Clinical implementation of a knowledge based planning tool for prostate VMAT

Powis, Richard; Bird, Andrew; Brennan, Matthew; Hinks, Susan; Newman, Hannah; Reed, Katie; Sage, John; Webster, G.

doi:10.1186/s13014-017-0814-z

Cited by 29 publications

(39 citation statements)

References 18 publications

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“…Such differences may limit the desired organs‐at‐risk (OAR) sparing and the target coverage that can be achieved. Recent investigations have demonstrated that knowledge based planning (KBP), which utilizes a library of previously treated patient plans, has enormous potential for improving the quality and consistency of treatment plans …”

Section: Introductionmentioning

confidence: 99%

Assessment of specific versus combined purpose knowledge based models in prostate radiotherapy

Bossart

Duffy

Simpson

et al. 2018

J Applied Clin Med Phys

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Knowledge‐based planning (KBP) can be used to improve plan quality, planning speed, and reduce the inter‐patient plan variability. KPB may also identify and reduce systematic variations in VMAT plans, something very important in multi‐institutional clinical trials. Training of a KBP library is a complex and difficult process, and models must be validated prior to their clinical use. The purpose of this work is to assess the quality of the treatment plans generated using a specific versus combined purpose model KBP library for prostate cancer. Seven KBP model libraries were created from a set of patients treated on various Institutional Review Board (IRB) approved protocols. All KBP libraries were validated using an independent set of twenty patients (half treated Pr: Prostate alone half treated PLN: prostate plus pelvic lymph nodes). Two models were tested on the Pr patients only, four tested on PLN patients only, and one tested on all patients. All plans were normalized such that at least 95% of the prostate planning target volume received 100% of the planned dose. The plans based on different model libraries were compared to each other and the expert clinical plan. For Pr plans there were almost no statistically significant differences (P < 0.008) between the plans types except conformity index (CI) with library plans better than the expert. For PLN plans, all model libraries in generally showed femur doses and CI better than the expert plans (P < 0.003). This study demonstrated that no large differences were observed between specific versus combined KBP model libraries in dosimetry of prostate cancer patients. This would allow for a fewer specific plans to be needed to create a model library. Further studies are needed to evaluate benefits of combined purpose model libraries for planning of complex sites such as head and neck cancer.

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Section: Introductionmentioning

confidence: 99%

Assessment of specific versus combined purpose knowledge based models in prostate radiotherapy

Bossart

Duffy

Simpson

et al. 2018

J Applied Clin Med Phys

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“…One or more specific dose metrics (14 articles) These methods aim to predict single or a small number of dose metrics to either guide plan optimization or a specific planning decision (e.g., the need for hydrogel injection). 21,[43][44][45][46][47][48][49][50][51][52][53][54][55] 3. Voxel-level doses (13 articles) This group of methods predicts dose at each voxel in 3D space.…”

Section: Dose-volume Histogram (Dvh) (36 Articles)mentioning

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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“…In recent years automated approaches have become popular in radiotherapy with the aim of reducing variability in tasks which are still heavily dependent on user-experience and training. With the widespread use of inverse planning techniques, these tools have become employed in Intensity-Modulated Radiation Therapy (IMRT) and Volumetric-Modulated Arc Therapy (VMAT) treatment planning, due to the development of dedicated algorithms [1][2][3][4][5], some of which have been included in commercial treatment planning systems (TPS) [6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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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Clinical implementation of a knowledge based planning tool for prostate VMAT

Cited by 29 publications

References 18 publications

Assessment of specific versus combined purpose knowledge based models in prostate radiotherapy

Assessment of specific versus combined purpose knowledge based models in prostate radiotherapy

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

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

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