Dosimetric evaluation with knowledge-based planning created at different periods in volumetric-modulated arc therapy for prostate cancer: a multi-institution study

Monzen, Hajime; Tamura, Mikoto; Ueda, Yoshihiro; Fukunaga, Junichi; Kamima, Tatsuya; Muraki, Yuta; Kubo, Kazuki; Nakamatsu, Kiyoshi

doi:10.1007/s12194-020-00585-0

Cited by 12 publications

(15 citation statements)

References 28 publications

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“…It is believed that the reasons why the homogeneity of the PTV‐R was improved in the RP1 model was to prioritize the manual upper and lower objectives for PTV‐R in the optimization process, and it caused lower sparing of the OARs due to the trade‐offs between the target and OARs compared with RP2 and RP3 models. The dose–volume parameters of the RP tend to be better when the registered plans are better, 18,26 and our previous report revealed that the RP’s feedback led to refinement of the quality of the registered plans by multi‐institution study 27 . However, the sparing of OARs by the model update might have an upper bound for improvement, as shown by comparison of the dose–volume parameters between RP2 and RP3 in Table 4.…”

Section: Discussionmentioning

confidence: 89%

“…Therefore, compared with the RP1 and RP2 models, the RP3 model could make better estimation of the DVH in the model for plans that were not part of the training set. A function of the RP as a training tool for the planners 27 and more strict dose constraints F I G U R E 4 Histograms of Cook's distance for the regression line in the rectal wall and bladder wall in the RP1 (a, b), RP2 (c, d), and RP3 models (e, f)…”

Section: Discussionmentioning

confidence: 99%

“…The dose–volume parameters of the RP tend to be better when the registered plans are better, 18 , 26 and our previous report revealed that the RP’s feedback led to refinement of the quality of the registered plans by multi‐institution study. 27 However, the sparing of OARs by the model update might have an upper bound for improvement, as shown by comparison of the dose–volume parameters between RP2 and RP3 in Table 4 . The dose reduction of the OARs is difficult where the overlap volume between the target and OARs is large, as described by Moore et al 38 The anatomical characteristics of the training plans in the RP3 model, such as the large overlap volume between the target and rectum compared with the RP2 model in Table 2 , may make it difficult for the RP3 model to reduce the dose of the OARs, as shown in Table 3 .…”

Section: Discussionmentioning

confidence: 99%

“…[12][13][14][15][16][17][18][19][20][21][22][23][24][25] The RP model performance, such as sparing of OARs, depends on the training plans included in the model library; therefore, the registration of better plans is useful to update the model and enhance its performance. 26,27 Wang et al investigated the performance of a RP model updated using a closed-loop technique and showed significant improvement in the sparing of OARs from first to second RP models. 28 However, the problem of over-fitting is of concern with this closedloop model updating technique.…”

Section: Introductionmentioning

confidence: 99%

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An updating approach for knowledge‐based planning models to improve plan quality and variability in volumetric‐modulated arc therapy for prostate cancer

Nakamura

Okuhata

Tamura

et al. 2021

J Applied Clin Med Phys

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The purpose of this study was to compare the dose-volume parameters and regression scatter plots of the iteratively improved RapidPlan (RP) models, specific knowledge-based planning (KBP) models, in volumetric-modulated arc therapy (VMAT) for prostate cancer over three periods.Methods: A RP1 model was created from 47 clinical intensity-modulated radiation therapy (IMRT)/VMAT plans. A RP2 model was created to exceed dosimetric goals which set as the mean values +1SD of the dose-volume parameters of RP1 (50 consecutive new clinical VMAT plans). A RP3 model was created with more strict dose constraints for organs at risks (OARs) than RP1 and RP2 models (50 consecutive anew clinical VMAT plans). Each RP model was validated against 30 validation plans (RP1, RP2, and RP3) that were not used for model configuration, and the dose-volume parameters were compared. The Cook's distances of regression scatterplots of each model were also evaluated.Results: Significant differences (p < 0.05) between RP1 and RP2 were found in D mean (101.5% vs. 101.9%), homogeneity index (3.90 vs. 4.44), 95% isodose conformity index (1.22 vs. 1.20) for the target, V 40Gy (47.3% vs. 45.7%), V 60Gy (27.9% vs. 27.1%), V 70Gy (16.4% vs. 15.2%), and V 78Gy (0.4% vs. 0.2%) for the rectal wall, and V 40Gy (43.8% vs. 41.8%) and V 70Gy (21.3% vs. 20.5%) for the bladder wall, whereas only V 70Gy (15.2% vs. 15.8%) of the rectal wall differed significantly between RP2 and RP3. The proportions of cases with a Cook's distance of <1.0 (RP1, RP2, and RP3 models) were 55%, 78%, and 84% for the rectal wall, and 77%, 68%, and 76% for the bladder wall, respectively. Conclusions:The iteratively improved RP models, reflecting the clear dosimetric goals based on the RP feedback (dose-volume parameters) and more strict dose constraints for the OARs, generated superior dose-volume parameters How to cite this article: Nakamura K, Okuhata K, Tamura M, et al. An updating approach for knowledge-based planning models to improve plan quality and variability in volumetricmodulated arc therapy for prostate cancer.

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Section: Discussionmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An updating approach for knowledge‐based planning models to improve plan quality and variability in volumetric‐modulated arc therapy for prostate cancer

Nakamura

Okuhata

Tamura

et al. 2021

J Applied Clin Med Phys

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“…The utility of KBP with IMRT/VMAT has been reported at various treatment regions (e.g., head and neck, breast, prostate, and hepatocellular cancer). [ 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 ] In SBRT of lung cancer, the doses to the lungs and heart can be reduced more in KBPs than CLPs. [ 29 ] However, some reports have trained KBP models using 9 or 10 structures.…”

Section: Introductionmentioning

confidence: 99%

Dosimetric Evaluation of Simplified Knowledge-Based Plan with an Extensive Stepping Validation Approach in Volumetric-Modulated Arc Therapy-Stereotactic Body Radiotherapy for Lung Cancer

Wada

Monzen

Tamura

et al. 2021

Journal of Medical Physics

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Purpose: We investigated the performance of the simplified knowledge-based plans (KBPs) in stereotactic body radiotherapy (SBRT) with volumetric-modulated arc therapy (VMAT) for lung cancer. Materials and Methods: For 50 cases who underwent SBRT, only three structures were registered into knowledge-based model: total lung, spinal cord, and planning target volume. We performed single auto-optimization on VMAT plans in two steps: 19 cases used for the model training (closed-loop validation) and 16 new cases outside of training set (open-loop validation) for TrueBeam (TB) and Halcyon (Hal) linacs. The dosimetric parameters were compared between clinical plans (CLPs) and KBPs: CLPclosed, KBPclosed-TB and KBPclosed-Hal in closed-loop validation, CLPopen, KBPopen-TB and KBPopen-Hal in open-loop validation. Results: All organs at risk were comparable between CLPs and KBPs except for contralateral lung: V5 of KBPs was approximately 3%–7% higher than that of CLPs. V20 of total lung for KBPs showed comparable to CLPs; CLPclosed vs. KBPclosed-TB and CLPclosed vs. KBPclosed-Hal: 4.36% ± 2.87% vs. 3.54% ± 1.95% and 4.36 ± 2.87% vs. 3.54% ± 1.94% (P = 0.54 and 0.54); CLPopen vs. KBPopen-TB and CLPopen vs. KBPopen-Hal: 4.18% ± 1.57% vs. 3.55% ± 1.27% and 4.18% ± 1.57% vs. 3.67% ± 1.26% (P = 0.19 and 0.27). CI95 of KBPs with both linacs was superior to that of the CLP in closed-loop validation: CLPclosed vs. KBPclosed-TB vs. KBPclosed-Hal: 1.32% ± 0.12% vs. 1.18% ± 0.09% vs. 1.17% ± 0.06% (P < 0.01); and open-loop validation: CLPopen vs. KBPopen-TB vs. KBPopen-Hal: 1.22% ± 0.09% vs. 1.14% ± 0.04% vs. 1.16% ± 0.05% (P ≤ 0.01). Conclusions: The simplified KBPs with limited number of structures and without planner intervention were clinically acceptable in the dosimetric parameters for lung VMAT-SBRT planning.

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Dosimetric potential of knowledge‐based planning model trained with HyperArc plans for brain metastases

Sagawa

Ueda

Tsuru

et al. 2022

J Applied Clin Med Phys

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Objective Dosimetric potential of knowledge‐based RapidPlan planning model trained with HyperArc plans (Model‐HA) for brain metastases has not been reported. We developed a Model‐HA and compared its performance with that of clinical volumetric modulated arc therapy (VMAT) plans. Methods From 67 clinical stereotactic radiosurgery (SRS) HyperArc plans for brain metastases, 47 plans were used to build and train a Model‐HA. The other 20 clinical HyperArc plans were recalculated in RapidPlan system with Model‐HA. The model performance was validated with the 20 plans by comparing dosimetric parameters for normal brain tissue between clinical plans and model‐generated plans. The 20 clinical conventional VMAT‐based SRS or stereotactic radiotherapy plans (CL‐VMAT) were reoptimized with Model‐HA (RP) and HyperArc system (HA), respectively. The dosimetric parameters were compared among three plans (CL‐VMAT vs. RP vs. HA) in terms of planning target volume (PTV), normal brain excluding PTVs (Brain − PTV), brainstem, chiasm, and both optic nerves. Results In model validation, the optimization performance of Model‐HA was comparable to that of HyperArc system. In comparison to CL‐VMAT, there were no significant differences among three plans with respect to PTV coverage ( p > 0.17) and maximum dose for brainstem, chiasm, and optic nerves ( p > 0.40). RP provided significantly lower V 20 Gy , V 12 Gy , and V 4 Gy for Brain − PTV than CL‐VMAT ( p < 0.01). Conclusion The Model‐HA has the potential to significantly reduce the normal brain dose of the original VMAT plans for brain metastases.

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Dosimetric evaluation with knowledge-based planning created at different periods in volumetric-modulated arc therapy for prostate cancer: a multi-institution study

Cited by 12 publications

References 28 publications

An updating approach for knowledge‐based planning models to improve plan quality and variability in volumetric‐modulated arc therapy for prostate cancer

An updating approach for knowledge‐based planning models to improve plan quality and variability in volumetric‐modulated arc therapy for prostate cancer

Dosimetric Evaluation of Simplified Knowledge-Based Plan with an Extensive Stepping Validation Approach in Volumetric-Modulated Arc Therapy-Stereotactic Body Radiotherapy for Lung Cancer

Dosimetric potential of knowledge‐based planning model trained with HyperArc plans for brain metastases

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