Knowledge-based treatment planning: An inter-technique and inter-system feasibility study for prostate cancer

Cagni, E.; Botti, A.; Micera, R.; Galeandro, M.; Sghedoni, Roberto; Orlandi, M.; Iotti, Cinzia; Cozzi, Luca; Iori, Mauro

doi:10.1016/j.ejmp.2017.03.002

Cited by 46 publications

(39 citation statements)

References 26 publications

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“…The RapidPlan module in Eclipse treatment planning system of version 13.5 or later (Varian Medical Systems, Palo Alto, CA) has commercialized the knowledge‐based solution18, 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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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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“…This group validated model performance and provided feedback to the final clinical version. Our experience showed that a collaboration of this type has the potential to develop models faster since, as previously been highlighted, Table 3 All development can be time-consuming [27], and more robustly due to the prompt availability and diversity of the plans included in the model training [20]. It was not previously shown that such a heterogeneous input population can be used to create uniform models in a diverse plan environment.…”

Section: Discussionmentioning

confidence: 80%

“…As a critical step towards this objective, an effort was made to reduce variability in treatment planning and improve overall plan quality by introducing automation. These institutions provided clinically approved treatment plans for a range of tumour sites (prostate, head and neck and anal canal [19]), in order to create heterogeneous training datasets, and models, with potential benefits, as previously highlighted [20]. A similar initiative was previously promoted by Schubert et al and Roach et al [21,22], however their models were created using data from a single institution, and then distributed to other centres.…”

Section: Introductionmentioning

confidence: 99%

“…This work presents the development and implementation of a multicentre automated prostate cancer model across the range of participating centres with different treatment approaches. Several series of results for the use of automated models for prostate planning are available [7,11,13,20,21,23,24], however, this project is unique as the model and its validation combines contouring, planning experience, protocols and fractionation regimens from different institutions. To evaluate its performance, the results of the analysis conducted on the dose distributions collected in the first months after its clinical implementation are presented, looking at inter-centre and intra-centre variability, along with the overall effect of introducing the same multicentre model across all participating centres.…”

Section: Introductionmentioning

confidence: 99%

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Development of a multicentre automated model to reduce planning variability in radiotherapy of prostate cancer

Panettieri

Ball

Chapman³

et al. 2019

Physics and Imaging in Radiation Oncology

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A B S T R A C TBackground and purpose: Inter-institutional studies highlighted correlation between consistent radiotherapy quality and improved overall patient survival. In treatment planning automation has the potential to address differences due to user-experience and training, promoting standardisation. The aim of this study was to evaluate implementation and clinical effect of a multicentre collaboratively-developed automated planning model for Intensity-Modulated Radiation Therapy/ Volumetric-Modulated Arc Therapy of prostate. The model was built using a variety of public institutions' clinical plans, incorporating different contouring and dose protocols, aiming at minimising their variation. Methods and materials: A model using 110 clinically approved and treated prostate plans provided by different radiotherapy centres was built with RapidPlan (RP), for use on intact and post-prostatectomy prostate cases. The model was validated, distributed and introduced into clinical practice in all institutions. To investigate its impact a total of 126 patients, originally manually inverse planned (OP), were replanned using RP without additional planner manual intervention. Target and organat-risk (OAR) metrics were statistically compared between original and automated plans. Results: For all centres combined and individually, RP provided plans comparable or superior to OP for all dose metrics. Statistically significant reductions with RP were found in bladder (V40Gy) and rectal (V50Gy) low doses (within 2.3% and 3.4% for combined and 4% and 10% individually). No clinically significant changes were seen for the PTV, independently of seminal vesicle inclusion. Conclusion: This project showed it is feasible to develop, share and implement RP models created with plans from different institutions treated with a variety of techniques and dose protocols, with the potential of improving treatment planning results and/or efficiency despite the original variability.

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“…79 To gain further understanding of the overall performance of KBP in prostate cancer planning, we have developed a visualization scheme to provide a summary view of nine KBP prostate studies that compared re-planned results with original clinically approved values. 20,25,27,34,35,56,57,60,63 As mentioned previously, the challenge of summarizing results across all studies lie in two aspects: (a) the results are based on different sample points of the DVH curve and measure changes along different directions (e.g., one study may use D35 while another use V65); (b) some of the studies report only the differences in DVH point metrics (e.g., D35 is reduced by 1.5) without providing the original clinically approved values. The first issue makes it difficult to quantitatively compare results from different studies even though many DVH point metrics assess performance in similar areas of the DVH curve.…”

Section: C Performance Of Kbpmentioning

confidence: 99%

Knowledge‐based planning for intensity‐modulated radiation therapy: A review of data‐driven approaches

2019

Medical Physics

170

150

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Purpose Intensity‐Modulated Radiation Therapy ( IMRT ), including its variations (including IMRT , Volumetric Arc Therapy ( VMAT ), and Tomotherapy), is a widely used and critically important technology for cancer treatment. It is a knowledge‐intensive technology due not only to its own technical complexity, but also to the inherently conflicting nature of maximizing tumor control while minimizing normal organ damage. As IMRT experience and especially the carefully designed clinical plan data are accumulated during the past two decades, a new set of methods commonly termed knowledge‐based planning ( KBP ) have been developed that aim to improve the quality and efficiency of IMRT planning by learning from the database of past clinical plans. Some of this development has led to commercial products recently that allowed the investigation of KBP in numerous clinical applications. In this literature review, we will attempt to present a summary of published methods of knowledge‐based approaches in IMRT and recent clinical validation results. Methods In March 2018, a literature search was conducted in the NIH Medline database using the PubMed interface to identify publications that describe methods and validations related to KBP in IMRT including variations such as VMAT and Tomotherapy. The search criteria were designed to have a broad scope to capture relevant results with high sensitivity. The authors filtered down the search results according to a predefined selection criteria by reviewing the titles and abstracts first and then by reviewing the full text. A few papers were added to the list based on the references of the reviewed papers. The final set of papers was reviewed and summarized here. Results The initial search yielded a total of 740 articles. A careful review of the titles, abstracts, and eventually the full text and then adding relevant articles from reviewing the references resulted in a final list of 73 articles published between 2011 and early 2018. These articles described methods for developing knowledge models for predicting such parameters as dosimetric and dose‐volume points, voxel‐level doses, and objective function weights that improve or automate IMRT planning for various cancer sites, addressing different clinical and quality assurance needs, and using a variety of machine learning approaches. A number of articles reported carefully designed clinical studies that assessed the performance of KBP models in realistic clinical applications. Overwhelming majority of the studies demonstrated the benefits of KBP in achieving comparable and often improve...

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Knowledge-based treatment planning: An inter-technique and inter-system feasibility study for prostate cancer

Cited by 46 publications

References 26 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

Development of a multicentre automated model to reduce planning variability in radiotherapy of prostate cancer

Knowledge‐based planning for intensity‐modulated radiation therapy: A review of data‐driven approaches

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