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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:BackgroundTo investigate the feasibility of a knowledge-based automated intensity-modulated radiation therapy (IMRT) planning technique for locally advanced nasopharyngeal carcinoma (NPC) radiotherapy.MethodsOne hundred forty NPC patients treated with definitive radiation therapy with the step-and-shoot IMRT techniques were retrospectively selected and separated into a knowledge library (n = 115) and a test library (n = 25). For each patient in the knowledge library, the overlap volume histogram (OVH), target volume histogram (TVH) and dose objectives were extracted from the manually generated plan. 5-fold cross validation was performed to divide the patients in the knowledge library into 5 groups before validating one group by using the other 4 groups to train each neural network (NN) machine learning models. For patients in the test library, their OVH and TVH were then used by the trained models to predict a corresponding set of mean dose objectives, which were subsequently used to generate automated plans (APs) in Pinnacle planning system via an in-house developed automated scripting system. All APs were obtained after a single step of optimization. Manual plans (MPs) for the test patients were generated by an experienced medical physicist strictly following the established clinical protocols. The qualities of the APs and MPs were evaluated by an attending radiation oncologist. The dosimetric parameters for planning target volume (PTV) coverage and the organs-at-risk (OAR) sparing were also quantitatively measured and compared using Mann-Whitney U test and Bonferroni correction.ResultsAPs and MPs had the same rating for more than 80% of the patients (19 out of 25) in the test group. Both AP and MP achieved PTV coverage criteria for no less than 80% of the patients. For each OAR, the number of APs achieving its criterion was similar to that in the MPs. The AP approach improved planning efficiency by greatly reducing the planning duration to about 17% of the MP (9.85 ± 1.13 min vs. 57.10 ± 6.35 min).ConclusionA robust and effective knowledge-based IMRT treatment planning technique for locally advanced NPC is developed. Patient specific dose objectives can be predicted by trained NN models based on the individual's OVH and clinical TVH goals. The automated planning scripts can use these dose objectives to efficiently generate APs with largely shortened planning time. These APs had comparable dosimetric qualities when compared to our clinic's manual plans.

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.

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Sensitivity analysis for lexicographic ordering in radiation therapy treatment planning.

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