Dataset Information

Automatic planning of head and neck treatment plans.

ABSTRACT: Treatment planning is time-consuming and the outcome depends on the person performing the optimization. A system that automates treatment planning could potentially reduce the manual time required for optimization and could also provide a method to reduce the variation between persons performing radiation dose planning (dosimetrist) and potentially improve the overall plan quality. This study evaluates the performance of the Auto-Planning module that has recently become clinically available in the Pinnacle3 radiation therapy treatment planning system. Twenty-six clinically delivered head and neck treatment plans were reoptimized with the Auto-Planning module. Comparison of the two types of treatment plans were performed using DVH metrics and a blinded clinical evaluation by two senior radiation oncologists using a scale from one to six. Both evaluations investigated dose coverage of target and dose to healthy tissues. Auto-Planning was able to produce clinically acceptable treatment plans in all 26 cases. Target coverages in the two types of plans were similar, but automatically generated plans had less irradiation of healthy tissue. In 94% of the evaluations, the autoplans scored at least as high as the previously delivered clinical plans. For all patients, the Auto-Planning tool produced clinically acceptable head and neck treatment plans without any manual intervention, except for the initial target and OAR delineations. The main benefit of the method is the likely improvement in the overall treatment quality since consistent, high-quality plans are generated which even can be further optimized, if necessary. This makes it possible for the dosimetrist to focus more time on difficult dose planning goals and to spend less time on the more tedious parts of the planning process.

SUBMITTER: Hazell I

PROVIDER: S-EPMC5690191 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Automatic planning of head and neck treatment plans.

Hazell Irene I Bzdusek Karl K Kumar Prashant P Hansen Christian R CR Bertelsen Anders A Eriksen Jesper G JG Johansen Jørgen J Brink Carsten C

Journal of applied clinical medical physics 20160108 1

Treatment planning is time-consuming and the outcome depends on the person performing the optimization. A system that automates treatment planning could potentially reduce the manual time required for optimization and could also provide a method to reduce the variation between persons performing radiation dose planning (dosimetrist) and potentially improve the overall plan quality. This study evaluates the performance of the Auto-Planning module that has recently become clinically available in t ...[more]

PMID: 26894364

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Project description:PurposeTo develop a tradeoff hyperplane model to facilitate tradeoff decision-making before inverse planning.Methods and materialsWe propose a model-based approach to determine the tradeoff hyperplanes that allow physicians to navigate the clinically viable space of plans with best achievable dose-volume parameters before planning. For a given case, a case reference set (CRS) is selected using a novel anatomic similarity metric from a large reference plan pool. Then, a regression model is built on the CRS to estimate the expected dose-volume histograms (DVHs) for the current case. This model also predicts the DVHs for all CRS cases and captures the variation from the corresponding DVHs in the clinical plans. Finally, these DVH variations are analyzed using the principal component analysis to determine the tradeoff hyperplane for the current case. To evaluate the effectiveness of the proposed approach, 244 head and neck cases were randomly partitioned into reference (214) and validation (30) sets. A tradeoff hyperplane was built for each validation case and evenly sampled for 12 tradeoff predictions. Each prediction yielded a tradeoff plan. The root-mean-square errors of the predicted and the realized plan DVHs were computed for prediction achievability evaluation.ResultsThe tradeoff hyperplane with 3 principal directions accounts for 57.8% ± 3.6% of variations in the validation cases, suggesting the hyperplanes capture a significant portion of the clinical tradeoff space. The average root-mean-square errors in 3 tradeoff directions are 5.23 ± 2.46, 5.20 ± 2.52, and 5.19 ± 2.49, compared with 4.96 ± 2.48 of the knowledge-based planning predictions, indicating that the tradeoff predictions are comparably achievable.ConclusionsClinically relevant tradeoffs can be effectively extracted from existing plans and characterized by a tradeoff hyperplane model. The hyperplane allows physicians and planners to explore the best clinically achievable plans with different organ-at-risk sparing goals before inverse planning and is a natural extension of the current knowledge-based planning framework.

Project description:BackgroundProton beam therapy is promising for the treatment of head and neck cancer (HNC), but it is sensitive to uncertainties in patient positioning and particle range. Studies have shown that the planning target volume (PTV) concept may not be sufficient to ensure robustness of the target coverage. A few planning studies have considered irradiation of unilateral HNC targets with protons, but they have only taken into account the dose on the nominal plan, without considering anatomy changes occurring during the treatment course.MethodsFour pencil beam scanning (PBS) proton therapy plans were calculated for 8 HNC patients with unilateral target volumes: single-field (SFO) and multi-field optimized (MFO) plans, either using the PTV concept or clinical target volume (CTV)-based robust optimization. The dose was recalculated on computed tomography (CT) scans acquired during the treatment course. Doses to target volumes and organs at risk (OARs) were compared for the nominal plans, cumulative doses considering anatomical changes, and additional setup and range errors in each fraction. If required, the treatment plan was adapted, and the dose was compared with the non-adapted plan.ResultsAll nominal plans fulfilled the clinical specifications for target coverage, but significantly higher doses on the ipsilateral parotid gland were found for both SFO approaches. MFO PTV-based plans had the lowest robustness against range and setup errors. During the treatment course, the influence of the anatomical variation on the dose has shown to be patient specific, mostly independent of the chosen planning approach. Nine plans in four patients required adaptation, which led to a significant improvement of the target coverage and a slight reduction in the OAR dose in comparison to the cumulative dose without adaptation.ConclusionsThe use of robust MFO optimization is recommended for ensuring plan robustness and reduced doses in the ipsilateral parotid gland. Anatomical changes occurring during the treatment course might degrade the target coverage and increase the dose in the OARs, independent of the chosen planning approach. For some patients, a plan adaptation may be required.

Project description:Purpose:Accurate contouring in head and neck cancer (HNC) is critical. International consensus guidelines recommend the 5 + 5 mm rule for expansions around the primary tumor, wherein high- and low-dose clinical target volumes (CTV-P1 and CTV-P2, respectively) are created using successive 5 mm expansions on the gross tumor volume. To our knowledge, the necessity of a low-dose CTV-P2 has never been assessed; therefore, we evaluated the dosimetric impact of adding a CTV-P2 expansion using the 5 + 5 mm rule compared with contouring with a high-dose CTV-P1 alone. Methods and materials:A retrospective study of clinically delivered (chemo)radiation therapy treatment plans for HNC was conducted. All patients were treated with 70 Gy in 35 fractions using volumetric modulated arc therapy in a single phase. CTV-P2 was retrospectively contoured per guidelines as a 5 mm expansion on CTV-P1 from the clinical plan, carving off specified barriers to spread. We used a 5 mm planning target volume (PTV) expansion. Our primary outcome was whether 95% of the volume of the PTV for the CTV-P2 contour (ie, PTV-P2) received at least 56 Gy. To assess dose falloff, the coverage of a PTV ring structure was created by subtracting PTV-P1 from PTV-P2. Results:Twenty-seven patients from 4 HNC subsites (base of tongue, tonsil, hypopharynx, and supraglottic larynx) were included. In all 108 treatment plans, at least 95% of the PTV-P2 structure received at least 56 Gy. The minimum volume of the PTV-P2 structure receiving at least 56 Gy was 97.4%. Eight of 108 treatment plans had borderline coverage of the PTV ring substructure alone. Conclusions:Adding a CTV-P2 structure using the 5 + 5 mm rule had no dosimetric impact, adds contouring time, adds treatment planning complexity, and could potentially introduce errors. The 5 + 5 mm rule may have value in other settings, such as when smaller PTV margins are used, and warrants further evaluation with prospective or randomized studies.

Project description:Accurate delineation of organs at risk (OARs) is a precondition for intensity modulated radiation therapy. However, manual delineation of OARs is time consuming and prone to high interobserver variability. Because of image artifacts and low image contrast between different structures, however, the number of available approaches for autosegmentation of structures in the head-neck area is still rather low. In this project, a new approach for automated segmentation of head-neck CT images that combine the robustness of multiatlas-based segmentation with the flexibility of geodesic active contours and the prior knowledge provided by statistical appearance models is presented.The presented approach is using an atlas-based segmentation approach in combination with label fusion in order to initialize a segmentation pipeline that is based on using statistical appearance models and geodesic active contours. An anatomically correct approximation of the segmentation result provided by atlas-based segmentation acts as a starting point for an iterative refinement of this approximation. The final segmentation result is based on using model to image registration and geodesic active contours, which are mutually influencing each other.18 CT images in combination with manually segmented labels of parotid glands and brainstem were used in a leave-one-out cross validation scheme in order to evaluate the presented approach. For this purpose, 50 different statistical appearance models have been created and used for segmentation. Dice coefficient (DC), mean absolute distance and max. Hausdorff distance between the autosegmentation results and expert segmentations were calculated. An average Dice coefficient of DC = 0.81 (right parotid gland), DC = 0.84 (left parotid gland), and DC = 0.86 (brainstem) could be achieved.The presented framework provides accurate segmentation results for three important structures in the head neck area. Compared to a segmentation approach based on using multiple atlases in combination with label fusion, the proposed hybrid approach provided more accurate results within a clinically acceptable amount of time.

Project description:BackgroundPencil beam scanning (PBS) proton therapy offers dosimetric advantages for several treatment sites, including head and neck (H&N). However, to achieve the optimal target coverage and robustness, these plans can be complex and time consuming to develop and optimize. Automating the treatment planning process can ensure a high-quality and standardized plan, reduce burden to the planner, and decrease time-to-treatment. We utilized in-house scripting to automate a four-field multi-field optimization (MFO) H&N planning technique.Methods and materialsTen bilateral H&N patients were planned in RayStation v6 with a four-field modified-X beam configuration using MFO planning. Automation included creation of avoidance structures to control spot placement and development of standardized beams, PBS spot settings, robust optimization objectives, and patient-specific predicted planning constraints. Each patient was planned both with and without automation to evaluate differences in planning time, perceived effort and plan quality, plan robustness, and OAR sparing.ResultsOn average, scripted plans required 3.2 h, compared to 4.3 h without the script. There was no difference in target coverage or plan robustness with or without automation. Automation significantly reduced mean dose to the oral cavity, parotids, esophagus, trachea, and larynx. Perceived effort was scaled from 1 (minimum effort) to 100 (maximum effort), and automation reduced perceived effort by 42% (p < 0.05). Two non-scripted plans required re-planning due to errors.ConclusionsAutomation of this multi-beam, the MFO proton planning process reduced planning time and improved OAR sparing compared to the same planning process without scripting. Scripting generation of complex structures and planning objectives reduced burden on the planner. With most current treatment planning software, this automation is simple to implement and can standardize quality of care across all treatment planners.

Project description:To demonstrate that novice dosimetry planners efficiently create clinically acceptable IMRT plans for head and neck cancer (HNC) patients using a commercially available multicriteria optimization (MCO) system.Twenty HNC patients were enrolled in this in-silico comparative planning study. Per patient, novice planners with less experience in dosimetry planning created an IMRT plan using an MCO system (RayStation). Furthermore, a conventionally planned clinical IMRT plan was available (Pinnacle(3)). All conventional IMRT and MCO-plans were blind-rated by two expert radiation-oncologists in HNC, using a 5-point scale (1-5 with 5 the highest score) assessment form comprising 10 questions. Additionally, plan quality was reported in terms of planning time, dosimetric and normal tissue complication probability (NTCP) comparisons. Inter-rater reliability was derived using the intra-class correlation coefficient (ICC).In total, the radiation-oncologists rated 800 items on plan quality. The overall plan score indicated no differences between both planning techniques (conventional IMRT: 3.8 ± 1.2 vs. MCO: 3.6 ± 1.1, p = 0.29). The inter-rater reliability of all ratings was 0.65 (95% CI: 0.57-0.71), indicating substantial agreement between the radiation-oncologists. In 93% of cases, the scoring difference of the conventional IMRT and MCO-plans was one point or less. Furthermore, MCO-plans led to slightly higher dose uniformity in the therapeutic planning target volume, to a lower integral body dose (13.9 ± 4.5 Gy vs. 12.9 ± 4.0 Gy, p < 0.001), and to reduced dose to the contra-lateral parotid gland (28.1 ± 11.8 Gy vs. 23.0 ± 11.2 Gy, p < 0.002). Consequently, NTCP estimates for xerostomia reduced by 8.4 ± 7.4% (p < 0.003). The hands-on time of the conventional IMRT planning was approximately 205 min. The time to create an MCO-plan was on average 43 ± 12 min.MCO planning enables novice treatment planners to create high quality IMRT plans for HNC patients. Plans were created with vastly reduced planning times, requiring less resources and a short learning curve.

Project description:Head and neck cancers centered at the base of skull are better visualized on MRI than on CT. The purpose of this investigation was to investigate the accuracy of bulk density assignment in head and neck intensity-modulated radiation therapy (IMRT) treatment plan optimization. Our study investigates dose calculation differences between density-assigned MRI and CT, and identifies potential limitations related to dental implants and MRI geometrical distortion in the framework of MRI-only-based treatment planning. Bulk density assignment was performed and applied onto MRI to generate three MRI image sets with increasing levels of heterogeneity for seven patients: 1) MRIW: all water-equivalent; 2) MRIW+B: included bone with density of 1.53 g/cm3; and 3) MRIW+B+A: included bone and air. Using identical planning and optimization parameters, MRI-based IMRT plans were generated and compared to corresponding, forward-calculated, CT-based plans on the basis of target coverage, isodose distributions, and dose-volume histograms (DVHs). Phantom studies were performed to assess the magnitude and spatial dependence of MRI geometrical distortion. MRIW-based dose calculations overestimated target coverage by 16.1%. Segmentation of bone reduced differences to within 2% of the coverage area on the CT-based plan. Further segmentation of air improved conformity near air-tissue interfaces. Dental artifacts caused substantial target coverage overestimation even on MRIW+B+A. Geometrical distortion was less than 1 mm in an imaging volume 20 × 20 × 20 cm3 around scanner isocenter, but up to 4 mm at 17 cm lateral to isocenter. Bulk density assignment in the framework of MRI-only IMRT head and neck treatment planning is a feasible method with certain limitations. Bone and teeth account for the majority of density heterogeneity effects. While soft tissue is well visualized on MRI compared to CT, dental implants may not be visible on MRI and must be identified by other means and assigned appropriate density for accurate dose calculation. Far off-center geometrical distortion of the body contour near the shoulder region is a potential source of dose calculation inaccuracy.

Dataset Information

Automatic planning of head and neck treatment plans.

Publications

Automatic planning of head and neck treatment plans.

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