Cross-institutional knowledge-based planning (KBP) implementation and its performance comparison to Auto-Planning Engine (APE)

Wu, Binbin; Kusters, Martijn; Kunze-Busch, Martina; Dijkema, Tim; McNutt, Todd; Sanguineti, Giuseppe; Bzdusek, Karl; Dritschilo, Anatoly; Pang, Dalong

doi:10.1016/j.radonc.2017.01.012

Cited by 51 publications

(61 citation statements)

References 24 publications

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“…Automated planning aims to make treatment planning more efficient . One step toward automation is knowledge‐based planning (KBP), which comprises a group of methods that learn from historical treatment plans and predict attributes of desirable plans for new patients . KBP predictions can be input into an automated planning engine to produce a treatment plan, but existing methods introduce a new planning paradigm where planners are typically unable to adjust the final plan using familiar inverse planning techniques (e.g., adjusting objective function weights) .…”

Section: Introductionmentioning

confidence: 99%

“…There are several candidate KBP methods that can provide input for an IO model. Query methods are one branch of KBP methods that predict single DVH points . A second branch combines principal component analysis (PCA) and linear regression to predict full DVHs .…”

Section: Introductionmentioning

confidence: 99%

“…A second branch combines principal component analysis (PCA) and linear regression to predict full DVHs . These methods leverage geometric information from overlap volume histograms (OVHs), and most also consider entire structure volumes . So far, these methods have been specialized only to predict DVHs for OARs but not for targets.…”

Section: Introductionmentioning

confidence: 99%

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Knowledge‐based automated planning for oropharyngeal cancer

et al. 2018

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We automatically generate intensity-modulated radiation therapy plans for oropharyngeal cancer by combining knowledge-based planning (KBP) predictions with an inverse optimization (IO) pipeline into a single automated treatment planning pipeline. We extended two existing KBP methods, which use patients' anatomical geometry to predict achievable dose volume histograms (DVHs), and developed the first IO method that takes DVHs as direct inputs. The DVH predictions from KBP are put into the IO pipeline to automatically generate treatment plans via an intermediate step using objective function weights and an inverse planning problem. This step enables our automated planning pipeline to seamlessly fuse with the current treatment planning paradigm to increase its efficiency. Our automated pipeline can replicate, and often improve upon the clinical treatment plans by reducing the dose to healthy tissue and increasing primary target coverage. These results have been validated using a large cohort of 217 oropharyngeal cancer patients.ii Dedicated to Leslie.iii

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Knowledge‐based automated planning for oropharyngeal cancer

et al. 2018

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“…Knowledge‐based radiotherapy treatment planning is deemed to reduce the inter‐planner varieties of plan quality1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 and expedite the planning process 14, 15, 16, 1718, 19 and displayed good compatibility across patient orientations, treatment techniques, and systems 20, 21…”

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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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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“…Wu et al [22] compared AP and RP, for oropharyngeal cancer patients and found that the plan quality from both systems was comparable. Differences between the two systems were in the range of 5%, which is in good agreement to the small differences observed in our study.…”

Section: Discussionmentioning

confidence: 99%

Planning comparison of five automated treatment planning solutions for locally advanced head and neck cancer

et al. 2018

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BackgroundAutomated treatment planning and/or optimization systems (ATPS) are in the process of broad clinical implementation aiming at reducing inter-planner variability, reducing the planning time allocated for the optimization process and improving plan quality. Five different ATPS used clinically were evaluated for advanced head and neck cancer (HNC).MethodsThree radiation oncology departments compared 5 different ATPS: 1) Automatic Interactive Optimizer (AIO) in combination with RapidArc (in-house developed and Varian Medical Systems); 2) Auto-Planning (AP) (Philips Radiation Oncology Systems); 3) RapidPlan version 13.6 (RP1) with HNC model from University Hospital A (Varian Medical Systems, Palo Alto, USA); 4) RapidPlan version 13.7 (RP2) combined with scripting for automated setup of fields with HNC model from University Hospital B; 5) Raystation multicriteria optimization algorithm version 5 (RS) (Laboratories AB, Stockholm, Sweden). Eight randomly selected HNC cases from institution A and 8 from institution B were used. PTV coverage, mean and maximum dose to the organs at risk and effective planning time were compared. Ranking was done based on 3 Gy increments for the parallel organs.ResultsAll planning systems achieved the hard dose constraints for the PTVs and serial organs for all patients. Overall, AP achieved the best ranking for the parallel organs followed by RS, AIO, RP2 and RP1. The oral cavity mean dose was the lowest for RS (31.3 ± 17.6 Gy), followed by AP (33.8 ± 17.8 Gy), RP1 (34.1 ± 16.7 Gy), AIO (36.1 ± 16.8 Gy) and RP2 (36.3 ± 16.2 Gy). The submandibular glands mean dose was 33.6 ± 10.8 Gy (AP), 35.2 ± 8.4 Gy (AIO), 35.5 ± 9.3 Gy (RP2), 36.9 ± 7.6 Gy (RS) and 38.2 ± 7.0 Gy (RP1). The average effective planning working time was substantially different between the five ATPS (in minutes): < 2 ± 1 for AIO and RP2, 5 ± 1 for AP, 15 ± 2 for RP1 and 340 ± 48 for RS, respectively.ConclusionsAll ATPS were able to achieve all planning DVH constraints and the effective working time was kept bellow 20 min for each ATPS except for RS. For the parallel organs, AP performed the best, although the differences were small.

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Cross-institutional knowledge-based planning (KBP) implementation and its performance comparison to Auto-Planning Engine (APE)

Cited by 51 publications

References 24 publications

Knowledge‐based automated planning for oropharyngeal cancer

Knowledge‐based automated planning for oropharyngeal cancer

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

Planning comparison of five automated treatment planning solutions for locally advanced head and neck cancer

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