RapidPlan development of VMAT plans for cervical cancer patients in low- and middle-income countries

Tinoco, Marisol; Waga, Erika; Tran, Kevin; Vo, Hieu; Baker, Jackie; Hunter, R.; Peterson, Christine B.; Taku, Nicolette; Court, Laurence Edward

doi:10.1016/j.meddos.2019.10.002

Cited by 19 publications

(19 citation statements)

References 10 publications

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“…Previous publications evaluating RP models showed that KBP plans exceeded the clinical accepted plan quality at various anatomical sites [7,16,30,31]. Although the models in these studies were registered with multiple original OAR structures, the RP performance of our models with only 2-5 pseudo-structures registered was found to be similar to that reported in previous publications.…”

Section: Discussionsupporting

confidence: 83%

Knowledge-based Planning using Pseudo-structures for Volumetric Modulated arc Therapy (VMAT) of Postoperative Uterine Cervical Cancer: A Multi-Institutional Study

Kamima

Ueda

Fukunaga

et al. 2020

Preprint

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Background: The aim of this study was to investigate the performance of the RapidPlan knowledge-based treatment planning system using models including registered pseudo-structures, and to determine how many structures are required for automatic optimization of volumetric modulated arc therapy (VMAT) for postoperative uterine cervical cancer. Methods: Pseudo-structures were retrospectively contoured for patients who had completed treatment at one of five institutions. For 22 patients, RPs were generated with a single optimization for models with two (RP_2), four (RP_4), or five (RP_5) registered structures, and the dosimetric parameters of these models were compared with a clinical plan with several optimizations. The total times for pseudo-structure creation and optimization were also measured.Results: Most dosimetric parameters showed no major differences between each RP. In particular, the rectum Dmax, V50Gy, and V40Gy with RP_2, RP_4, and RP_5 were not significantly different, and were lower than those of the clinical plan. In addition, the average proportions of plans achieving acceptable criteria for all dosimetric parameters were 98%, 99%, 98%, and 98% for the clinical plan, RP_2, RP_4, and RP_5, respectively. The average times for the creation and optimization of pseudo-structures were 105, 17, 21, and 29 minutes, for the clinical plan, RP_2, RP_4, and RP_5, respectively. Conclusions: The RapidPlan model with two registered pseudo-structures could generate clinically acceptable plans while saving time. This modeling approach using pseudo-structures could possibility be used for the VMAT planning process.

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Section: Discussionsupporting

confidence: 83%

Knowledge-based Planning using Pseudo-structures for Volumetric Modulated arc Therapy (VMAT) of Postoperative Uterine Cervical Cancer: A Multi-Institutional Study

Kamima

Ueda

Fukunaga

et al. 2020

Preprint

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“…1 One of the statistical modeling methods is RapidPlan (Varian Medical Systems, Palo Alto, CA), a commercial knowledge-based planning solution derived from earlier work, which uses a model based on a library of previous plans. [2][3][4][5][6][7][8][9][10][11] The model can be used to predict a range of achievable organ at risk (OAR) dose-volume histograms (DVHs) for new patients. It is possible to share models among different clinical institutes in a cooperative framework.…”

Section: Introductionmentioning

confidence: 99%

“…Methods for knowledge‐based planning can be further divided into two major categories: atlas‐based methods and statistical modeling (including machine learning methods) 1 . One of the statistical modeling methods is RapidPlan (Varian Medical Systems, Palo Alto, CA), a commercial knowledge‐based planning solution derived from earlier work, which uses a model based on a library of previous plans 2–11 . The model can be used to predict a range of achievable organ at risk (OAR) dose‐volume histograms (DVHs) for new patients.…”

Section: Introductionmentioning

confidence: 99%

Reducing variability among treatment machines using knowledge‐based planning for head and neck, pancreatic, and rectal cancer

Hirashima

Nakamura

Mukumoto

et al. 2021

J Applied Clin Med Phys

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Purpose: This study aimed to assess dosimetric indices of RapidPlan model-based plans for different energies (6, 8, 10, and 15 MV; 6-and 10-MV flattening filterfree), multileaf collimator (MLC) types (Millennium 120, High Definition 120, duallayer MLC), and disease sites (head and neck, pancreatic, and rectal cancer) and compare these parameters with those of clinical plans. Methods: RapidPlan models in the Eclipse version 15.6 were used with the data of 28, 42, and 20 patients with head and neck, pancreatic, and rectal cancer, respectively. RapidPlan models of head and neck, pancreatic, and rectal cancer were created for TrueBeam STx (High Definition 120) with 6 MV, TrueBeam STx with 10-MV flattening filter-free, and Clinac iX (Millennium 120) with 15 MV, respectively. The models were used to create volumetric-modulated arc therapy plans for a 10patient test dataset using all energy and MLC types at all disease sites. The Holm test was used to compare multiple dosimetric indices in different treatment machines and energy types. Results: The dosimetric indices for planning target volume and organs at risk in RapidPlan model-based plans were comparable to those in the clinical plan. Furthermore, no dose difference was observed among the RapidPlan models. The variability among RapidPlan models was consistent regardless of the treatment machines, MLC types, and energy. Conclusions: Dosimetric indices of RapidPlan model-based plans appear to be comparable to the ones based on clinical plans regardless of energies, MLC types, and disease sites. The results suggest that the RapidPlan model can generate treatment plans independent of the type of treatment machine.

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“…VMAT technology is widely used for the cervical cancer radiotherapy [1][2][3]. These studies on the application have shown that VMAT technology can be used for cervical cancer radiotherapy, and compared with High Dose Rate brachytherapy, VMAT plan achieves significant dose reduction of rectum, bladder and sigmoid, as well as superior homogeneous target coverage compared to brachytherapy plan [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Study on the ability of 3D gamma analysis and bio-mathematical model in detecting dose changes caused by dose-calculation-grid-size (DCGS)

Bai

Zhu

et al. 2020

Radiat Oncol

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Objective: To explore the efficacy and sensitivity of 3D gamma analysis and bio-mathematical model for cervical cancer in detecting dose changes caused by dose-calculation-grid-size (DCGS). Methods: 17 patients' plans for cervical cancer were enrolled (Pinnacle TPS, VMAT), and the DCGS was changed from 2.0 mm to 5.0 mm to calculate the planned dose respectively. The dose distribution calculated by DCGS = 2.0 mm as the "reference" data set (RDS), the dose distribution calculated by the rest DCGS as the"measurement"data set (MDS), the 3D gamma passing rates and the (N) TCPs of the all structures under different DCGS were obtained, and then analyze the ability of 3D gamma analysis and (N) TCP model in detecting dose changes and what factors affect this ability. Results: The effect of DCGS on planned dose was obvious. When the gamma standard was 1.0 mm, 1.0 and 10.0%, the difference of the results of the DCGS on dose-effect could be detected by 3D gamma analysis (all p value < 0.05). With the decline of the standard, 3D gamma analysis' ability to detect this difference shows weaker. When the standard was 1.0 mm, 3.0 and 10.0%, the p value of > 0.05 accounted for the majority. With DCGS = 2.0 mm being RDS, Δgammapassing-rate presented the same trend with Δ(N) TCPs of all structures except for the femurs only when the 1.0 mm, 1.0 and 10.0% standards were adopted for the 3D gamma analysis. Conclusions: The 3D gamma analysis and bio-mathematical model can be used to analyze the effect of DCGS on the planned dose. For comparison, the former's detection ability has a lot to do with the designed standard, and the latter's capability is related to the parameters and calculated accuracy instrinsically.

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RapidPlan development of VMAT plans for cervical cancer patients in low- and middle-income countries

Cited by 19 publications

References 10 publications

Knowledge-based Planning using Pseudo-structures for Volumetric Modulated arc Therapy (VMAT) of Postoperative Uterine Cervical Cancer: A Multi-Institutional Study

Knowledge-based Planning using Pseudo-structures for Volumetric Modulated arc Therapy (VMAT) of Postoperative Uterine Cervical Cancer: A Multi-Institutional Study

Reducing variability among treatment machines using knowledge‐based planning for head and neck, pancreatic, and rectal cancer

Study on the ability of 3D gamma analysis and bio-mathematical model in detecting dose changes caused by dose-calculation-grid-size (DCGS)

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