Different treatment planning protocols can lead to large differences in organ at risk sparing

Exploring the impact of metabolic imaging in head and neck cancer treatment

Background

Target volume delineation is performed with anatomical imaging for head and neck cancer. Molecular imaging allows the recognition of specific tumor regions. Its inclusion in the pathway could lead to changes in delineation and resultant treatment plans.

Methods

PRISMA methodology was adhered to when selecting the articles for analysis and only full articles were quality assessed.

Results

Seventeen articles were included. Gross tumor volume (GTV) primary, GTV nodal, and other target volumes were evaluated. Positron emission tomography/computerized tomography (PET/CT) produced smaller primary GTVs, although not with diffusion‐weighted imaging‐magnetic resonance imaging (DWI‐MRI) or PET/MRI. The impact of these image modalities on GTV nodal did not display any consistency. Additionally, there was considerable heterogeneity in metrics comparing delineations. Four studies included appraised the dosimetric impact of the changes in target volume delineation.

Conclusion

Quantifying the impact of molecular imaging is difficult, due to heterogeneity in reporting metrics in molecular imaging modalities and a paucity of detail regarding delineation method and guideline adherence.

Influence of Beam Angle on Normal Tissue Complication Probability of Knowledge-Based Head and Neck Cancer Proton Planning

Knowledge-based planning solutions have brought significant improvements in treatment planning. However, the performance of a proton-specific knowledge-based planning model in creating knowledge-based plans (KBPs) with beam angles differing from those used to train the model remains unexplored. We used a previously validated RapidPlanPT model and scripting to create nine KBPs, one with default and eight with altered beam angles, for 10 recent oropharynx cancer patients. The altered-angle plans were compared against the default-angle ones in terms of grade 2 dysphagia and xerostomia normal tissue complication probability (NTCP), mean doses of several organs at risk, and dose homogeneity index (HI). As KBP could be suboptimal, a proof of principle automatic iterative optimizer (AIO) was added with the aim of reducing the plan NTCP. There were no statistically significant differences in NTCP or HI between default- and altered-angle KBPs, and the altered-angle plans showed a <1% reduction in NTCP. AIO was able to reduce the sum of grade 2 NTCPs in 66/90 cases with mean a reduction of 3.5 ± 1.8%. While the altered-angle plans saw greater benefit from AIO, both default- and altered-angle plans could be improved, indicating that the KBP model alone was not completely optimal to achieve the lowest NTCP. Overall, the data showed that the model was robust to the various beam arrangements within the range described in this analysis.

Maintaining dosimetric quality when switching to a Monte Carlo dose engine for head and neck volumetric-modulated arc therapy planning

Head and neck cancers present challenges in radiation treatment planning due to the large number of critical structures near the target(s) and highly heterogeneous tissue composition. While Monte Carlo (MC) dose calculations currently offer the most accurate approximation of dose deposition in tissue, the switch to MC presents challenges in preserving the parameters of care. The differences in dose‐to‐tissue were widely discussed in the literature, but mostly in the context of recalculating the existing plans rather than reoptimizing with the MC dose engine. Also, the target dose homogeneity received less attention. We adhere to strict dose homogeneity objectives in clinical practice. In this study, we started with 21 clinical volumetric‐modulated arc therapy (VMAT) plans previously developed in Pinnacle treatment planning system. Those plans were recalculated “as is” with RayStation (RS) MC algorithm and then reoptimized in RS with both collapsed cone (CC) and MC algorithms. MC statistical uncertainty (0.3%) was selected carefully to balance the dose computation time (1–2 min) with the planning target volume (PTV) dose‐volume histogram (DVH) shape approaching that of a “noise‐free” calculation. When the hot spot in head and neck MC‐based treatment planning is defined as dose to 0.03 cc, it is exceedingly difficult to limit it to 105% of the prescription dose, as we were used to with the CC algorithm. The average hot spot after optimization and calculation with RS MC was statistically significantly higher compared to Pinnacle and RS CC algorithms by 1.2 and 1.0 %, respectively. The 95% confidence interval (CI) observed in this study suggests that in most cases a hot spot of ≤107% is achievable. Compared to the 95% CI for the previous clinical plans recalculated with RS MC “as is” (upper limit 108%), in real terms this result is at least as good or better than the historic plans.

Planning comparison of five automated treatment planning solutions for locally advanced head and neck cancer

Background

Automated treatment planning and/or optimization systems (ATPS) are in the process of broad clinical implementation aiming at reducing inter-planner variability, reducing the planning time allocated for the optimization process and improving plan quality. Five different ATPS used clinically were evaluated for advanced head and neck cancer (HNC).

Methods

Three radiation oncology departments compared 5 different ATPS: 1) Automatic Interactive Optimizer (AIO) in combination with RapidArc (in-house developed and Varian Medical Systems); 2) Auto-Planning (AP) (Philips Radiation Oncology Systems); 3) RapidPlan version 13.6 (RP1) with HNC model from University Hospital A (Varian Medical Systems, Palo Alto, USA); 4) RapidPlan version 13.7 (RP2) combined with scripting for automated setup of fields with HNC model from University Hospital B; 5) Raystation multicriteria optimization algorithm version 5 (RS) (Laboratories AB, Stockholm, Sweden). Eight randomly selected HNC cases from institution A and 8 from institution B were used. PTV coverage, mean and maximum dose to the organs at risk and effective planning time were compared. Ranking was done based on 3 Gy increments for the parallel organs.

Results

All planning systems achieved the hard dose constraints for the PTVs and serial organs for all patients. Overall, AP achieved the best ranking for the parallel organs followed by RS, AIO, RP2 and RP1. The oral cavity mean dose was the lowest for RS (31.3 ± 17.6 Gy), followed by AP (33.8 ± 17.8 Gy), RP1 (34.1 ± 16.7 Gy), AIO (36.1 ± 16.8 Gy) and RP2 (36.3 ± 16.2 Gy). The submandibular glands mean dose was 33.6 ± 10.8 Gy (AP), 35.2 ± 8.4 Gy (AIO), 35.5 ± 9.3 Gy (RP2), 36.9 ± 7.6 Gy (RS) and 38.2 ± 7.0 Gy (RP1). The average effective planning working time was substantially different between the five ATPS (in minutes): < 2 ± 1 for AIO and RP2, 5 ± 1 for AP, 15 ± 2 for RP1 and 340 ± 48 for RS, respectively.

Conclusions

All ATPS were able to achieve all planning DVH constraints and the effective working time was kept bellow 20 min for each ATPS except for RS. For the parallel organs, AP performed the best, although the differences were small.

The role of Imaging and Radiation Oncology Core for precision medicine era of clinical trial

Imaging and Radiation Oncology Core (IROC) services have been established for the quality assurance (QA) of imaging and radiotherapy (RT) for NCI's Clinical Trial Network (NCTN) for any trials that contain imaging or RT. The randomized clinical trial is the gold standard for evidence-based medicine. QA ensures data quality, preventing noise from inferior treatments obscuring clinical trial outcome. QA is also found to be cost-effective. IROC has made great progress in multi-institution standardization and is expected to lead QA standardization, QA science in imaging and RT and to advance quality data analysis with big data in the future. The QA in the era of precision medicine is of paramount importance, when individualized decision making may depend on the quality and accuracy of RT and imaging.

A method for a priori estimation of best feasible DVH for organs-at-risk: Validation for head and neck VMAT planning

PURPOSE

Despite improvements in optimization and automation algorithms, the quality of radiation treatment plans still varies dramatically. A tool that allows a priori estimation of the best possible sparing (Feasibility DVH, or FDVH) of an organ at risk (OAR) in high-energy photon planning may help reduce plan quality variability by deriving patient-specific OAR goals prior to optimization. Such a tool may be useful for (a) meaningfully evaluating patient-specific plan quality and (b) supplying best theoretically achievable DVH goals, thus pushing the solution toward automatic Pareto optimality. This work introduces such a tool and validates it for clinical Head and Neck (HN) datasets.

METHODS

To compute FDVH, first the targets are assigned uniform prescription doses, with no reference to any particular beam arrangement. A benchmark 3D dose built outside the targets is estimated using a series of energy-specific dose spread calculations reflecting observed properties of radiation distribution in media. For the patient, the calculation is performed on the heterogeneous dataset, taking into account the high- (penumbra driven) and low- (PDD and scatter-driven) gradient dose spreading. The former is driven mostly by target dose and surface shape, while the latter adds the dependence on target volume. This benchmark dose is used to produce the "best possible sparing" FDVH for an OAR, and based on it, progressively more easily achievable FDVH curves can be estimated. Validation was performed using test cylindrical geometries as well as 10 clinical HN datasets. For HN, VMAT plans were prepared with objectives of covering the primary and the secondary (bilateral elective neck) PTVs while addressing only one OAR at a time, with the goal of maximum sparing. The OARs were each parotid, the larynx, and the inferior pharyngeal constrictor. The difference in mean OAR doses was computed for the achieved vs. FDVHs, and the shapes of those DVHs were compared by means of the Dice similarity coefficient (DSC).

RESULTS

For all individually optimized HN OARs (N = 38), the average DSC between the planned DVHs and the FDVHs was 0.961 ± 0.018 (95% CI 0.955-0.967), with the corresponding average of mean OAR dose differences of 1.8 ± 5.8% (CI -0.1-3.6%). For realistic plans the achieved DVHs run no lower than the FDVHs, except when target coverage is compromised at the target/OAR interface.

CONCLUSIONS

For the validation of VMAT plans, the OAR DVHs optimized one-at-a-time were similar in shape to and bound on the low side by the FDVHs, within the confines of planner's ability to precisely cover the target(s) with the prescription dose(s). The method is best suited for the OARs close to the target. This approach is fundamentally different from "knowledge-based planning" because it is (a) independent of the treatment plan and prior experience, and (b) it approximates, from nearly first principles, the lowest possible boundary of the OAR DVH, but not necessarily its actual shape in the presence of competing OAR sparing and target dose homogeneity objectives.

Using a knowledge-based planning solution to select patients for proton therapy

BACKGROUND AND PURPOSE

Patient selection for proton therapy by comparing proton/photon treatment plans is time-consuming and prone to bias. RapidPlan™, a knowledge-based-planning solution, uses plan-libraries to model and predict organ-at-risk (OAR) dose-volume-histograms (DVHs). We investigated whether RapidPlan, utilizing an algorithm based only on photon beam characteristics, could generate proton DVH-predictions and whether these could correctly identify patients for proton therapy.

MATERIAL AND METHODS

Model_PROT and Model_PHOT comprised 30 head-and-neck cancer proton and photon plans, respectively. Proton and photon knowledge-based-plans (KBPs) were made for ten evaluation-patients. DVH-prediction accuracy was analyzed by comparing predicted-vs-achieved mean OAR doses. KBPs and manual plans were compared using salivary gland and swallowing muscle mean doses. For illustration, patients were selected for protons if predicted Model_PHOT mean dose minus predicted Model_PROT mean dose (ΔPrediction) for combined OARs was ≥6Gy, and benchmarked using achieved KBP doses.

RESULTS

Achieved and predicted Model_PROT/Model_PHOT mean dose R² was 0.95/0.98. Generally, achieved mean dose for Model_PHOT/Model_PROT KBPs was respectively lower/higher than predicted. Comparing Model_PROT/Model_PHOT KBPs with manual plans, salivary and swallowing mean doses increased/decreased by <2Gy, on average. ΔPrediction≥6Gy correctly selected 4 of 5 patients for protons.

CONCLUSIONS

Knowledge-based DVH-predictions can provide efficient, patient-specific selection for protons. A proton-specific RapidPlan-solution could improve results.

Inverse treatment planning for spinal robotic radiosurgery: an international multi-institutional benchmark trial

Stereotactic radiosurgery (SRS) is the accurate, conformal delivery of high‐dose radiation to well‐defined targets while minimizing normal structure doses via steep dose gradients. While inverse treatment planning (ITP) with computerized optimization algorithms are routine, many aspects of the planning process remain user‐dependent. We performed an international, multi‐institutional benchmark trial to study planning variability and to analyze preferable ITP practice for spinal robotic radiosurgery. 10 SRS treatment plans were generated for a complex‐shaped spinal metastasis with 21 Gy in 3 fractions and tight constraints for spinal cord (V14Gy<2 cc, V18Gy<0.1 cc) and target (coverage >95%). The resulting plans were rated on a scale from 1 to 4 (excellent‐poor) in five categories (constraint compliance, optimization goals, low‐dose regions, ITP complexity, and clinical acceptability) by a blinded review panel. Additionally, the plans were mathematically rated based on plan indices (critical structure and target doses, conformity, monitor units, normal tissue complication probability, and treatment time) and compared to the human rankings. The treatment plans and the reviewers' rankings varied substantially among the participating centers. The average mean overall rank was 2.4 (1.2‐4.0) and 8/10 plans were rated excellent in at least one category by at least one reviewer. The mathematical rankings agreed with the mean overall human rankings in 9/10 cases pointing toward the possibility for sole mathematical plan quality comparison. The final rankings revealed that a plan with a well‐balanced trade‐off among all planning objectives was preferred for treatment by most participants, reviewers, and the mathematical ranking system. Furthermore, this plan was generated with simple planning techniques. Our multi‐institutional planning study found wide variability in ITP approaches for spinal robotic radiosurgery. The participants', reviewers', and mathematical match on preferable treatment plans and ITP techniques indicate that agreement on treatment planning and plan quality can be reached for spinal robotic radiosurgery.

PACS number(s): 87.55.de

Evaluation of the optimal combinations of modulation factor and pitch for Helical TomoTherapy plans made with TomoEdge using Pareto optimal fronts

Background

Modulation factor (MF) and pitch have an impact on Helical TomoTherapy (HT) plan quality and HT users mostly use vendor-recommended settings. This study analyses the effect of these two parameters on both plan quality and treatment time for plans made with TomoEdge planning software by using the concept of Pareto optimal fronts.

Methods

More than 450 plans with different combinations of pitch [0.10–0.50] and MF [1.2–3.0] were produced. These HT plans, with a field width (FW) of 5 cm, were created for five head and neck patients and homogeneity index, conformity index, dose-near-maximum (D2), and dose-near-minimum (D98) were analysed for the planning target volumes, as well as the mean dose and D2 for most critical organs at risk. For every dose metric the median value will be plotted against treatment time. A Pareto-like method is used in the analysis which will show how pitch and MF influence both treatment time and plan quality.

Results

For small pitches (≤0.20), MF does not influence treatment time. The contrary is true for larger pitches (≥0.25) as lowering MF will both decrease treatment time and plan quality until maximum gantry speed is reached. At this moment, treatment time is saturated and only plan quality will further decrease.

Conclusion

The Pareto front analysis showed optimal combinations of pitch [0.23–0.45] and MF > 2.0 for a FW of 5 cm. Outside this range, plans will become less optimal. As the vendor-recommended settings fall within this range, the use of these settings is validated.

Automatic interactive optimization for volumetric modulated arc therapy planning

BACKGROUND

Intensity modulated radiotherapy treatment planning for sites with many different organs-at-risk (OAR) is complex and labor-intensive, making it hard to obtain consistent plan quality. With the aim of addressing this, we developed a program (automatic interactive optimizer, AIO) designed to automate the manual interactive process for the Eclipse treatment planning system. We describe AIO and present initial evaluation data.

METHODS

Our current institutional volumetric modulated arc therapy (RapidArc) planning approach for head and neck tumors places 3-4 adjustable OAR optimization objectives along the dose-volume histogram (DVH) curve that is displayed in the optimization window. AIO scans this window and uses color-coding to differentiate between the DVH-lines, allowing it to automatically adjust the location of the optimization objectives frequently and in a more consistent fashion. We compared RapidArc AIO plans (using 9 optimization objectives per OAR) with the clinical plans of 10 patients, and evaluated optimal AIO settings. AIO consistency was tested by replanning a single patient 5 times.

RESULTS

Average V95&V107 of the boost planning target volume (PTV) and V95 of the elective PTV differed by ≤0.5%, while average elective PTV V107 improved by 1.5%. Averaged over all patients, AIO reduced mean doses to individual salivary structures by 0.9-1.6Gy and provided mean dose reductions of 5.6Gy and 3.9Gy to the composite swallowing structures and oral cavity, respectively. Re-running AIO five times, resulted in the aforementioned parameters differing by less than 3%.

CONCLUSIONS

Using the same planning strategy as manually optimized head and neck plans, AIO can automate the interactive Eclipse treatment planning process and deliver dosimetric improvements over existing clinical plans.