Knowledge-based IMRT planning for individual liver cancer patients using a novel specific model

Yu, Gang; Li, Yang; Feng, Ziwei; Cheng, Tao; Yu, Zuyi; Li, Baosheng; Li, Dengwang

doi:10.1186/s13014-018-0996-z

Cited by 21 publications

(20 citation statements)

References 30 publications

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“…There are many studies in the literature which have clinically implemented commercial and in-house implementations of PB-AIO, KBP, and MCO automated planning. [42][43][44][45][46][48][49][50][51][52][53][54][55][56][57][59][60][61][62][63][64][65][66][67][68][70][71][72][73][74][75][76][77][78][79][81][82][83][89][90][91][92][93][94][95][96][97][99][100][101][102][103][150][151]…”

Section: Clinical Evaluation and Implementation Of Automated Planningmentioning

confidence: 99%

“…89 Knowledge-based planning KBP has been clinically investigated in various clinical sites, with most studies reporting that KBP is at least as good, or (slightly) better, than manual planning with improved efficiency and consistency, without manual intervention. [42][43][44][45][46][48][49][50][51][52][53][54][55][56][57][59][60][61][62][63][64][65][66][67][68][70][71][72][73][74][75][76][77][78][79][81][82][83] All of the published studies are via the use of the commercial implementation of KBP in the Varian RapidPlan solution.…”

Section: Clinical Evaluation and Implementation Of Automated Planningmentioning

confidence: 99%

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Automation in intensity modulated radiotherapy treatment planning—a review of recent innovations

Hussein

Heijmen

Verellen

et al. 2018

The British Journal of Radiology

188

187

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Radiotherapy treatment planning of complex radiotherapy techniques, such as intensity modulated radiotherapy and volumetric modulated arc therapy, is a resource-intensive process requiring a high level of treatment planner intervention to ensure high plan quality. This can lead to variability in the quality of treatment plans and the efficiency in which plans are produced, depending on the skills and experience of the operator and available planning time. Within the last few years, there has been significant progress in the research and development of intensity modulated radiotherapy treatment planning approaches with automation support, with most commercial manufacturers now offering some form of solution. There is a rapidly growing number of research articles published in the scientific literature on the topic. This paper critically reviews the body of publications up to April 2018. The review describes the different types of automation algorithms, including the advantages and current limitations. Also included is a discussion on the potential issues with routine clinical implementation of such software, and highlights areas for future research.

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Section: Clinical Evaluation and Implementation Of Automated Planningmentioning

confidence: 99%

Section: Clinical Evaluation and Implementation Of Automated Planningmentioning

confidence: 99%

Automation in intensity modulated radiotherapy treatment planning—a review of recent innovations

Hussein

Heijmen

Verellen

et al. 2018

The British Journal of Radiology

188

187

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“…This group of methods aims to predict the entire DVH curve for a new patient and then frequently uses the predicted DVHs to guide the plan optimization process …”

Section: Resultsmentioning

confidence: 99%

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

2019

Medical Physics

180

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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“…To the best of our knowledge, this is the first study to assess the performance of KBPs in single‐isocenter VMAT for multiple brain metastases using the k ‐fold CV method. The number of test samples was almost equivalent to previous reports on KB planning 11–15,19 . As shown in Table 3, most previous studies employed the hold‐out method; however, as described above, one disadvantage of this method is that the performance evaluation is subject to a higher variance given the smaller dataset, whereas multiple validations can be performed using the k ‐fold CV method to conclusively determine the model overfitting.…”

Section: Discussionmentioning

confidence: 99%

“…In general, k ‐fold CV is usually considered as the preferred method because it allows the model to train via multiple train‐test splits providing a better indication of how well the model will perform on unseen data. Most studies on KB planning employed the hold‐out method for prostate/uterine cancer, head and neck cancer, and lung, liver, and primary brain tumors 11–15 . To date, however, there has been insufficient literature comparing the performance of KB models through k ‐fold CV, or on the application of KB planning to multiple brain metastases.…”

Section: Introductionmentioning

confidence: 99%

Validation of the clinical applicability of knowledge‐based planning models in single‐isocenter volumetric‐modulated arc therapy for multiple brain metastases

Kishi

Nakamura

Hirashima

et al. 2020

J Applied Clin Med Phys

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Purpose To validate the clinical applicability of knowledge‐based (KB) planning in single‐isocenter volumetric‐modulated arc therapy (VMAT) for multiple brain metastases using the k ‐fold cross‐validation (CV) method. Methods This study comprised 60 consecutive patients with multiple brain metastases treated with single‐isocenter VMAT (28 Gy in five fractions). The patients were divided randomly into five groups (Groups 1–5). The data of Groups 1–4 were used as the training and validation dataset and those of Group 5 were used as the testing dataset. Four KB models were created from three of the training and validation datasets and then applied to the remaining Groups as the fourfold CV phase. As the testing phase, the final KB model was applied to Group 5 and the dose distributions were calculated with a single optimization process. The dose‐volume indices (DVIs), modified Ian Paddick Conformity Index (mIPCI), modulation complexity scores for VMAT plans (MCSv), and the total number of monitor units (MUs) of the final KB plan were compared to those of the clinical plan (CL) using a paired Wilcoxon signed‐rank test. Results In the fourfold CV phase, no significant differences were observed in the DVIs among the four KB plans (KBPs). In the testing phase, the final KB plan was statistically equivalent to the CL, except for planning target volumes (PTVs) D 2% and D 50% . The differences between the CL and KBP in terms of the PTV D 99.5% , normal brain, and D max to all organs at risk (OARs) were not significant. The KBP achieved a lower total number of MUs and higher MCSv than the CL with no significant difference. Conclusions We demonstrated that a KB model in a single‐isocenter VMAT for multiple brain metastases was equivalent in dose distribution, MCSv, and total number of MUs to a CL with a single optimization.

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Knowledge-based IMRT planning for individual liver cancer patients using a novel specific model

Cited by 21 publications

References 30 publications

Automation in intensity modulated radiotherapy treatment planning—a review of recent innovations

Automation in intensity modulated radiotherapy treatment planning—a review of recent innovations

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

Validation of the clinical applicability of knowledge‐based planning models in single‐isocenter volumetric‐modulated arc therapy for multiple brain metastases

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