Clinical validation and benchmarking of knowledge-based IMRT and VMAT treatment planning in pelvic anatomy

Hussein, Mohammad; South, C.; Barry, M; Adams, Elizabeth; Jordan, Thomas; Stewart, Alexandra J.; Nisbet, Andrew

doi:10.1016/j.radonc.2016.06.022

Cited by 153 publications

(226 citation statements)

References 20 publications

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“…These results echoed the superiority of knowledge‐based solution over the conventional trial‐and‐error manual planning, in line with previous publications 17, 20, 22, 23, 24, 25, 26, 27. It suggested that knowledge‐ and geometry‐based dosimetric predictions can help avoid selecting suboptimal or conflict optimization constraints as manual limitations.…”

Section: Discussionsupporting

confidence: 87%

“…Well‐trained RapidPlan models have outperformed conventional trial and error‐based manual planning by reducing excess organs‐at‐risk (OAR) dose with greater consistency 17, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30. Should the model performance be highly dependent on the library volume31 and average quality of the training plans,17, 32 incorporating the model‐improved constituent training plans into the model (closed‐loop)25 may potentially evolve the model as a cycle of interactive improvement.…”

Section: Introductionmentioning

confidence: 99%

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An interactive plan and model evolution method for knowledge‐based pelvic VMAT planning

Wang

Huang

et al. 2018

J Applied Clin Med Phys

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PurposeTo test if a RapidPlan DVH estimation model and its training plans can be improved interactively through a closed‐loop evolution process.Methods and materialsEighty‐one manual plans (P0) that were used to configure an initial rectal RapidPlan model (M0) were reoptimized using M0 (closed‐loop), yielding 81 P1 plans. The 75 improved P1 (P1+) and the remaining 6 P0 were used to configure model M1. The 81 training plans were reoptimized again using M1, producing 23 P2 plans that were superior to both their P0 and P1 forms (P2+). Hence, the knowledge base of model M2 composed of 6 P0, 52 P1+, and 23 P2+. Models were tested dosimetrically on 30 VMAT validation cases (Pv) that were not used for training, yielding Pv(M0), Pv(M1), and Pv(M2) respectively. The 30 Pv were also optimized by M2_new as trained by the library of M2 and 30 Pv(M0).ResultsBased on comparable target dose coverage, the first closed‐loop reoptimization significantly (P < 0.01) reduced the 81 training plans’ mean dose to femoral head, urinary bladder, and small bowel by 2.65 Gy/15.63%, 2.06 Gy/8.11%, and 1.47 Gy/6.31% respectively, which were further reduced significantly (P < 0.01) in the second closed‐loop reoptimization by 0.04 Gy/0.28%, 0.18 Gy/0.77%, 0.22 Gy/1.01% respectively. However, open‐loop VMAT validations displayed more complex and intertwined plan quality changes: mean dose to urinary bladder and small bowel decreased monotonically using M1 (by 0.34 Gy/1.47%, 0.25 Gy/1.13%) and M2 (by 0.36 Gy/1.56%, 0.30 Gy/1.36%) than using M0. However, mean dose to femoral head increased by 0.81 Gy/6.64% (M1) and 0.91 Gy/7.46% (M2) than using M0. The overfitting problem was relieved by applying model M2_new.ConclusionsThe RapidPlan model and its constituent plans can improve each other interactively through a closed‐loop evolution process. Incorporating new patients into the original training library can improve the RapidPlan model and the upcoming plans interactively.

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Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

An interactive plan and model evolution method for knowledge‐based pelvic VMAT planning

Wang

Huang

et al. 2018

J Applied Clin Med Phys

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“…In intensity-modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT), variations in knowledge and experience can lead to significant differences in the plan quality and may compromise the gains of high-precision radiotherapy [1, 2]. Knowledge-based treatment planning (KBP) is an approach to reduce the differences in plan quality and improve planning consistency [1].…”

Section: Introductionmentioning

confidence: 99%

“…Knowledge-based treatment planning (KBP) is an approach to reduce the differences in plan quality and improve planning consistency [1]. A commercial KBP module, RapidPlan (Varian Medical Systems, Palo Alto CA, USA) has been released for the Eclipse (Varian) treatment planning system (TPS).…”

Section: Introductionmentioning

confidence: 99%

“…A commercial KBP module, RapidPlan (Varian Medical Systems, Palo Alto CA, USA) has been released for the Eclipse (Varian) treatment planning system (TPS). KBP uses a statistical model generated from a library of clinically accepted plans with consistent high quality to train dose volume histograms (DVHs) [1, 3]. This model predicts an achievable DVH range and generates dose volume objectives for IMRT and VMAT plan optimization [1, 3].…”

Section: Introductionmentioning

confidence: 99%

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Mechanical performance of a commercial knowledge-based VMAT planning for prostate cancer

et al. 2018

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BackgroundThis study clarified the mechanical performance of volumetric modulated arc therapy (VMAT) plans for prostate cancer generated with a commercial knowledge-based treatment planning (KBP) and whether KBP system could be applied clinically without any major problems with mechanical performance.MethodsThirty consecutive prostate cancer patients who underwent VMAT using extant clinical plans were evaluated. The mechanical performance and dosimetric accuracy of the single optimized KBPs, which were trained with other 51 clinical plans, were compared with the clinical plans. The mechanical performance metrics were mean field area (MFA), mean asymmetry distance (MAD), cross-axis score (CAS), closed leaf score (CLS), small aperture score (SAS), leaf travel (LT), modulation complexity score (MCSv), and monitor unit (MU). The γ passing rates were evaluated with ArcCheck and EBT3 film.ResultsThe mean mechanical performance metrics (clinical plan vs. KBP) were as follows: 18.28 cm2 vs. 17.25 cm2 (MFA), 21.08 mm vs. 20.47 mm (MAD), 0.54 vs. 0.55 (CAS), 0.040 vs. 0.051 (CLS), 0.20 vs. 0.23 (SAS5mm), 458.5 mm vs. 418.8 mm (LT), 0.27 vs. 0.27 (MCSv), and 618.2 vs. 622.1 (MU), respectively. Significant differences were observed for CLS and LT. The average γ passing rates (clinical plan vs. KBP) were as follows: 99.0% vs. 99.1% (3%/3 mm) and 92.4% vs. 92.5% (2%/2 mm) with ArcCHeck, and 99.5% vs. 99.4% (3%/3 mm) and 95.2% vs. 95.4% (2%/2 mm) with EBT3 film, respectively.ConclusionsThe KBP used lower multileaf collimator (MLC) travel and more closed or small MLC apertures than the clinical plan. The KBP system of VMAT for the prostate cancer was acceptable for clinical use without any major problems.

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Photon Optimizer (PO) prevails over Progressive Resolution Optimizer (PRO) for VMAT planning with or without knowledge‐based solution

Jiang

Yue

et al. 2017

J Applied Clin Med Phys

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The enhanced dosimetric performance of knowledge‐based volumetric modulated arc therapy (VMAT) planning might be jointly contributed by the patient‐specific optimization objectives, as estimated by the RapidPlan model, and by the potentially improved Photon Optimizer (PO) algorithm than the previous Progressive Resolution Optimizer (PRO) engine. As PO is mandatory for RapidPlan estimation but optional for conventional manual planning, appreciating the two optimizers may provide practical guidelines for the algorithm selection because knowledge‐based planning may not replace the current method completely in a short run. Using a previously validated dose–volume histogram (DVH) estimation model which can produce clinically acceptable plans automatically for rectal cancer patients without interactive manual adjustment, this study reoptimized 30 historically approved plans (referred as clinical plans that were created manually with PRO) with RapidPlan solution (PO plans). Then the PRO algorithm was utilized to optimize the plans again using the same dose–volume constraints as PO plans, where the line objectives were converted as a series of point objectives automatically (PRO plans). On the basis of comparable target dose coverage, the combined applications of new objectives and PO algorithm have significantly reduced the organs‐at‐risk (OAR) exposure by 23.49–32.72% than the clinical plans. These discrepancies have been largely preserved after substituting PRO for PO, indicating the dosimetric improvements were mostly attributable to the refined objectives. Therefore, Eclipse users of earlier versions may instantly benefit from adopting the model‐generated objectives from other RapidPlan‐equipped centers, even with PRO algorithm. However, the additional contribution made by the PO relative to PRO accounted for 1.54–3.74%, suggesting PO should be selected with priority whenever available, with or without RapidPlan solution as a purchasable package. Significantly increased monitor units were associated with the model‐generated objectives but independent from the optimizers, indicating higher modulation in these plans. As a summary, PO prevails over PRO algorithm for VMAT planning with or without knowledge‐based technique.

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Clinical validation and benchmarking of knowledge-based IMRT and VMAT treatment planning in pelvic anatomy

Cited by 153 publications

References 20 publications

An interactive plan and model evolution method for knowledge‐based pelvic VMAT planning

An interactive plan and model evolution method for knowledge‐based pelvic VMAT planning

Mechanical performance of a commercial knowledge-based VMAT planning for prostate cancer

Photon Optimizer (PO) prevails over Progressive Resolution Optimizer (PRO) for VMAT planning with or without knowledge‐based solution

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