A dose-volume histogram based decision-support system for dosimetric comparison of radiotherapy treatment plans

Multicenter Multivendor Evaluation of Dose Volume Histogram Creation Consistencies for 8 Commercial Radiation Therapy Dosimetric Systems

PURPOSE

To evaluate dose volume histogram (DVH) construction differences across 8 major commercial treatment planning systems (TPS) and dose reporting systems for clinically treated plans of various anatomic sites and target sizes.

METHODS AND MATERIALS

Dose files from 10 selected clinically treated plans with a hypofractionation, stereotactic radiation therapy prescription or sharp dose gradients such as head and neck plans ranging from prescription doses of 18 Gy in 1 fraction to 70 Gy in 35 fractions, each calculated at 0.25 and 0.125 cm grid size, were created and anonymized in Eclipse TPS, and exported to 7 other major TPS (Pinnacle, RayStation, and Elements) and dose reporting systems (MIM, Mobius, ProKnow, and Velocity) systems for comparison. Dose-volume constraint points of clinical importance for each plan were collected from each evaluated system (D0.03 cc [Gy], volume, and the mean dose were used for structures without specified constraints). Each reported constraint type and structure volume was normalized to the value from Eclipse for a pairwise comparison. A Wilcoxon rank-sum test was used for statistical significance and a multivariable regression model was evaluated adjusting for plan, grid size, and distance to target center.

RESULTS

For all DVH points relative to Eclipse, all systems reported median values within 1.0% difference of each other; however, they were all different from Eclipse. Considering mean values, Pinnacle, RayStation, and Elements averaged at 1.038, 1.046, and 1.024, respectively, while MIM, Mobius, ProKnow, and Velocity reported 1.026, 1.050, 1.033, and 1.022, respectively relative to Eclipse. Smaller dose grid size improved agreement between the systems marginally without statistical significance. For structure volumes relative to Eclipse, larger differences are seen across all systems with a range in median values up to 3.0% difference and mean up to 10.1% difference.

CONCLUSIONS

Large variations were observed between all systems. Eclipse generally reported, at statistically significant levels, lower values than all other evaluated systems. The nonsignificant change resulting from lowering the dose grid resolution indicates that this resolution may be less important than other aspects of calculating DVH curves, such as the 3-dimensional modeling of the structure.

A Monte Carlo simulation-based decision support system for radiation oncologists in the treatment of glioblastoma multiforme

In the present research, we have developed a model-based crisp logic function statistical classifier decision support system supplemented with treatment planning systems for radiation oncologists in the treatment of glioblastoma multiforme (GBM). This system is based on Monte Carlo radiation transport simulation and it recreates visualization of treatment environments on mathematical anthropomorphic brain (MAB) phantoms. Energy deposition within tumour tissue and normal tissues are graded by quality audit factors which ensure planned dose delivery to tumour site thereby minimising damages to healthy tissues. The proposed novel methodology predicts tumour growth response to radiation therapy from a patient-specific medicine quality audit perspective. Validation of the study was achieved by recreating thirty-eight patient-specific mathematical anthropomorphic brain phantoms of treatment environments by taking into consideration density variation and composition of brain tissues. Dose computations accomplished through water phantom, tissue-equivalent head phantoms are neither cost-effective, nor patient-specific customized and is often less accurate. The above-highlighted drawbacks can be overcome by using open-source Electron Gamma Shower (EGSnrc) software and clinical case reports for MAB phantom synthesis which would result in accurate dosimetry with due consideration to the time factors. Considerable dose deviations occur at the tumour site for environments with intraventricular glioblastoma, haematoma, abscess, trapped air and cranial flaps leading to quality factors with a lower logic value of 0. Logic value of 1 depicts higher dose deposition within healthy tissues and also leptomeninges for majority of the environments which results in radiation-induced laceration.

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.

A generalized fit index for evaluating treatment plans of multiple target volumes with different prescribed dose: Generalized dose distribution fit index

BACKGROUND AND PURPOSE

The differential fit index (dFI) and cumulative fit index (cFI) were defined in our previous study to evaluate the fit of isodose surfaces to the target volume. They were only applicable to plans for a single target volume. Therefore, this study aimed to generalize these indices for evaluating plans for multiple target volumes and different prescribed doses.

MATERIALS AND METHODS

dFI was redefined as the ratio of the integral dose of the volume occupied by an isodose surface to that of the union of all target volumes. cFI was defined as the integral of dFI from a certain dose level of interest to the prescribed dose to be evaluated. To evaluate the performance of the generalized fit index, brain metastasis, head and neck, lung cancer, liver cancer, and cervical cancer cases were selected. For each case, a pair of plans was designed, with one plan having a better fitting dose distribution. The dose fit of these plans was investigated using cFI, the dose gradient index (GI), and the conformity index (CI).

RESULTS

In total, 26 pairs of evaluations were performed. The correct evaluation rates for cFI, GI, and CI were 96%, 26.92%, and 92.31%, respectively, illustrating that GI was not valid for evaluating complex plans.

CONCLUSIONS

The generalized fit index proved effective for evaluating the dose fit of plans for multiple target volumes with different prescribed doses.

Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'?

Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is 'clinical acceptability'? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of 'clinical acceptability' and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools.

Improvement in plan quality after Implementation of clinical goals in a large network of cancer centers

Clinical Goals (CG) is a tool available in the Varian Eclipse planning system to objectively and visually evaluate the quality of treatment plans based upon user-defined dose-volume parameters. We defined a set of CG for Stereotactic Radiosurgery (SRS) and Intensity-Modulated Radiotherapy (IMRT) based on published data and guidelines and implemented this in a network of cancer centers in India (American Institute of Oncology). A dosimetric study was performed to compare brain SRS and breast IMRT plan quality before and after CG implementation.The CG defined for SRS plans were target V_100% ≥ 98%, dose gradient measure (GM) ≤ 0.5 cm, conformity index (CI) 1.0 to 1.2. For breast IMRT plans, CG defined target V_100% ≥ 97%, V_95% ≥ 95%, V_107% ≤ 2%, V_105% ≤ 10%, and D_max ≤ 2.4 Gy. Dose limits to organs-at-risk (OAR) were summarize in supplemental materials. Twenty brain SRS and 10 breast IMRT treatment plans that were previously delivered on patients were selected and re-planned using CG. The pre and postoptimized plan parameters were compared using student t-tests.For brain SRS plans, the V₁₀₀, GM, and CI for the pre- and post-Clinical-Goals plans were 93.22% ± 7.2% vs 97.96% ± 0.29% (p = 0.009), 0.63 ± 0.16 vs 0.42 ± 0.05 (p < 0.001) and 1.07 ± 0.18 vs 1.06 ± 0.06 (p = 0.79), respectively. There were no differences in max dose to OARs. In breast IMRT plans, the target V_107% for pre and postimplemented plans were 16.50% ± 10.98% vs 0.32% ± 0.32%, respectively (p = 0.001). The average target V_105% were 44.00% ± 15.72% and 8.69% ± 4.53%, respectively (p < 0.001). No differences were found in the average target V_100% (p = 0.128) and V_95% (p = 0.205). The average target D_max were 112.28% ± 1.59% and 109.14% ± 0.73%, respectively (p < 0.001). There were only minor differences in doses to OARs.The implementation of CG in Varian Eclipse significantly improved SRS and IMRT plan quality with enhanced coverage, dose GM, and CI without increased dose to OARs.

Integrated scoring approach to assess radiotherapy plan quality for breast cancer treatment

Background

Proposal of an integrated scoring approach assessing the quality of different treatment techniques in a radiotherapy planning comparison. This scoring method incorporates all dosimetric indices of planning target volumes (PTVs) as well as organs at risk (OARs) and provides a single quantitative measure to select an ideal plan.

Materials and methods

The radiotherapy planning techniques compared were field-in-field (FinF), intensity modulated radiation therapy (IMRT), volumetric modulated arc therapy (VMAT), hybrid IMRT (H-IMRT), and hybrid VMAT (H-VMAT). These plans were generated for twenty-five locally advanced left-sided breast cancer patients. The PTVs were prescribed a hypofractionation dose of 40.5 Gy in 15 fractions. The integrated score for each planning technique was calculated using the proposed formula.

Results

An integrated score value that is close to zero indicates a superior plan. The integrated score that incorporates all dosimetric indices (PTVs and OARs) were 1.37, 1.64, 1.72, 1.18, and 1.24 for FinF, IMRT, VMAT, H-IMRT, and H-VMAT plans, respectively.

Conclusion

The proposed integrated scoring approach is scientific to select a better plan and flexible to incorporate the patient-specific clinical demands. This simple tool is useful to quantify the treatment techniques and able to differentiate the acceptable and unacceptable plans.

Modeling physician’s preference in treatment plan approval of stereotactic body radiation therapy of prostate cancer

Objective. Treatment planning of radiation therapy is a time-consuming task. It is desirable to develop automatic planning approaches to generate plans favorable to physicians. The purpose of this study is to develop a deep learning based virtual physician network (VPN) that models physician’s preference on plan approval for prostate cancer stereotactic body radiation therapy (SBRT). Approach. VPN takes one planning target volume (PTV) and eight organs at risk structure images, as well as a dose distribution of a plan seeking approval as input. It outputs a probability of approving the plan, and a dose distribution indicating improvements to the input dose. Due to the lack of unapproved plans in our database, VPN is trained using an adversarial framework. 68 prostate cancer patients who received 45 Gy in 5-fraction SBRT were selected in this study, with 60 patients for training and cross validation, and 8 patients for independent testing. Main results. The trained VPN was able to differentiate approved and unapproved plans with Area under the curve 0.97 for testing data. For unapproved plans, after applying VPN’s suggested dose improvement, the improved dose agreed with ground truth with relative differences 2.03 ± 2.17 % for PTV D 98 % , 0.49 ± 0.29 % for PTV V 95 % , 3.08 ± 2.24 % for penile bulb D mean , 3.73 ± 2.20 % for rectum V 50 % , and 2.06 ± 1.73 % for bladder V 50 % . Significance. VPN was developed to accurately model a physician’s preference on plan approval and to provide suggestions on how to improve the dose distribution.

A Nomogram for the Determination of the Necessity of Concurrent Chemotherapy in Patients With Stage II-IVa Nasopharyngeal Carcinoma

Background

The efficiency of concurrent chemotherapy (CC) remains controversial for stage II–IVa nasopharyngeal carcinoma (NPC) patients treated with induction chemotherapy (IC) followed by intensity-modulated radiotherapy (IMRT). Therefore, we aimed to propose a nomogram to identify patients who would benefit from CC.

Methods

A total of 434 NPC patients (stage II–IVa) treated with IC followed by IMRT between January 2010 and December 2015 were included. There were 808 dosimetric parameters extracted by the in-house script for each patient. A dosimetric signature was developed with the least absolute shrinkage and selection operator algorithm. A nomogram was built by incorporating clinical factors and dosimetric signature using Cox regression to predict recurrence-free survival (RFS). The C-index was used to evaluate the performance of the nomogram. The patients were stratified into low- and high-risk recurrence according to the optimal cutoff of risk score.

Results

The nomogram incorporating age, TNM stage, and dosimetric signature yielded a C-index of 0.719 (95% confidence interval, 0.658–0.78). In the low-risk group, CC was associated with a 9.4% increase of 5-year locoregional RFS and an 8.8% increase of 5-year overall survival (OS), whereas it was not significantly associated with an improvement of locoregional RFS (LRFS) and OS in the high-risk group. However, in the high-risk group, patients could benefit from adjuvant chemotherapy (AC) by improving 33.6% of the 5-year LRFS.

Conclusions

The nomogram performed an individualized risk quantification of RFS in patients with stage II–IVa NPC treated with IC followed by IMRT. Patients with low risk could benefit from CC, whereas patients with high risk may require additional AC.

A new index for evaluating the fit of dose distribution to target volume: Dose distribution fix index

To develop a new dose evaluation index, fit index (FI), to help evaluate the fit between isodose surfaces at different percentages of the prescription dose and the target volume. Two types of FI, differential and cumulative, were defined. The differential fit index (dFI) was defined as the ratio of the integral dose of volume occupied by an isodose surface to the integral dose of the planning target volume. The cumulative fit index (cFI) was defined as the integral of dFI from the minimum dose of clinical significance to the 100% prescription dose. Performance of the cFI was evaluated with virtual dose distributions. In addition, non-coplanar and coplanar VMAT plans of 20 brain metastasis cases were evaluated using the FI, and the results were compared with results from the dose gradient index (GI) and conformity index (CI). Correlations between cFI and GI, and between cFI and CI were studied and Pearson's correlation coefficients were calculated. dFI and cFI provided comprehensive and objective results in evaluating the dose fit between isodose surfaces at different percentages of the prescription dose and the target volume. Analysis showed a positive correlation between cFI and GI with a Pearson correlation coefficient of 0.928 (p < 0.01) and a negative correlation between cFI and CI with a Pearson correlation coefficient of -0.831 (p < 0.01). dFI and cFI were shown to be effective and convenient tools for evaluating the dose fit of a radiotherapy plan.

Dosimetric analysis of cervical cancer stage IIB patients treated with volumetric modulated arc therapy using plan uncertainty parameters module of Varian Eclipse treatment planning system

Introduction. The present study aims to investigate the dosimetric and radiobiological impact of patient setup errors (PSE) on the target and organs at risk (OAR) of the cervix carcinoma stage IIB patients treated with volumetric-modulated arc therapy (VMAT) delivery technique using plan uncertainty parameters module of Varian Eclipse treatment planning system and in-house developed DVH Analyzer program.Materials and Methods. A total of 976 VMAT plans were generated to simulate the PSE in the base plan that varies from -10 mm to 10 mm in a step size of 1 mm in x- (lateral), y- (craniocaudal), and z- (anteroposterior) directions. The different OAR and tumor (PTV) volumes were delineated in each case. Various plan quality metrics, such as conformity index (CI) and homogeneity index (HI), as well as radiobiological quantities, such as tumor control probability (TCP) and normal tissue control probability (NTCP), were calculated from the DVH bands generated from the cohort of treatment plans associated with each patient case, using an in-house developed 'DVH Analyzer' program. The extracted parameters were statistically analyzed and compared with the base plan's dosimetric parameters having no PSE.Results. The maximum variation of (i) 2.4%, 21.5%, 0.8%, 2.5% in D_2ccof bladder, rectum, small bowel and sigmoid colon respectively; (ii) 19.3% and 18.9% in D_maxof the left and right femoral heads (iii) 16.9% in D_95%of PTV (iv) 12.1% in NTCP of sigmoid colon were observed with change of PSE in all directions. TCP was found to be considerably affected for PSEs larger than 4 mm in x⁺, y⁺, z⁺directions and 7 mm in x^-, y^-and z^-directions, respectively.Conclusion. This study presents the effect of PSE on TCP and NTCP for the cervix carcinoma cases treated with VMAT technique and also recommends daily image guidance to mitigate the effects of PSE.

Evaluating different radiotherapy treatment plans, in terms of critical organ scoring index, conformity index, tumor control probability, and normal tissue complication probability calculations in early glottic larynx carcinoma

Purpose

In this study, it is aimed to compare three different radiotherapy treatment planning techniques in terms of critical organ scoring index (COSI), two different conformity index (CI), tumor control probability (TCP), and normal tissue complication probability (NTCP) calculations in early (T1) glottic larynx carcinoma (T1GL). Furthermore, it is aimed to investigate these parameters compliance with dose-volume histograms (DVH) parameters.

Materials and Methods

Ten T1GL patients were immobilized in a supine position with a head and neck thermoplastic mask. Treatment plans were created with opposed lateral fields (OLAFs) and intensity-modulated radiation therapy (IMRT) techniques with a total dose of 66 Gy in 33 fraction with 2 Gy/day. IMRT fields were selected as five fields (5IMRT) and seven fields (7IMRT). Dosimetric evaluation of three different treatment plans for T1GL carcinoma was performed in two consequential steps. First step was the assessment of planning target volume (PTV), all organs at risks (OARs), and normal tissue (NT) dose calculations according to given dose constraint directions and comparing the plans via DVH. In the second step, for PTV, the compatibility of DVH data with CIs-TCP was investigated where COSI-NTCP was compared with DVH for OARs. The DVH data were considered as reference in all evaluations.

Results

The CI_RTOG mean values were significantly closer to 1 with IMRT plans when compared to OLAF plans (P = 0.005). The CI_PADDICK mean values revealed that OLAF plans were significantly worse than IMRT plans (P = 0.005). No statistically significant difference was found between all three plans in terms of homogeneity index mean values (P = 0.076). The calculated mean TCP values were significantly better for 7IMRT plans when compared to OLAF and 5IMRT plans (P = 0.007 and P = 0.017, respectively). Both NTCP and COSI evaluations, which is compatible with DVH, significantly favored OLAF plan for spinal cord and 7IMRT for thyroid gland. The COSI evaluations, which are compatible with DVH, significantly favored 7IMRT plan for carotid arteries and 5IMRT plan for NT.

Conclusion

Our results demonstrated that CI_PADDICK-TCP calculations for PTV and COSI-NTCP calculations for OARs were compatible with DVH in T1 GL plans. Therefore, we suggest such parameters as valuable tools for choosing the feasible one among multiple plans and even with different treatment machines.

What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans

Plan evaluation is a key step in the radiotherapy treatment workflow. Central to this step is the assessment of treatment plan quality. Hence, it is important to agree on what we mean by plan quality and to be fully aware of which parameters it depends on. We understand plan quality in radiotherapy as the clinical suitability of the delivered dose distribution that can be realistically expected from a treatment plan. Plan quality is commonly assessed by evaluating the dose distribution calculated by the treatment planning system (TPS). Evaluating the 3D dose distribution is not easy, however; it is hard to fully evaluate its spatial characteristics and we still lack the knowledge for personalising the prediction of the clinical outcome based on individual patient characteristics. This advocates for standardisation and systematic collection of clinical data and outcomes after radiotherapy. Additionally, the calculated dose distribution is not exactly the dose delivered to the patient due to uncertainties in the dose calculation and the treatment delivery, including variations in the patient set-up and anatomy. Consequently, plan quality also depends on the robustness and complexity of the treatment plan. We believe that future work and consensus on the best metrics for quality indices are required. Better tools are needed in TPSs for the evaluation of dose distributions, for the robust evaluation and optimisation of treatment plans, and for controlling and reporting plan complexity. Implementation of such tools and a better understanding of these concepts will facilitate the handling of these characteristics in clinical practice and be helpful to increase the overall quality of treatment plans in radiotherapy.

Clinical validation of a graphical method for radiation therapy plan quality assessment

Background

This work aims at clinically validating a graphical tool developed for treatment plan assessment, named SPIDERplan, by comparing the plan choices based on its scoring with the radiation oncologists (RO) clinical preferences.

Methods

SPIDERplan validation was performed for nasopharynx pathology in two steps. In the first step, three ROs from three Portuguese radiotherapy departments were asked to blindly evaluate and rank the dose distributions of twenty pairs of treatment plans. For plan ranking, the best plan from each pair was selected. For plan evaluation, the qualitative classification of ‘Good’, ‘Admissible with minor deviations’ and ‘Not Admissible’ were assigned to each plan. In the second step, SPIDERplan was applied to the same twenty patient cases. The tool was configured for two sets of structures groups: the local clinical set and the groups of structures suggested in international guidelines for nasopharynx cancer. Group weights, quantifying the importance of each group and incorporated in SPIDERplan, were defined according to RO clinical preferences and determined automatically by applying a mixed linear programming model for implicit elicitation of preferences. Intra- and inter-rater ROs plan selection and evaluation were assessed using Brennan-Prediger kappa coefficient.

Results

Two-thirds of the plans were qualitatively evaluated by the ROs as ‘Good’. Concerning intra- and inter-rater variabilities of plan selection, fair agreements were obtained for most of the ROs. For plan evaluation, substantial agreements were verified in most cases. The choice of the best plan made by SPIDERplan was identical for all sets of groups and, in most cases, agreed with RO plan selection. Differences between RO choice and SPIDERplan analysis only occurred in cases for which the score differences between the plans was very low. A score difference threshold of 0.005 was defined as the value below which two plans are considered of equivalent quality.

Conclusion

Generally, SPIDERplan response successfully reproduced the ROs plan selection. SPIDERplan assessment performance can represent clinical preferences based either on manual or automatic group weight assignment. For nasopharynx cases, SPIDERplan was robust in terms of the definitions of structure groups, being able to support different configurations without losing accuracy.

The mean absolute dose deviation-A common metric for the evaluation of dose-volume histograms in radiation therapy

Radiation therapy needs to balance between delivering a high dose to targets and the lowest possible dose to the organs at risk. Dose-volume histograms (DVHs) summarize the distribution of radiation doses in the irradiated structures. The interpretation can however be a challenge when the number of structures is high. We propose the use of a simple summary metric. We define the mean absolute dose deviation (MADD) as the average of absolute differences between a DVH and a reference dose. The properties are evaluated through numerical analysis. Calculus trivially shows the identity of the MADD and the area between curves, between DVH and reference dose. Computation of the MADD is the same regardless of structures' designation, whether organ at risk or target, on the same dose scale. Basic calculus properties open the perspective of applying the MADD to the evaluation of treatment plans.

Spatial optimization for radiation therapy of brain tumours

Glioblastomas are the most common primary brain tumours. They are known for their highly aggressive growth and invasion, leading to short survival times. Treatments for glioblastomas commonly involve a combination of surgical intervention, chemotherapy, and external beam radiation therapy (XRT). Previous works have not only successfully modelled the natural growth of glioblastomas in vivo, but also show potential for the prediction of response to radiation prior to treatment. This suggests that the efficacy of XRT can be optimized before treatment in order to yield longer survival times. However, while current efforts focus on optimal scheduling of radiotherapy treatment, they do not include a similarly sophisticated spatial optimization. In an effort to improve XRT, we present a method for the spatial optimization of radiation profiles. We expand upon previous results in the general problem and examine the more physically reasonable cases of 1-step and 2-step radiation profiles during the first and second XRT fractions. The results show that by including spatial optimization in XRT, while retaining a constant prescribed total dose amount, we are able to increase the total cell kill from the clinically-applied uniform case.

Evaluation of plan optimisers in prostate VMAT using the dose distribution index

Purpose:

Dose distribution index (DDI) is a treatment planning evaluation parameter, reflecting dosimetric information of target coverage that can help to spare organs at risk (OARs) and remaining volume at risk (RVR). The index has been used to evaluate and compare prostate volumetric modulated arc therapy (VMAT) plans using two different plan optimisers, namely photon optimisation (PO) and its predecessor, progressive resolution optimisation (PRO).

Materials and methods:

Twenty prostate VMAT treatment plans were created using the PO and PRO in this retrospective study. The 6 MV photon beams and a dose prescription of 78 Gy/39 fractions were used in plans with the same dose–volume criteria for plan optimisation. Dose–volume histograms (DVHs) of the planning target volume (PTV), as well as of OARs such as the rectum, bladder, left and right femur were determined in each plan. DDIs were calculated and compared for plans created by the PO and PRO based on DVHs of the PTV and all OARs.

Results:

The mean DDI values were 0·784 and 0·810 for prostate VMAT plans created by the PO and PRO, respectively. It was found that the DDI of the PRO plan was about 3·3% larger than the PO plan, which means that the dose distribution of the target coverage and sparing of OARs in the PRO plan was slightly better. Changing the weighting factors in different OARs would vary the DDI value by ∼7%. However, for plan comparison based on the same set of dose–volume criteria, the effect of weighting factor can be neglected because they were the same in the PO and PRO.

Conclusions:

Based on the very similar DDI values calculated from the PO and PRO plans, with the DDI value in the PRO plan slightly larger than that of the PO, it may be concluded that the PRO can create a prostate VMAT plan with slightly better dose distribution regarding the target coverage and sparing of OARs. Moreover, we found that the DDI is a simple and comprehensive dose–volume parameter for plan evaluation considering the target, OARs and RVR.

Temporally feathered intensity-modulated radiation therapy: A planning technique to reduce normal tissue toxicity

PURPOSE

Intensity-modulated radiation therapy (IMRT) has allowed optimization of three-dimensional spatial radiation dose distributions permitting target coverage while reducing normal tissue toxicity. However, radiation-induced normal tissue toxicity is a major contributor to patients' quality of life and often a dose-limiting factor in the definitive treatment of cancer with radiation therapy. We propose the next logical step in the evolution of IMRT using canonical radiobiological principles, optimizing the temporal dimension through which radiation therapy is delivered to further reduce radiation-induced toxicity by increased time for normal tissue recovery. We term this novel treatment planning strategy "temporally feathered radiation therapy" (TFRT).

METHODS

Temporally feathered radiotherapy plans were generated as a composite of five simulated treatment plans each with altered constraints on particular hypothetical organs at risk (OARs) to be delivered sequentially. For each of these TFRT plans, OARs chosen for feathering receive higher doses while the remaining OARs receive lower doses than the standard fractional dose delivered in a conventional fractionated IMRT plan. Each TFRT plan is delivered a specific weekday, which in effect leads to a higher dose once weekly followed by four lower fractional doses to each temporally feathered OAR. We compared normal tissue toxicity between TFRT and conventional fractionated IMRT plans by using a dynamical mathematical model to describe radiation-induced tissue damage and repair over time.

RESULTS

Model-based simulations of TFRT demonstrated potential for reduced normal tissue toxicity compared to conventionally planned IMRT. The sequencing of high and low fractional doses delivered to OARs by TFRT plans suggested increased normal tissue recovery, and hence less overall radiation-induced toxicity, despite higher total doses delivered to OARs compared to conventional fractionated IMRT plans. The magnitude of toxicity reduction by TFRT planning was found to depend on the corresponding standard fractional dose of IMRT and organ-specific recovery rate of sublethal radiation-induced damage.

CONCLUSIONS

TFRT is a novel technique for treatment planning and optimization of therapeutic radiotherapy that considers the nonlinear aspects of normal tissue repair to optimize toxicity profiles. Model-based simulations of TFRT to carefully conceptualized clinical cases have demonstrated potential for radiation-induced toxicity reduction in a previously described dynamical model of normal tissue complication probability (NTCP).

Incorporating big data into treatment plan evaluation: Development of statistical DVH metrics and visualization dashboards

Purpose

To develop statistical dose-volume histogram (DVH)–based metrics and a visualization method to quantify the comparison of treatment plans with historical experience and among different institutions.

Methods and materials

The descriptive statistical summary (ie, median, first and third quartiles, and 95% confidence intervals) of volume-normalized DVH curve sets of past experiences was visualized through the creation of statistical DVH plots. Detailed distribution parameters were calculated and stored in JavaScript Object Notation files to facilitate management, including transfer and potential multi-institutional comparisons. In the treatment plan evaluation, structure DVH curves were scored against computed statistical DVHs and weighted experience scores (WESs). Individual, clinically used, DVH-based metrics were integrated into a generalized evaluation metric (GEM) as a priority-weighted sum of normalized incomplete gamma functions. Historical treatment plans for 351 patients with head and neck cancer, 104 with prostate cancer who were treated with conventional fractionation, and 94 with liver cancer who were treated with stereotactic body radiation therapy were analyzed to demonstrate the usage of statistical DVH, WES, and GEM in a plan evaluation. A shareable dashboard plugin was created to display statistical DVHs and integrate GEM and WES scores into a clinical plan evaluation within the treatment planning system. Benchmarking with normal tissue complication probability scores was carried out to compare the behavior of GEM and WES scores.

Results

DVH curves from historical treatment plans were characterized and presented, with difficult-to-spare structures (ie, frequently compromised organs at risk) identified. Quantitative evaluations by GEM and/or WES compared favorably with the normal tissue complication probability Lyman-Kutcher-Burman model, transforming a set of discrete threshold-priority limits into a continuous model reflecting physician objectives and historical experience.

Conclusions

Statistical DVH offers an easy-to-read, detailed, and comprehensive way to visualize the quantitative comparison with historical experiences and among institutions. WES and GEM metrics offer a flexible means of incorporating discrete threshold-prioritizations and historic context into a set of standardized scoring metrics. Together, they provide a practical approach for incorporating big data into clinical practice for treatment plan evaluations.