Automatic VMAT planning for post-operative prostate cancer cases using particle swarm optimization: A proof of concept study

MRI-Guided Adaptive Radiation Therapy

Magnetic resonance imaging-guided radiation therapy (MRIgRT) has improved soft tissue contrast over computed tomography (CT) based image-guided RT. Superior visualization of the target and surrounding radiosensitive structures has the potential to improve oncological outcomes partly due to safer dose-escalation and adaptive planning. In this review, we highlight the workflow of adaptive MRIgRT planning, which includes simulation imaging, daily MRI, identifying isocenter shifts, contouring, plan optimization, quality control, and delivery. Increased utilization of MRIgRT will depend on addressing technical limitations of this technology, while addressing treatment efficacy, cost-effectiveness, and workflow training.

Full daily re-optimization improves plan quality during online adaptive radiotherapy

Background and purpose

Daily online treatment plan adaptation requires a fast workflow and planning process. Current online planning consists of adaptation of a predefined reference plan, which might be suboptimal in cases of large anatomic changes. The aim of this study was to investigate plan quality differences between the current online re-planning approach and a complete re-optimization.

Material and methods

Magnetic resonance linear accelerator reference plans for ten prostate cancer patients were automatically generated using particle swarm optimization (PSO). Adapted plans were created for each fraction using (1) the current re-planning approach and (2) full PSO re-optimization and evaluated overall compliance with institutional dose-volume criteria compared to (3) clinically delivered fractions. Relative volume differences between reference and daily anatomy were assessed for planning target volumes (PTV60, PTV57.6), rectum and bladder and correlated with dose-volume results.

Results

The PSO approach showed significantly higher adherence to dose-volume criteria than the reference approach and clinical fractions (p < 0.001). In 74 % of PSO plans at most one criterion failed compared to 56 % in the reference approach and 41 % in clinical plans. A fair correlation between PTV60 D98% and relative bladder volume change was observed for the reference approach. Bladder volume reductions larger than 50 % compared to the reference plan recurrently decreased PTV60 D98% below 56 Gy.

Conclusion

Complete re-optimization maintained target coverage and organs at risk sparing even after large anatomic variations. Re-planning based on daily magnetic resonance imaging was sufficient for small variations, while large variations led to decreasing target coverage and organ-at-risk sparing.

Tree-based exploration of the optimization objectives for automatic cervical cancer IMRT treatment planning

OBJECTIVE

To develop and evaluate a practical automatic treatment planning method for intensity-modulated radiation therapy (IMRT) in cervical cancer cases.

METHODS

A novel algorithm named as Optimization Objectives Tree Search Algorithm (OOTSA) was proposed to emulate the planning optimization process and achieve a progressively improving IMRT plan, based on the Eclipse Scripting Application Programming Interface (ESAPI). 30 previously treated cervical cancer cases were selected from the clinical database and comparison was made between the OOTSA-generated plans and clinical treated plans and RapidPlan-based (RP) plans.

RESULTS

In clinical evaluation, compared with plan scores of the clinical plans and the RP plans, 22 and 26 of the OOTSA plans were considered as clinically improved in terms of plan quality, respectively. The average conformity index (CI) for the PTV in the OOTSA plans was 0.86 ± 0.01 (mean ± 1 standard deviation), better than those in the RP plans (0.83 ± 0.02) and the clinical plans (0.71 ± 0.11). Compared with the clinical plans, the mean doses of femoral head, rectum, spinal cord and right kidney in the OOTSA plans were reduced by 2.34 ± 2.87 Gy, 1.67 ± 2.10 Gy, 4.12 ± 6.44 Gy and 1.15 ± 2.67 Gy. Compared with the RP plans, the mean doses of femoral head, spinal cord, right kidney and small intestine in the OOTSA plans were reduced by 3.31 ± 1.55 Gy, 4.25 ± 3.69 Gy, 1.54 ± 2.23 Gy and 3.33 ± 1.91 Gy, respectively. In the OOTSA plans, the mean dose of bladder was slightly increased, with 2.33 ± 2.55 Gy (versus clinical plans) and 1.37 ± 1.74 Gy (vs RP plans). The average elapsed time of OOTSA and clinical planning were 59.2 ± 3.47 min and 76.53 ± 5.19 min.

CONCLUSION

The plans created by OOTSA have been shown marginally better than the manual plans, especially in preserving OARs. In addition, the time of automatic treatment planning has shown a reduction compared to a manual planning process, and the variation of plan quality was greatly reduced. Although improvement on the algorithm is warranted, this proof-of-concept study has demonstrated that the proposed approach can be a practical solution for automatic planning.

ADVANCES IN KNOWLEDGE

The proposed method is novel in the emulation strategy of the physicists' iterative operation during the planning process. Based on the existing optimizers, this method can be a simple yet effective solution for automated IMRT treatment planning.

First experience of autonomous, un-supervised treatment planning integrated in adaptive MR-guided radiotherapy and delivered to a patient with prostate cancer

BACKGROUND AND PURPOSE

Currently clinical radiotherapy (RT) planning consists of a multi-step routine procedure requiring human interaction which often results in a time-consuming and fragmented process with limited robustness. Here we present an autonomous un-supervised treatment planning approach, integrated as basis for online adaptive magnetic resonance guided RT (MRgRT), which was delivered to a prostate cancer patient as a first-in-human experience.

MATERIALS AND METHODS

For an intermediate risk prostate cancer patient OARs and targets were automatically segmented using a deep learning-based software and logical volume operators. A baseline plan for the 1.5 T MR-Linac (20x3 Gy) was automatically generated using particle swarm optimization (PSO) without any human interaction. Plan quality was evaluated by predefined dosimetric criteria including appropriate tolerances. Online plan adaptation during clinical MRgRT was defined as first checkpoint for human interaction.

RESULTS

OARs and targets were successfully segmented (3 min) and used for automatic plan optimization (300 min). The autonomous generated plan satisfied 12/16 dosimetric criteria, however all remained within tolerance. Without prior human validation, this baseline plan was successfully used during online MRgRT plan adaptation, where 14/16 criteria were fulfilled. As postulated, human interaction was necessary only during plan adaptation.

CONCLUSION

Autonomous, un-supervised data preparation and treatment planning was first-in-human shown to be feasible for adaptive MRgRT and successfully applied. The checkpoint for first human intervention was at the time of online MRgRT plan adaptation. Autonomous planning reduced the time delay between simulation and start of RT and may thus allow for real-time MRgRT applications in the future.

Full automation of spinal stereotactic radiosurgery and stereotactic body radiation therapy treatment planning using Varian Eclipse scripting

The purpose of this feasibility study is to develop a fully automated procedure capable of generating treatment plans with multiple fractionation schemes to improve speed, robustness, and standardization of plan quality. A fully automated script was implemented for spinal stereotactic radiosurgery/stereotactic body radiation therapy (SRS/SBRT) plan generation using Eclipse v15.6 API. The script interface allows multiple dose/fractionation plan requests, planning target volume (PTV) expansions, as well as information regarding distance/overlap between spinal cord and targets to drive decision‐making. For each requested plan, the script creates the course, plans, field arrangements, and automatically optimizes and calculates dose. The script was retrospectively applied to ten computed tomography (CT) scans of previous cervical, thoracic, and lumbar spine SBRT patients. Three plans were generated for each patient — simultaneous integrated boost (SIB) 1800/1600 cGy to gross tumor volume (GTV)/PTV in one fraction; SIB 2700/2100 cGy to GTV/PTV in three fractions; and 3000 cGy to PTV in five fractions. Plan complexity and deliverability patient‐specific quality assurance (QA) was performed using ArcCHECK with an Exradin A16 chamber inserted. Dose objectives were met for all organs at risk (OARs) for each treatment plan. Median target coverage was GTV V100% = 87.3%, clinical target volume (CTV) V100% = 95.7% and PTV V100% = 88.0% for single fraction plans; GTV V100% = 95.6, CTV V100% = 99.6% and PTV V100% = 97.2% for three fraction plans; and GTV V100% = 99.6%, CTV V100% = 99.1% and PTV V100% = 97.2% for five fraction plans. All plans (n = 30) passed patient‐specific QA (>90%) at 2%/2 mm global gamma. A16 chamber dose measured at isocenter agreed with planned dose within 3% for all cases. Automatic planning for spine SRS/SBRT through scripting increases efficiency, standardizes plan quality and approach, and provides a tool for target coverage comparison of different fractionation schemes without the need for additional resources.