Automatic genetic planning for volumetric modulated arc therapy: A large multi-centre validation for prostate cancer

Fiandra, C.; Rossi, L.; Alparone, A.; Zara, Stefania; Vecchi, C.; Sardo, A.; Bartoncini, Sara; Loi, G.; Pisani, Carla; Gino, Eva; Redda, Maria Grazia Ruo; Deotto, Gian Marco; Tini, Paolo; Comi, S.; Zerini, Dario; Ametrano, Gianluca; Borzillo, Valentina; Strigari, Lidia; Strolin, Silvia; Savini, A.; Romeo, Antonino; Reccanello, Sonia; Rumeileh, Imad Abu; Ciscognetti, Nunzia; Guerrisi, Flavia; Balestra, Gabriella; Ricardi, Umberto; Heijmen, B.

doi:10.1016/j.radonc.2020.04.020

Cited by 13 publications

(10 citation statements)

References 27 publications

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“…Even with the use of rectal spacer, the role of treatment planning is essential in delivering prescription dose to the prostate while meeting normal tissue tolerances. Advances in treatment planning over the past decade have enabled the radiation oncology community to reap the benefits of faster optimization and calculation times, automated planning processes, and higher accuracy in dose calculation 9–12 . Techniques that have long been researched have made their way into clinical implementation by way of commercial software.…”

Section: Introductionmentioning

confidence: 99%

“…Advances in treatment planning over the past decade have enabled the radiation oncology community to reap the benefits of faster optimization and calculation times, automated planning processes, and higher accuracy in dose calculation. 9 , 10 , 11 , 12 Techniques that have long been researched have made their way into clinical implementation by way of commercial software. Knowledge‐based planning (KBP) and multicriteria optimization (MCO) are two recent advancements in inverse planning that have become commercially available through multiple vendors.…”

Section: Introductionmentioning

confidence: 99%

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The effect of integrating knowledge‐based planning with multicriteria optimization in treatment planning for prostate SBRT

Jayarathna

Chen

et al. 2023

J Applied Clin Med Phys

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Knowledge‐based planning (KBP) and multicriteria optimization (MCO) are two powerful tools to assist treatment planners in achieving optimal target coverage and organ‐at‐risk (OAR) sparing. The purpose of this work is to investigate if integrating MCO with conventional KBP can further improve treatment plan quality for prostate cancer stereotactic body radiation therapy (SBRT). A two‐phase study was designed to investigate the impact of MCO and KBP in prostate SBRT treatment planning. The first phase involved the creation of a KBP model based on thirty clinical SBRT plans, generated by manual optimization (KBP_M). A ten‐patient validation cohort was used to compare manual, MCO, and KBP_M optimization techniques. The next phase involved replanning the original model cohort with additional tradeoff optimization via MCO to create a second model, KBP_MCO. Plans were then generated using linear integration (KBP_M+MCO), non‐linear integration (KBP_MCO), and a combination of integration methods (KBP_MCO+MCO). All plans were analyzed for planning target volume (PTV) coverage, OAR constraints, and plan quality metrics. Comparisons were generated to evaluate plan and model quality. Phase 1 highlighted the necessity of KBP and MCO in treatment planning, as both optimization methods improved plan quality metrics (Conformity and Heterogeneity Indices) and reduced mean rectal dose by 2 Gy, as compared to manual planning. Integrating MCO with KBP did not further improve plan quality, as little significance was seen over KBP or MCO alone. Principal component score (PCS) fitting showed KBP_MCO improved bladder and rectum estimated and modeled dose correlation by 5% and 22%, respectively; however, model improvements did not significantly impact plan quality. KBP and MCO have shown to reduce OAR dose while maintaining desired PTV coverage in this study. Further integration of KBP and MCO did not show marked improvements in treatment plan quality while requiring increased time in model generation and optimization time.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The effect of integrating knowledge‐based planning with multicriteria optimization in treatment planning for prostate SBRT

Jayarathna

Chen

et al. 2023

J Applied Clin Med Phys

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“…A dedicated application for breast autoplanning was also implemented in a commercial TPS [20,21]. Scripting in commercial TPS has also resulted in several clinical autoplanning applications [22,23]. Autoplanning has also been commercially implemented for so-called a posteriori multi-criterial optimization (MCO): automated generation of a Pareto front, followed by final plan selection by a planner using Pareto navigation [24][25][26][27].…”

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confidence: 99%

Pre-clinical validation of a novel system for fully-automated treatment planning

Bijman¹,

Sharfo²,

Rossi³

et al. 2021

Radiotherapy and Oncology

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Introduction: Many approaches for automated treatment planning (autoplanning) have been proposed and investigated. Autoplanning can enhance plan quality compared to 'manual' trial-and-error planning, and decrease routine planning workload. A few approaches have been implemented in commercial treatment planning systems (TPSs). We performed a pre-clinical validation of a new system ('NovelATP') that is based on fully-automated multi-criterial optimization (MCO). The aim of NovelATP is to automatically generate for each patient a single high-quality, Pareto-optimal plan without manual Pareto navigation. Material and methods: Validation was performed by generating VMAT/IMRT plans for conventional treatment of prostate cancer (101 pts), prostate SBRT (20 pts), bilateral head-and-neck cancer (50 pts) and rectal cancer treated at an MR-Linac (23 pts). NovelATP autoplans were compared to plans that were generated with our in-house autoplanning system. In many previous validation studies, the latter system consistently showed enhanced plan quality when compared to manual planning. Results: Dosimetrical differences between NovelATP and benchmark plans were on average small and presumably not clinically relevant, pointing at high NovelATP dosimetric plan quality. MUs were 11-19% higher with NovelATP. NovelATP delivery times were up to 12% longer. Overall, there was a slight disadvantage for NovelATP regarding gamma analyses. Calculation times for NovelATP plans were between 29 and 151 min with no overall differences with the benchmark plans. Conclusion:The new autoplanning system was able to produce high-quality plans for four highly different planning protocols/treatment sites with a total of 194 patients investigated.

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“…The accessibility of the merging platform opens the opportunities of the super-model to a wide set of users and allows centres with minimal experience or lack of data to use high-quality and well-tested super-models. Multi-centre validation of models has been common practice for a while [18] , [19] , [20] , and has shown that combining experience between centres can improve model performance and standardisation of radiotherapy [10] , [11] , [12] . Therefore, the ease with which multiple centres can contribute to and influence models plays an important role in improving radiotherapy treatment.…”

Section: Discussionmentioning

confidence: 99%

Using multi-centre data to train and validate a knowledge-based model for planning radiotherapy of the head and neck

Frizzelle

Pediaditaki

Thomas

et al. 2022

Physics and Imaging in Radiation Oncology

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Background and purpose: Knowledge-based radiotherapy planning models have been shown to reduce healthy tissue dose and optimisation times, with larger training databases delivering greater robustness. We propose a method of combining knowledge-based models from multiple centres to create a 'super-model' using their collective patient libraries, thereby increasing the breadth of training knowledge. Materials and methods: A head and neck super-model containing 207 patient datasets was created by merging the data libraries of three centres. Validation was performed on 30 independent datasets during which optimiser parameters were tuned to deliver the optimal set of model template objectives. The super-model was tested on a further 40 unseen patients from four radiotherapy centres, including one centre external to the training process. The generated plans were assessed using established plan evaluation criteria. Results: The super-model generated plans that surpassed the dose objectives for all patients with single optimisations in an average time of 10 min. Healthy tissue sparing was significantly improved over manual planning, with dose reductions to parotid of 4.7 ± 2.1 Gy, spinal cord of 3.3 ± 0.9 Gy and brainstem of 2.9 ± 1.7 Gy. Target coverage met the established constraints but was marginally reduced compared with clinical plans. Conclusions: Three centres successfully merged patient libraries to create a super-model capable of generating plans that met plan evaluation criteria for head and neck patients with improvements in healthy tissue sparing. The findings indicate that the super-model could improve head and neck planning quality, efficiency and consistency across radiotherapy centres.

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Automatic genetic planning for volumetric modulated arc therapy: A large multi-centre validation for prostate cancer

Cited by 13 publications

References 27 publications

The effect of integrating knowledge‐based planning with multicriteria optimization in treatment planning for prostate SBRT

The effect of integrating knowledge‐based planning with multicriteria optimization in treatment planning for prostate SBRT

Pre-clinical validation of a novel system for fully-automated treatment planning

Using multi-centre data to train and validate a knowledge-based model for planning radiotherapy of the head and neck

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