriptions in radiation therapy of GBM.Materials and Methods::In this quasi-experimental study, MR images and its relevant data from 30 patients consisting of 9 females and 21 males (mean age of 46.3 ± 15.8 years) diagnosed with GBM, whose referred for radiotherapy were selected. The data of age, gender, tumor size, volume, and signal intensity using analysis of MRI data pre- and postradiotherapy were used for calculating TCP. TCP was calculated from three common radiobiological models including Poisson, linear quadratic, and equivalent uniform dose. The impact of some radiobiological parameters on final TCP in all patients planned with three-dimensional conformal radiation therapy was obtained.Results:A statistically significant difference was found among TCP in Poisson model compared to the other two models (P < 0.001). Changes in tumor volume and size after treatment were statistically significant (P < 0.05). Different combinations of radiobiological parameters (α/β and SF2 in all models) observed were meaningful (P < 0.05).Conclusion:The results showed that among TCP radiobiological models, the optimum is the Poisson. The results also identified the importance of TCP radiobiological models in order to improve radiotherapy dose prescriptions.

73 An Innovative Learning Tool for Radiotherapy Treatment Plan Evaluation: Implementation and Evaluation

73 An Innovative Learning Tool for Radiotherapy Treatment Plan Evaluation: Implementation and Evaluation

An Innovative Learning Tool for Radiotherapy Treatment Plan Evaluation: Implementation and Evaluation

An Innovative Learning Tool for Radiotherapy Treatment Plan Evaluation: Implementation and Evaluation

Dose-mass inverse optimization for minimally moving thoracic lesions

In the past decade, several different radiotherapy treatment plan evaluation and optimization schemes have been proposed as viable approaches, aiming for dose escalation or an increase of healthy tissue sparing. In particular, it has been argued that dose-mass plan evaluation and treatment plan optimization might be viable alternatives to the standard of care, which is realized through dose-volume evaluation and optimization. The purpose of this investigation is to apply dose-mass optimization to a cohort of lung cancer patients and compare the achievable healthy tissue sparing to that one achievable through dose-volume optimization.Fourteen non-small cell lung cancer (NSCLC) patient plans were studied retrospectively. The range of tumor motion was less than 0.5 cm and motion management in the treatment planning process was not considered. For each case, dose-volume (DV)-based and dose-mass (DM)-based optimization was performed. Nine-field step-and-shoot IMRT was used, with all of the optimization parameters kept the same between DV and DM optimizations. Commonly used dosimetric indices (DIs) such as dose to 1% the spinal cord volume, dose to 50% of the esophageal volume, and doses to 20 and 30% of healthy lung volumes were used for cross-comparison. Similarly, mass-based indices (MIs), such as doses to 20 and 30% of healthy lung masses, 1% of spinal cord mass, and 33% of heart mass, were also tallied. Statistical equivalence tests were performed to quantify the findings for the entire patient cohort.Both DV and DM plans for each case were normalized such that 95% of the planning target volume received the prescribed dose. DM optimization resulted in more organs at risk (OAR) sparing than DV optimization. The average sparing of cord, heart, and esophagus was 23, 4, and 6%, respectively. For the majority of the DIs, DM optimization resulted in lower lung doses. On average, the doses to 20 and 30% of healthy lung were lower by approximately 3 and 4%, whereas lung volumes receiving 2000 and 3000 cGy were lower by 3 and 2%, respectively. The behavior of MIs was very similar. The statistical analyses of the results again indicated better healthy anatomical structure sparing with DM optimization.The presented findings indicate that dose-mass–based optimization results in statistically significant OAR sparing as compared to dose-volume–based optimization for NSCLC. However, the sparing is case-dependent and it is not observed for all tallied dosimetric endpoints.

SU‐E‐T‐823: Implication of the Spatial Resolution of the Conventional Dose‐Volume Histogram Analysis in the Radiation Therapy Treatments

Purpose: The detection of the local‐spots of interest is important in evaluation of radiotherapy treatment plans. The conventional dose‐volume histogram (DVH) analysis loses the spatial information of the dose‐distribution such as “hot” and “cold” spots in the evaluation process. However, the cDVH can be resolved into the components of spatial‐DVHs (sDVHs; x‐,y‐ and zDVHs respectively). The objective of this work was to assess the accuracy of IMRT plans using a noble approach of sDVH analysis in the open‐source software system, Histogram Analysis in Radiation Therapy, HART (J Appl Clin Med Phys, Vol.11 (1), p. 3013, 2010). Method and Materials:The contours of the regions‐of‐interest along z‐direction were reformatted to generate contours along x‐, and y‐ planes respectively, using RTOG format of the IMRT plans of 10 head and neck cancer patients (N=10). The dose computed along z‐planes of the CT slices were used to compute dose‐volume matrices along x‐,y‐ and z‐planes respectively, using the user‐friendly HART program. The sDVH data‐points were validated with the cumulative‐DVH (cDVH) statistics of the corresponding organs. The sDVH statistics were utilized to assess the dose‐distribution in organs‐at‐risk such as salivary glands and larynx. Results: HART extracted more than 7000 sDVH statistics in 15–20 minutes for 20–30 organs using the dual‐core processor of 3GB RAM. The hot‐spots estimated from the sDVH analyses, were consistent with the cDVH analyses at higher resolution (1 mm). The low‐density hot‐spots (&lt; 5% per slice) were symmetrically polarized in 3D‐space in 57 ±12% and 93 ±2% of the slices (N=10) of parotid glands and larynx respectively, however high‐density hot‐spots were detected in 90 ±8% of the slices (N=10) of the submandibular glands. Conclusions: The sDVH analysis is an accurate and efficient approach for the in‐depth analysis of the radiotherapy plans and the radio‐biological outcomes of the treatments, using the HART program.

“SABER”: A new software tool for radiotherapy treatment plan evaluation

Both spatial and biological information are necessary in order to perform true optimization of a treatment plan and for predicting clinical outcome. The goal of this work is to develop an enhanced treatment plan evaluation tool which incorporates biological parameters and retains spatial dose information. A software system is developed which provides biological plan evaluation with a novel combination of features. It incorporates hyper-radiosensitivity using the induced-repair model and applies the new concept of dose convolution filter (DCF) to simulate dose wash-out effects due to cell migration, bystander effect, and/or tissue motion during treatment. Further, the concept of spatial DVH (sDVH) is introduced to evaluate and potentially optimize the spatial dose distribution in the target volume. Finally, generalized equivalent uniform dose is derived from both the physical dose distribution (gEUD) and the distribution of equivalent dose in 2 Gy fractions (gEUD2) and the software provides three separate models for calculation of tumor control probability (TCP), normal tissue complication probability (NTCP), and probability of uncomplicated tumor control (P+). TCP, NTCP, and P+ are provided as a function of prescribed dose and multivariable TCP, NTCP, and P+ plots are provided to illustrate the dependence on individual parameters used to calculate these quantities. Ten plans from two clinical treatment sites are selected to test the three calculation models provided by this software. By retaining both spatial and biological information about the dose distribution, the software is able to distinguish features of radiotherapy treatment plans not discernible using commercial systems. Plans that have similar DVHs may have different spatial and biological characteristics and the application of novel tools such as sDVH and DCF within the software may substantially change the apparent plan quality or predicted plan metrics such as TCP and NTCP. For the cases examined, both the calculation method and the application of DCF can change the ranking order of competing plans. The voxel-by-voxel TCP model makes it feasible to incorporate spatial variations of clonogen densities (n), radiosensitivities (SF2), and fractionation sensitivities (alpha/beta) as those data become available. The new software incorporates both spatial and biological information into the treatment planning process. The application of multiple methods for the incorporation of biological and spatial information has demonstrated that the order of application of biological models can change the order of plan ranking. Thus, the results of plan evaluation and optimization are dependent not only on the models used but also on the order in which they are applied. This software can help the planner choose more biologically optimal treatment plans and potentially predict treatment outcome more accurately.

SU-GG-T-580: Parameterization of Dose Volume Histograms (DVHs) of Planning Target Volume (PTV) Among Patients Undergoing Radiation Therapy for Prostate Cancer

Purpose: To propose and validate a method to parameterize Dose Volume Histograms (DVHs) of the Planning Target Volume (PTV) for prostate cancer patients undergoing radiation therapy. Methods and Materials: There are generally two different formats of DVH: differential DVH (dDVH) and cumulative DVH (cDVH). Although cDVHs are more commonly used for radiotherapy treatment plan evaluation, dDVHs were used in our study to test the parameterization, as mathematically, the deferential format preserves more details than the cumulative/integral format. Prostate adenocarcinoma patients previously irradiated at our institution were selected for the study. For these patients, the total dose was 7400 cGy prescribed to cover 95%–98% of the PTV. The treatments were planned using a 7–9 field IMRT technique. It was found that the PTV dDVHs could be characterized by a normal function. Fifteen patients were selected and their PTV DVHs were fitted with a normal function. Parameters regularly used for the treatment plan evaluations, e.g. D95, mean dose and generalized mean dose (GMD), from the original DVHs were compared with those from fitted DVHs. Results: The fittings of the rectal DVHs yielded high goodness of fit with adjusted R-square greater than 0.97. The mode of the peak was 7575.4 ± 57.1 cGy. The standard deviation was 43.3 ± 10.5 cGy. For D95, mean dose and GMD, the relative differences between original and fitted DVHs were within 3%. Conclusions: DVHs could be represented by several parameters. The PTV DVH of patients undergoing irradiation to the prostate gland could successfully be parameterized using a normal function.

SU‐GG‐T‐351: Using Dose Mass Histograms (DMH) for the Evaluation of Head and Neck IMRT Plans Calculated by Monte Carlo

Purpose: Dose volume histograms (DVH) represent an important tool for the clinical evaluation of radiotherapy treatment plans. For head and neck regions, however, the presence of air cavities makes the DVH tool less adequate for Monte Carlo calculated IMRT plans. The air cavities may introduce high dose uncertainties in both air cavities and surrounding tissues (interface effects). Since the dose to air is clinically irrelevant DVH becomes less clinically representative in the presence of large air cavities, and hence is no longer a good parameter for plan evaluation. In this work we assess the limitations of DVH for head and neck plan evaluation and investigate the dose mass histogram (DMH) as an alternative to overcome those limitations. Method and Materials: Seven IMRT head and neck cases were included in this study. The Monte Carlo simulation geometry (2mm voxels) was built from patient CT data. Patient dose calculations were performed using the EGS4 based MCSIM code and photon source models for 6 and 10MV beams. Isodose distributions and DVH (including air) were generated for these plans. A new feature was created in order to calculate DMH for both the target (PTV) and critical structures. In addition, DVH excluding air were also generated. Results: Our results for the 7 patients show significant differences (up to 10%) between the DVH calculated including and excluding air for the PTV and critical structures. DMH, on the other hand, eliminates the effect of large Monte Carlo statistical uncertainties in air cavities and is a better parameter than DVH for evaluating head and neck IMRT treatment plans. Conclusion: DMH is a better parameter than DVH for evaluating treatment plans calculated by Monte Carlo simulations that may have large statistical uncertainties in low‐density regions such as air cavities and lung tissues.

EUD-based radiotherapy treatment plan evaluation: incorporating physical and Monte Carlo statistical dose uncertainties

The purpose of this work is to quantify the impact of dose uncertainty on radiobiologically based treatment plan evaluation. Dose uncertainties are divided into two categories: physical and statistical. Physical dose uncertainty is associated with the systematic and/or random errors incurred during treatment planning and/or delivery. The dose uncertainty associated with Monte Carlo calculated dose distributions is deemed statistical and noted as artificial with respect to the actual delivered dose. We will refer to all dose uncertainties that arise from either calculation or delivery as stochastic. Both physical and statistical dose uncertainties are considered at the intra- and inter-voxel levels. To account for voxel dose uncertainty, we calculate the mean survival fraction (SF) for the random dose deposition. Mathematically, the expression for the mean survival fraction is identical to that used by Niemierko (1997 Med. Phys. 24 103–10) in defining equivalent uniform dose (EUD). To distinguish between spatial and probabilistic dose variations, we define equivalent stochastic dose (ESD) as a voxel dose that gives the mean expected survival fraction for the randomly deposited dose. For a probability density function f(D), that represents the probabilistic voxel dose, SF(ESD) can be calculated by convolving SF(D) with f(D). In the case where the probability density function follows a Gaussian distribution, an analytic expression is derived for SF(ESD). The derived expression is verified using the Monte Carlo method and ESD values calculated with varied radiosensitivities for cases of 60 and 70 Gy at 2 Gy per fraction. The analytic expression is also extended to account for a multi-voxel dose distribution that incorporates a spatial dose heterogeneity. The results show that survival fraction increases with an increased dose uncertainty. This reduction depends on radiobiological parameters attributed to tissue and tumour. For tissue, ESD drops to 55% of the mean physical dose when the dose has a 10% intra- and inter-voxel dose uncertainty and inhomogeneity.

A TCP‐NTCP estimation module using DVHs and known radiobiological models and parameter sets

Radiotherapy treatment plan evaluation relies on an implicit estimation of the tumor control probability (TCP) and normal tissue complication probability (NTCP) arising from a given dose distribution. A potential application of radiobiological modeling to radiotherapy is the ranking of treatment plans via a more explicit determination of TCP and NTCP values. Although the limited predictive capabilities of current radiobiological models prevent their use as a primary evaluative tool, radiobiological modeling predictions may still be a valuable complement to clinical experience. A convenient computational module has been developed for estimating the TCP and the NTCP arising from a dose distribution calculated by a treatment planning system, and characterized by differential (frequency) dose‐volume histograms (DDVHs). The radiobiological models included in the module are sigmoidal dose response and Critical Volume NTCP models, a Poisson TCP model, and a TCP model incorporating radiobiological parameters describing linear‐quadratic cell kill and repopulation. A number of sets of parameter values for the different models have been gathered in databases. The estimated parameters characterize the radiation response of several different normal tissues and tumor types. The system also allows input and storage of parameters by the user, which is particularly useful because of the rapidly increasing number of parameter estimates available in the literature. Potential applications of the system include the following: comparing radiobiological predictions of outcome for different treatment plans or types of treatment; comparing the number of observed outcomes for a cohort of patient DVHs to the predicted number of outcomes based on different models/parameter sets; and testing of the sensitivity of model predictions to uncertainties in the parameter values. The module thus helps to amalgamate and make more accessible current radiobiological modeling knowledge, and may serve as a useful aid in the prospective and retrospective analysis of radiotherapy treatment plans.PACS number: 87.53.Tf

Bioplan: software for the biological evaluation of radiotherapy treatment plans

Distributions of absorbed dose do not provide information on the biological response of tissues (either tumor or organs at risk [OAR]) to irradiation. BIOPLAN (BIOlogical evaluation of PLANs) has been conceived and developed as a PC-based user-friendly software that allows the user to evaluate a treatment plan from the (more objective) point of view of the biological response of the irradiated tissues, and at the same time, provides flexibility in the use of models and parameters. It requires information on dose-volume histograms (DVHs) and can accept a number of different formats (including DVH files from commercial treatment planning systems). BIOPLAN provides a variety of tools, such as tumor control probability (TCP) calculations (using the Poisson model), normal tissue complication probability (NTCP) calculations (using either the Lyman-Kutcher-Burman or the relative seriality models), the ΔTCP method, DVH subtraction, plots of NTCP/TCP as a function of prescription dose, tumor and OAR dose statistics, equivalent uniform dose (EUD), individualized dose prescription, and parametric sensitivity analysis of the TCP/NTCP models employed.

Evaluation and scoring of radiotherapy treatment plans using an artificial neural network

The objective of this work was to demonstrate the feasibility of using an artificial neural network to predict the clinical evaluation of radiotherapy treatment plans. Approximately 150 treatment plans were developed for 16 patients who received external-beam radiotherapy for soft-tissue sarcomas of the lower extremity. Plans were assigned a figure of merit by a radiation oncologist using a five-point rating scale. Plan scoring was performed by a single physician to ensure consistency in rating. Dose-volume information extracted from a training set of 511 treatment plans on 14 patients was correlated to the physician-generated figure of merit using an artificial neural network. The neural network was tested with a test set of 19 treatment plans on two patients whose plans were not used in the training of the neural net. Physician scoring of treatment plans was consistent to within one point on the rating scale 88% of the time. The neural net reproduced the physician scores in the training set to within one point approximately 90% of the time. It reproduced the physician scores in the test set to within one point approximately 83% of the time. An artificial neural network can be trained to generate a score for a treatment plan that can be correlated to a clinically-based figure of merit. The accuracy of the neural net in scoring plans compares well with the reproducibility of the clinical scoring. The system of radiotherapy treatment plan evaluation using an artificial neural network demonstrates promise as a method for generating a clinically relevant figure of merit.

Integrated software tools for the evaluation of radiotherapy treatment plans

Purpose : This article announces the availability of a convenient and useful software environment for the evaluation of three-dimensional (3D) radiotherapy treatment plans. Materials and Methods : Using standards such as American National Standards for Information Systems C and the X Window System allowed us to bring the computation and display of dose-volume histograms, dose statistics, tumor control probabilities, normal tissue complication probabilities, and a figure of merit together under one user interface. These plan evaluation tools are not stand alone, but must interact with a 3D radiation therapy planning system to obtain the required dose matrices and patient anatomical contours. Installation of the software involves a programmer who writes a software bridge between the radiation therapy planning system and the tools, thereby providing access to local data files. This design strategy confines portability issues to one area of the software. Results : Access to the other tools is through the Graphical Plan Evaluation Tool (GPET). GPET coordinates the use of each of the tools and provides graphical facilities for display of their results. Importantly, GPET assures that the displayed results of each tool have been computed with the same input specifications for all treatment plans being compared. For added convenience, the user can rearrange the resultant data to be reviewed in various ways on the video screen. The software design also allows incorporation of customized algorithms and input data for computing tumor control probability and normal tissue complication probabilities, since those currently available are controversial. Conclusion : The Graphical Plan Evaluation Tool unifies the simultaneous computation for several analytical tools and graphical display of their results. Within the constraints of the X Window System environment, this assemblage of software tools provides a portable, flexible, and convenient method for the quantitative evaluation of several radiotherapy treatment plans.

Integrated software tools for the evaluation of radiotherapy treatment plans

Integrated software tools for the evaluation of radiotherapy treatment plans