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Sensitivity analysis for lexicographic ordering in radiation therapy treatment planning.

ABSTRACT: To introduce a method to efficiently identify and calculate meaningful tradeoffs between criteria in an interactive IMRT treatment planning procedure. The method provides a systematic approach to developing high-quality radiation therapy treatment plans.Treatment planners consider numerous dosimetric criteria of varying importance that, when optimized simultaneously through multicriteria optimization, yield a Pareto frontier which represents the set of Pareto-optimal treatment plans. However, generating and navigating this frontier is a time-consuming, nontrivial process. A lexicographic ordering (LO) approach to IMRT uses a physician's criteria preferences to partition the treatment planning decisions into a multistage treatment planning model. Because the relative importance of criteria optimized in the different stages may not necessarily constitute a strict prioritization, the authors introduce an interactive process, sensitivity analysis in lexicographic ordering (SALO), to allow the treatment planner control over the relative sequential-stage tradeoffs. By allowing this flexibility within a structured process, SALO implicitly restricts attention to and allows exploration of a subset of the Pareto efficient frontier that the physicians have deemed most important.Improvements to treatment plans over a LO approach were found by implementing the SALO procedure on a brain case and a prostate case. In each stage, a physician assessed the tradeoff between previous stage and current stage criteria. The SALO method provided critical tradeoff information through curves approximating the relationship between criteria, which allowed the physician to determine the most desirable treatment plan.The SALO procedure provides treatment planners with a directed, systematic process to treatment plan selection. By following a physician's prioritization, the treatment planner can avoid wasting effort considering clinically inferior treatment plans. The planner is guided by criteria importance, but given the information necessary to accurately adjust the relative importance at each stage. Through these attributes, the SALO procedure delivers an approach well balanced between efficiency and flexibility.

SUBMITTER: Long T

PROVIDER: S-EPMC3371080 | biostudies-other | 2012 Jun

REPOSITORIES: biostudies-other

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Project description:Treatment planning for whole-brain radiation treatment is technically a simple process, but in practice it takes valuable clinical time of repetitive and tedious tasks. This report presents a method that automatically segments the relevant target and normal tissues, and creates a treatment plan in only a few minutes after patient simulation. Segmentation of target and critical structures is performed automatically through morphological operations on the soft tissue and was validated by comparing with manual clinical segmentation using the Dice coefficient and Hausdorff distance. The treatment plan is generated by searching a database of previous cases for patients with similar anatomy. In this search, each database case is ranked in terms of similarity using a customized metric designed for sensitivity by including only geometrical changes that affect the dose distribution. The database case with the best match is automatically modified to replace relevant patient info and isocenter position while maintaining original beam and MLC settings. Fifteen patients with marginally acceptable treatment plans were used to validate the method. In each of these cases the anatomy was accurately segmented, but the beams and MLC settings led to a suboptimal treatment plan by either underdosing the brain or excessively irradiating critical normal tissues. For each case, the anatomy was automatically segmented with the proposed method, and the automated and anual segmentations were then compared. The mean Dice coefficient was 0.97, with a standard deviation of 0.008 for the brain, 0.85 ± 0.009 for the eyes, and 0.67 ± 0.11 for the lens. The mean Euclidian distance was 0.13 ± 0.13 mm for the brain, 0.27± 0.31 for the eye, and 2.34 ± 7.23 for the lens. Each case was then subsequently matched against a database of 70 validated treatment plans and the best matching plan (termed autoplanned), was compared retrospectively with the clinical plans in terms of brain coverage and maximum doses to critical structures. Maximum doses were reduced by a maximum of 8.37 Gy for the left eye (mean 2.08), 11.67 for the right eye (1.90) and, respectively, 25.44 (5.59) for the left lens and 24.40 (4.85) for the right lens. Time to generate the autoplan, including the segmentation, was 3-4min. Automated database- based matching is an alternative to classical treatment planning that improves quality while providing a cost-effective solution to planning through modifying previous validated plans to match a current patient's anatomy.

Project description:PurposeTo assess the impact of two multileaf collimator (MLC) systems (2.5 and 5 mm leaf widths) on three-dimensional conformal radiotherapy, intensity-modulated radiotherapy, and dynamic conformal arc techniques for stereotactic body radiation therapy (SBRT) of liver and lung lesions.MethodsTwenty-nine SBRT plans of primary liver (n=11) and lung (n=18) tumors were the basis of this study. Five-millimeter leaf width 120-leaf Varian Millennium (M120) MLC-based plans served as reference, and were designed using static conformal beams (3DCRT), sliding-window intensity-modulated beams (IMRT), or dynamic conformal arcs (DCA). Reference plans were either re-optimized or recomputed, with identical planning parameters, for a 2.5-mm width 120-leaf BrainLAB/Varian high-definition (HD120) MLC system. Dose computation was based on the anisotropic analytical algorithm (AAA, Varian Medical Systems) with tissue heterogeneity taken into account. Each plan was normalized such that 100% of the prescription dose covered 95% of the planning target volume (PTV). Isodose distributions and dose-volume histograms (DVHs) were computed and plans were evaluated with respect to target coverage criteria, normal tissue sparing criteria, as well as treatment efficiency.ResultsDosimetric differences achieved using M120 and the HD120 MLC planning were generally small. Dose conformality improved in 51.7%, 62.1% and 55.2% of the IMRT, 3DCRT and DCA cases, respectively, with use of the HD120 MLC system. Dose heterogeneity increased in 75.9%, 51.7%, and 55.2% of the IMRT, 3DCRT and DCA cases, respectively, with use of the HD120 MLC system. DVH curves demonstrated a decreased volume of normal tissue irradiated to the lower (90%, 50% and 25%) isodose levels with the HD120 MLC.ConclusionData derived from the present comparative assessment suggest dosimetric merit of the high definition MLC system over the millennium MLC system. However, the clinical significance of these results warrants further investigation in order to determine whether the observed dosimetric advantages translate into outcome improvements.

Project description:PurposeThis work aims to study the generalizability of a pre-developed deep learning (DL) dose prediction model for volumetric modulated arc therapy (VMAT) for prostate cancer and to adapt the model, via transfer learning with minimal input data, to three different internal treatment planning styles and one external institution planning style.MethodsWe built the source model with planning data from 108 patients previously treated with VMAT for prostate cancer. For the transfer learning, we selected patient cases planned with three different styles, 14-29 cases per style, in the same institution and 20 cases treated in a different institution to adapt the source model to four target models in total. We compared the dose distributions predicted by the source model and the target models with the corresponding clinical plan dose used for patient treatments and quantified the improvement in the prediction quality for the target models over the source model using the Dice similarity coefficients (DSC) of 0% to 100% isodose volumes and the dose-volume-histogram (DVH) parameters of the planning target volume and the organs-at-risk.ResultsThe source model accurately predicts dose distributions for plans generated in the same source style, but performs sub-optimally for the three different internal and one external target styles, with the mean DSC ranging between 0.81-0.94 and 0.82-0.91 for the internal and the external styles, respectively. With transfer learning, the target model predictions improved the mean DSC to 0.88-0.95 and 0.92-0.96 for the internal and the external styles, respectively. Target model predictions significantly improved the accuracy of the DVH parameter predictions to within 1.6%.ConclusionWe demonstrated the problem of model generalizability for DL-based dose prediction and the feasibility of using transfer learning to solve this problem. With 14-29 cases per style, we successfully adapted the source model into several different practice styles. This indicates a realistic way forward to widespread clinical implementation of DL-based dose prediction.

Project description:Spine stereotactic body radiation therapy (SBRT) is becoming widely accepted as first-line treatment of oligometastatic spine disease as well as in the postoperative setting. The reported incidence of myelopathy is very low and guidelines vary widely on the maximum tolerable dose of the spinal cord and thecal sac.Between April 2008 and December 2010, radiation parameters were exported for 46 patients treated with spine SBRT at the Mayo Clinic. Using an in-house data mining program, dose-volume histogram constraints were extracted, including dose prescription, dose fractionation, planning target volume, planning target volume coverage, maximum dose to the cord, D2cc thecal sac, and D2cc spinal cord. Diagnostic magnetic resonance imaging scans and/or computed tomography myelograms were fused with the planning set to delineate the cord and thecal sac for receiver operating characteristic analysis of cord and thecal sac subvolume doses. A high-resolution planning at-risk volume was created in 1-mm increments for cord (1-7 mm) and the thecal sac (1-2 mm) to examine dose gradients that might be correlated with toxicity.No patients experienced myelopathy with a median follow-up of 14 months. The most common toxicities were pain and nausea. Median values of D2cc maximum dose (maximum dose received by 2 cc of the organ at risk; biologically equivalent 2-Gy dose maximum [EQD2]) for cord and thecal sac were 38.5 Gy (range, 7.5-67.9 Gy) and 67.7 Gy (range, 15.5-155.8 Gy), respectively. Median values for high-dose subvolumes for cord and thecal sac were 2 times higher than the doses for 5% predicted grade 3 cord toxicity as recommended in the current literature. Cord D0.1cc[EQD2] ≥23.8 Gy was correlated with pain flare (n = 5). Thecal sac D2cc [EQD2] ≥29.3 Gy was a significant indicator of nausea.Current guidelines may overestimate the risk of myelopathy from spine SBRT. The current study's population included both radiation-naïve and retreatment cases, but no myelopathy was observed despite exceeding recommended spine limits.

Project description:Recent research efforts investigating dose escalation techniques for three-dimensional conformal radiation therapy (3D CRT) and intensity modulated radiation therapy (IMRT) have demonstrated great benefit when high-dose hypofractionated treatment schemes are implemented. The use of these paradigms emphasizes the importance of smaller treatment margins to avoid high dose to surrounding normal tissue or organs at risk (OARs). However, tighter margins may lead to underdosage of the target due to the presence of organ motion. It is important to characterize organ motion and possibly account for it during treatment delivery. The need for real-time localization of dynamic targets has encouraged the use and development of more continuous motion monitoring systems such as kilo-voltage/fluoroscopic imaging, electromagnetic tracking, and optical monitoring systems.This paper presents the implementation of an algorithm to quantify translational and rotational interfractional and intrafractional prostate motion and compute the dosimetric effects of these motion patterns. The estimated delivered dose is compared with the static plan dose to evaluate the success of delivering the plan in the presence of prostate motion. The method is implemented on a commercial treatment planning system (Pinnacle(3), Philips Radiation Oncology Systems, Philips Healthcare) and is termed delivered dose investigational tool (DiDIT). The DiDIT implementation in Pinnacle(3) is validated by comparisons with previously published results. Finally, different workflows are discussed with respect to the potential use of this tool in clinical treatment planning.The DiDIT dose estimation process took approximately 5-20 min (depending on the number of fractions analyzed) on a Pinnacle(3) 9.100 research version running on a Dell M90 system (Dell, Inc., Round Rock, TX, USA) equipped with an Intel Core 2 Duo processor (Intel Corporation, Santa Clara, CA, USA). The DiDIT implementation in Pinnacle(3) was found to be in agreement with previously published results, on the basis of the percent dose difference (PDD). This metric was also utilized to compare plan dose versus delivered dose, for prostate targets in three clinically acceptable treatment plans.This paper presents results from the implementation of an algorithm on a commercially available treatment planning system that quantifies the dosimetric effects of interfractional and intrafractional motion in external beam radiation therapy (EBRT) of prostate cancer. The implementation of this algorithm within a commercial treatment planning system such as Pinnacle(3) enables easy deployment in the existing clinical workflow. The results of the PDD tests validate the implementation of the DiDIT algorithm in Pinnacle(3), in comparison with previously published results.

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Sensitivity analysis for lexicographic ordering in radiation therapy treatment planning.

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