A method to improve dose gradient for robotic radiosurgery

Selection criteria for circular collimator- vs. Multileaf collimator-based plans in robotic stereotactic radiotherapy for brain metastases and benign intracranial disease: Impact of target size, shape complexity, and proximity to at-risk organs

PURPOSE

This study aimed to determine the selection criteria for circular collimator (CC)- and multileaf collimator (MLC)-based stereotactic radiosurgery (SRS)/stereotactic radiotherapy (SRT) plans for brain metastases (BM) and benign intracranial disease (BID) in terms of geometric parameters using CyberKnife (CK).

METHODS

Forty-eight and eighty-five patients with BM and BID, respectively, were included. Two plans using CC and MLC were created for each case. Six dosimetric parameters and mathematical scores (MS) were extracted from each plan to assess plan quality. Two geometric parameters in BM-equivalent radius (rGTV) and sphericity index (SI) of the gross tumor volume-and three in BID-rGTV, SI, and the distance between the GTV and organ at risk (dOAR)-were calculated. Their effect on the superiority of CC- or MLC-based plans in terms of dosimetric parameters and MS was evaluated using multiple regression analysis.

RESULTS

The rGTV was associated with improved dosimetric parameters of MLC-based plans, especially the GTV conformity in BM and BID cases (β: 0.70 and 0.51) and the OAR sparing in BM cases (β: 0.82), where β represents the regression coefficient. Based on the MS, where the weights for the GTV coverage and OAR sparing were equal, the thresholds at which the MLC-based plans become comparable or superior to the CC-based plans in BM and BID were rGTV > 7.6 and >17.5 mm, respectively. Meanwhile, SI and dOAR were weakly correlated (β ≤ 0.30).

CONCLUSIONS

In SRS/SRT plans for BM and BID cases using CyberKnife, geometric parameters, especially rGTV, must be considered when selecting CC or MLC.

Robust and optimal dose distribution for brain metastases with robotic radiosurgery system: recipe for an inflection point

Purpose.This study aims to establish a robust dose prescription methodology in stereotactic radiosurgery (SRS) and stereotactic radiotherapy (SRT) for brain metastases, considering geometrical uncertainty and minimising dose exposure to the surrounding normal brain tissue.Methods and Materials.Treatment plans employing 40%-90% isodose lines (IDL) at 10% IDL intervals were created for variously sized brain metastases. The plans were constructed to deliver 21 Gy in SRS. Robustness of each plan was analysed using parameters such as the near minimum dose to the tumour, the near maximum dose to the normal brain, and the volume of normal brain irradiated above 14 Gy.Results.Plans prescribed at 60% IDL demonstrated the least variation in the near minimum dose to the tumour and the near maximum dose to the normal brain under conditions of minimal geometrical uncertainty relative to tumour radius. When the IDL-percentage prescription was below 60%, geometrical uncertainties led to increases in these doses. Conversely, they decreased with IDL-percentage prescriptions above 60%. The volume of normal brain irradiated above 14 Gy was lowest at 60% IDL, regardless of geometrical uncertainty.Conclusions.To enhance robustness against geometrical uncertainty and to better spare healthy brain tissue, a 60% IDL prescription is recommended in SRS and SRT for brain metastases using a robotic radiosurgery system.

Applying pytorch toolkit to plan optimization for circular cone based robotic radiotherapy

BACKGROUND

Robotic linac is ideally suited to deliver hypo-fractionated radiotherapy due to its compact head and flexible positioning. The non-coplanar treatment space improves the delivery versatility but the complexity also leads to prolonged optimization and treatment time.

METHODS

In this study, we attempted to use the deep learning (pytorch) framework for the plan optimization of circular cone based robotic radiotherapy. The optimization problem was topologized into a simple feedforward neural network, thus the treatment plan optimization was transformed into network training. With this transformation, the pytorch toolkit with high-efficiency automatic differentiation (AD) for gradient calculation was used as the optimization solver. To improve the treatment efficiency, plans with fewer nodes and beams were sought. The least absolute shrinkage and selection operator (lasso) and the group lasso were employed to address the "sparsity" issue.

RESULTS

The AD-S (AD sparse) approach was validated on 6 brain and 6 liver cancer cases and the results were compared with the commercial MultiPlan (MLP) system. It was found that the AD-S plans achieved rapid dose fall-off and satisfactory sparing of organs at risk (OARs). Treatment efficiency was improved by the reduction in the number of nodes (28%) and beams (18%), and monitor unit (MU, 24%), respectively. The computational time was shortened to 47.3 s on average.

CONCLUSIONS

In summary, this first attempt of applying deep learning framework to the robotic radiotherapy plan optimization is promising and has the potential to be used clinically.

Comparison of Two Cyberknife Planning Approaches for Multiple Brain Metastases

Purpose

To compare the delivery efficiency, plan quality, and planned treatment volume (PTV) and normal brain dosimetry between different Cyberknife planning approaches for multiple brain metastases (MBM), and to evaluate the effects of the number of collimators on the related parameters.

Methods

The study included 18 cases of MBM. The Cyberknife treatment plans were classified as Separate or Combined. For the Separate plan, each lesion was targeted by the collimator auto-selection method (Conformality 2/3 collimators). For the Combined plan, a PTV including all PTVs was targeted by the collimators. Monitor units (MUs), number of nodes and beams, estimated fraction treatment time (EFTT), new conformity index (nCI), dose gradient index (GI), homogeneity index (HI), PTV minimum/maximum dose (D_max/D_min), volume doses (D_2% and D_98%), maximum doses to lenses, optic nerves, and brainstem as well as normal brain 3, 6, 10, and 12 Gy (V_3Gy–V_12Gy) were compared.

Results

Compared to the Combined plan, the Separate plan had fewer nodes and beams, shorter EFTT, smaller PTV D_min, normal brain dose, and GI, and larger HI. The Separate plan with 2 collimators also had worse PTV coverage. In the Combined plan, more collimators increased beams, EFTT, GI, and normal brain dose but improved the PTV D_min. Among treatments based on the Separate approach, there were obvious differences between plans for most of the items except the nCI. Fewer collimators resulted in significantly reduced beams, EFTT, PTV D_98%, and normal brain dose with improved GI, although PTV D_min and MUs were decreased while HI was increased.

Conclusion

Both approaches met the requirements for SRS/HFSRT. We found that Separate plans improved treatment efficiency and normal tissue dosimetry.

Optimization of dose distributions of target volumes and organs at risk during stereotactic body radiation therapy for pancreatic cancer with dose-limiting auto-shells

Background

To identify optimization of dose distributions of target volumes and decrease of radiation doses to normal tissues during stereotactic body radiation therapy (SBRT) for pancreatic cancer with dose-limiting auto-shells.

Methods

With the same prescription dose, dose constraints of normal organs and calculation algorithm, treatment plans of each eligible patient were re-generated with 3 shells, 5 shells and 7 shells, respectively. The prescription isodose line and beam number of each patient in 3-shell, 5-shell and 7-shell plan remained the same. Hence, a triplet data set of dosimetric parameters was generated and analyzed.

Results

As the increase of shell number, the conformal index, volumes encompassed by 100% prescription isodose line and 30% prescription isodose line significantly decreased. The new conformal index was higher in 3-shell group than that in 5-shell and 7-shell group. A sharper dose fall-off was found in 5-shell and 7-shell group compared to 3-shell group. And the tumor coverage in 7-shell was better than that of 3-shell and 5-shell. Lower D_5cc of the intestine, D_10cc of the stomach, D_max of the spinal cord and smaller V₁₀ of the spleen was confirmed in 7-shell group compared to 3-shell group.

Conclusions

More conformal dose distributions of target volumes and lower radiation doses to normal organs could be performed with the increase of dose-limiting auto-shells, which may be more beneficial to potential critical organs without established dose constraints.

A singular value decomposition linear programming (SVDLP) optimization technique for circular cone based robotic radiotherapy

With robot-controlled linac positioning, robotic radiotherapy systems such as CyberKnife significantly increase freedom of radiation beam placement, but also impose more challenges on treatment plan optimization. The resampling mechanism in the vendor-supplied treatment planning system (MultiPlan) cannot fully explore the increased beam direction search space. Besides, a sparse treatment plan (using fewer beams) is desired to improve treatment efficiency. This study proposes a singular value decomposition linear programming (SVDLP) optimization technique for circular collimator based robotic radiotherapy. The SVDLP approach initializes the input beams by simulating the process of covering the entire target volume with equivalent beam tapers. The requirements on dosimetry distribution are modeled as hard and soft constraints, and the sparsity of the treatment plan is achieved by compressive sensing. The proposed linear programming (LP) model optimizes beam weights by minimizing the deviation of soft constraints subject to hard constraints, with a constraint on the l 1 norm of the beam weight. A singular value decomposition (SVD) based acceleration technique was developed for the LP model. Based on the degeneracy of the influence matrix, the model is first compressed into lower dimension for optimization, and then back-projected to reconstruct the beam weight. After beam weight optimization, the number of beams is reduced by removing the beams with low weight, and optimizing the weights of the remaining beams using the same model. This beam reduction technique is further validated by a mixed integer programming (MIP) model. The SVDLP approach was tested on a lung case. The results demonstrate that the SVD acceleration technique speeds up the optimization by a factor of 4.8. Furthermore, the beam reduction achieves a similar plan quality to the globally optimal plan obtained by the MIP model, but is one to two orders of magnitude faster. Furthermore, the SVDLP approach is tested and compared with MultiPlan on three clinical cases of varying complexities. In general, the plans generated by the SVDLP achieve steeper dose gradient, better conformity and less damage to normal tissues. In conclusion, the SVDLP approach effectively improves the quality of treatment plan due to the use of the complete beam search space. This challenging optimization problem with the complete beam search space is effectively handled by the proposed SVD acceleration.