Technical Note: A fast inverse direct aperture optimization algorithm for volumetric-modulated arc therapy

Clinical VMAT machine parameter optimization for localized prostate cancer using deep reinforcement learning

BACKGROUND

Volumetric modulated arc therapy (VMAT) machine parameter optimization (MPO) remains computationally expensive and sensitive to input dose objectives creating challenges for manual and automatic planning. Reinforcement learning (RL) involves machine learning through extensive trial-and-error, demonstrating performance exceeding humans, and existing algorithms in several domains.

PURPOSE

To develop and evaluate an RL approach for VMAT MPO for localized prostate cancer to rapidly and automatically generate deliverable VMAT plans for a clinical linear accelerator (linac) and compare resultant dosimetry to clinical plans.

METHODS

We extended our previous RL approach to enable VMAT MPO of a 3D beam model for a clinical linac through a policy network. It accepts an input state describing the current control point and predicts continuous machine parameters for the next control point, which are used to update the input state, repeating until plan termination. RL training was conducted to minimize a dose-based cost function for prescription of 60 Gy in 20 fractions using CT scans and contours from 136 retrospective localized prostate cancer patients, 20 of which had existing plans used to initialize training. Data augmentation was employed to mitigate over-fitting, and parameter exploration was achieved using Gaussian perturbations. Following training, RL VMAT was applied to an independent cohort of 15 patients, and the resultant dosimetry was compared to clinical plans. We also combined the RL approach with our clinical treatment planning system (TPS) to automate final plan refinement, and creating the potential for manual review and edits as required for clinical use.

RESULTS

RL training was conducted for 5000 iterations, producing 40 000 plans during exploration. Mean ± SD execution time to produce deliverable VMAT plans in the test cohort was 3.3 ± 0.5 s which were automatically refined in the TPS taking an additional 77.4 ± 5.8 s. When normalized to provide equivalent target coverage, the RL+TPS plans provided a similar mean ± SD overall maximum dose of 63.2 ± 0.6 Gy and a lower mean rectum dose of 17.4 ± 7.4 compared to 63.9 ± 1.5 Gy (p = 0.061) and 21.0 ± 6.0 (p = 0.024) for the clinical plans.

CONCLUSIONS

An approach for VMAT MPO using RL for a clinical linac model was developed and applied to automatically generate deliverable plans for localized prostate cancer patients, and when combined with the clinical TPS shows potential to rapidly generate high-quality plans. The RL VMAT approach shows promise to discover advanced linac control policies through trial-and-error, and algorithm limitations and future directions are identified and discussed.

Determination of acceptable Hounsfield units uncertainties via a sensitivity analysis for an accurate dose calculation in the context of prostate MRI-only radiotherapy

Radiation therapy is moving from CT based to MRI guided planning, particularly for soft tissue anatomy. An important requirement of this new workflow is the generation of synthetic-CT (sCT) from MRI to enable treatment dose calculations. Automatic methods to determine the acceptable range of CT Hounsfield Unit (HU) uncertainties to avoid dose distribution errors is thus a key step toward safe MRI-only radiotherapy. This work has analysed the effects of controlled errors introduced in CT scans on the delivered radiation dose for prostate cancer patients. Spearman correlation coefficient has been computed, and a global sensitivity analysis performed following the Morris screening method. This allows the classification of different error factors according to their impact on the dose at the isocentre. sCT HU estimation errors in the bladder appeared to be the least influential factor, and sCT quality assessment should not only focus on organs surrounding the radiation target, as errors in other soft tissue may significantly impact the dose in the target volume. This methodology links dose and intensity-based metrics, and is the first step to define a threshold of acceptability of HU uncertainties for accurate dose planning.

Fully automated segmentally boosted VMAT

PURPOSE

Treatment planning for volumetric modulated arc therapy (VMAT) typically involves the use of multiple arcs to achieve sufficient intensity modulation. Alternatively, we can perform segment boosting to achieve similar intensity modulation while also reducing the number of control points used. Here, we propose the MetaPlanner Boosted VMAT (MPBV) approach, which generates boosted VMAT plans through a fully automated framework.

METHODS

The proposed MPBV approach is an open-source framework that consists of three main stages: meta-optimization of treatment plan hyperparameters, fast beam angle optimization on a coarse dose grid to select desirable segments for boosting, and final plan generation (i.e., constructing the boosted VMAT arc and performing optimization).

RESULTS

Performance for the MPBV approach is evaluated on 21 prostate cases and 6 head and neck cases using clinically relevant plan quality metrics (i.e., target coverage, dose conformity, dose homogeneity, and OAR sparing). As compared to two baseline methods with multiple arcs, MPBV maintains or improves dosimetric performance for the evaluated metrics while substantially reducing average estimated delivery times (from 2.6 to 2.1 min).

CONCLUSION

Our proposed MPBV approach provides an automated framework for producing high-quality VMAT plans that uses fewer control points and reduces delivery time as compared to traditional approaches with multiple arcs. MPBV applies automated treatment planning to segmentally boosted VMAT to address the beam utilization inefficiencies of traditional VMAT approaches that use multiple full arcs.

A study of different minimum segment area parameters on automatic IMRT plans for cervical cancer using Pinnacle3 9.10 TPS

Based on Pinnacle39.10 treatment planning system (TPS) automatic planning module, we investigated the effect of minimum segmentation area (MSA) parameters on Auto-Plan Intensity Modulated Radiotherapy (AP-IMRT) without affecting the dose distribution of the target and the Organ at Risk (OAR). The results provided the basis for the ideal MSA parameters in the design of AP-IMRT plan. Ten patients with cervical cancer in our hospital were selected randomly for AP-IMRT design. Each patient was devised with 10 AP-IMRT plans. The prescription dose of PTV was 50 Gy/25 fractions. The radiotherapy plans of all patients were adopted with 7 field-averaged fixed fields. The MSA was set to 4 cm2, 9 cm2, 14 cm2, 20 cm2, 25 cm2, 40 cm2, 50 cm2, 60 cm2, 80 cm2, and 100 cm2. Plan quality and delivery efficiency were evaluated based on dose-volume histograms (DVHs), control points, monitor units (MUs), dosimetric measurement verification results, and plan delivery time. Except for the small difference in monitor units, the number of segmentations and target dose coverage, there were no statistically significant differences between the other dosimetric parameters in the planning target volumes. With the increase of MSA, the total number of MUs in AP-IMRT decreased from (649 ± 32) MUs to (312 ± 26) MUs, and the total number of segmentations decreased from (69 ± 1) to (28 ± 3). There was no statistical significance in the dose distribution of AP-IMRT target area with the MSA of 4-50 cm2 (P > .05). There was no significant difference in OAR dose between AP-IMRT plans with different MSA (P > .05). The calculated gamma indices using the 3% /3 mm and 2%/2 mm criteria. Both of the gamma pass rate and DTA pass rate all ≥95% under the condition of MSA are greater than 4 cm2, and the difference was no statistically significant (P > .05). The plan delivery times decreased with increasing MSA (P < .05). When using Pinnacle3 9.10 TPS to design AP-IMRT plan for cervical cancer, the parameter of MSA can be increased appropriately. Increasing the MSA allows for improved plan delivery accuracy and efficiency without significantly affecting the AP-IMRT plan quality. The MSA in the range of 14 to 50 cm2 can obtain a more reasonable dose distribution in the target area while the dose of target area and OAR had no significant changes. It is important to improve the plan quality, delivery accuracy, and efficiency for cervical AP-IMRT radiation therapy.

A practical algorithm for VMAT optimization using column generation techniques

PURPOSE

As a challenging but important optimization problem, the inverse planning for volumetric modulated arc therapy (VMAT) has attracted much research attention. The column generation (CG) type method is so far one of the most effective solution schemes. However, it often relies on simplifications leading to significant gaps between the output and the actual feasible plan. This paper presents a novel column generation (NCG) approach to push the planning results substantially closer to practice.

METHODS

The proposed NCG algorithm is equipped with multiple new quality-enhancing and computation-facilitating modules as below (A) Flexible constraints are enabled on both dose rates and treatment time to adapt to machine capabilities as well as planner's preferences, respectively; (B) A cross-control-point intermediate aperture simulation is incorporated to better conform to the underlying physics; (C) New pricing and pruning subroutines are adopted to achieve better optimization outputs. To evaluate the effectiveness of this NCG, five VMAT plans, i.e., three prostate cases and two head-and-neck cases, were computed using proposed NCG. The planning results were compared with those yielded by a historical benchmark planning scheme.

RESULTS

The NCG generated plans of significantly better quality than the benchmark planning algorithm. For prostate cases, NCG plans satisfied all PTV criteria whereas CG plans failed on D10% criteria of PTVs for over 9 Gy or more on all cases. For head-and-neck cases, again, NCG plans satisfied all PTVs criteria while CG plans failed on D10% criteria of PTVs for over 3 Gy or more on all cases as well as the max dose criteria of both cord and brain stem for over 13 Gy on one case. Moreover, the pruning scheme was found to be effective in enhancing the optimization quality.

CONCLUSIONS

The proposed NCG inherits the computational advantages of the traditional CG, while capturing a more realistic characterization of the machine capability and underlying physics. The output solutions of the NCG are substantially closer to practical implementation. This article is protected by copyright. All rights reserved.

Meta-optimization for fully automated radiation therapy treatment planning

Objective. Radiation therapy treatment planning is a time-consuming process involving iterative adjustments of hyperparameters. To automate the treatment planning process, we propose a meta-optimization framework, called MetaPlanner (MP).Approach. Our MP algorithm automates planning by performing meta-optimization of treatment planning hyperparameters. The algorithm uses a derivative-free method (i.e. parallel Nelder-Mead simplex search) to search for weight configurations that minimize a meta-scoring function. Meta-scoring is performed by constructing a tier list of the relevant considerations (e.g. dose homogeneity, conformity, spillage, and OAR sparing) to mimic the clinical decision-making process. Additionally, we have made our source code publicly available via github.Main results. The proposed MP method is evaluated on two datasets (21 prostate cases and 6 head and neck cases) collected as part of clinical workflow. MP is applied to both IMRT and VMAT planning and compared to a baseline of manual VMAT plans. MP in both IMRT and VMAT scenarios has comparable or better performance than manual VMAT planning for all evaluated metrics.Significance. Our proposed MP provides a general framework for fully automated treatment planning that produces high quality treatment plans. Our MP method promises to substantially reduce the workload of treatment planners while maintaining or improving plan quality.