Impact of model and dose uncertainty on model-based selection of oropharyngeal cancer patients for proton therapy

Bijman, R.; Breedveld, S.; Arts, Tine; Astreinidou, E.; Jong, M.A. de; Granton, Patrick V.; Петит, С.; Hoogeman, M.

doi:10.1080/0284186x.2017.1355113

Cited by 34 publications

(31 citation statements)

References 24 publications

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“…Bijman et al assessed the model uncertainty using a probability distribution (mean and CI) of the model coefficients from multivariable NTCP models in head and neck cancers and concluded that the accuracy of the MBA on patient selection for PBT is largely affected by the uncertainty in the NTCP models [ 4 ]. In contrast, the uncertainty of the LKB NTCP model for RILD relies on the model parameters’ variability,

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Section: Discussionmentioning

confidence: 99%

“…The selective use of PBT relies on a predefined ∆NTCP threshold, such as 10% for Grade II or 15% for total clinical benefit, according to the Dutch Society of Radiation Oncology (NRVO) guidelines [ 3 ]. However, these models were derived from a statistical assumption based on a small subset of the population, and the model uncertainty affects the accuracy of PBT selection [ 4 ]. In other words, underestimating ∆NTCP can eliminate the opportunity to benefit from PBT, whereas an overly cautious practice might cause the unnecessary use of this high-cost treatment.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the uncertainty in a normal tissue complication probability difference (∆NTCP): radiation-induced liver disease (RILD) in liver tumour patients treated with proton vs X-ray therapy

Kobashi

Prayongrat

Kimoto

et al. 2018

Journal of Radiation Research

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Modern radiotherapy technologies such as proton beam therapy (PBT) permit dose escalation to the tumour and minimize unnecessary doses to normal tissues. To achieve appropriate patient selection for PBT, a normal tissue complication probability (NTCP) model can be applied to estimate the risk of treatment-related toxicity relative to X-ray therapy (XRT). A methodology for estimating the difference in NTCP (∆NTCP), including its uncertainty as a function of dose to normal tissue, is described in this study using the Delta method, a statistical method for evaluating the variance of functions, considering the variance–covariance matrix. We used a virtual individual patient dataset of radiation-induced liver disease (RILD) in liver tumour patients who were treated with XRT as a study model. As an alternative option for individual patient data, dose-bin data, which consists of the number of patients who developed toxicity in each dose level/bin and the total number of patients in that dose level/bin, are useful for multi-institutional data sharing. It provides comparable accuracy with individual patient data when using the Delta method. With reliable NTCP models, the ∆NTCP with uncertainty might potentially guide the use of PBT; however, clinical validation and a cost-effectiveness study are needed to determine the appropriate ∆NTCP threshold.

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and

.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the uncertainty in a normal tissue complication probability difference (∆NTCP): radiation-induced liver disease (RILD) in liver tumour patients treated with proton vs X-ray therapy

Kobashi

Prayongrat

Kimoto

et al. 2018

Journal of Radiation Research

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“…Meanwhile, proton treatment planning studies which draw upon empirical RBE models are increasingly highlighting end-of-range RBE effects as a serious area for concern with regards to possible toxicities, 38 and RBE uncertainties clearly impact upon both normal tissue complication probability and tumour control probability estimates for proton therapy. 39 Improved understanding of proton RBE would bring increased precision to model-based selection of those patients who stand to benefit most from proton therapy, 40 enhancing clinical trial design and interpretation. With sufficient validation, multiscale RBE models could also offer true "biological dose" optimisation within clinical treatment planning systems, dose-painting of radioresistant tumour regions and perhaps, even treatment individualisation based upon a specific patient's tumour or normal tissue biology.…”

Section: The Potential Impact Of Multiscale Rbe Modelling On Clinicalmentioning

confidence: 99%

Proton relative biological effectiveness (RBE): a multiscale problem

Underwood

McMahon

2018

The British Journal of Radiology

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Proton radiotherapy is undergoing rapid expansion both within the UK and internationally, but significant challenges still need to be overcome if maximum benefit is to be realised from this technique. One major limitation is the persistent uncertainty in proton relative biological effectiveness (RBE). While RBE values are needed to link proton radiotherapy to our existing experience with photon radiotherapy, RBE remains poorly understood and is typically incorporated as a constant dose scaling factor of 1.1 in clinical plans. This is in contrast to extensive experimental evidence indicating that RBE is a function of dose, tissue type, and proton linear energy transfer, among other parameters. In this article, we discuss the challenges associated with obtaining clinically relevant values for proton RBE through commonly-used assays, and highlight the wide range of other experimental end points which can inform our understanding of RBE. We propose that accurate and robust optimization of proton radiotherapy ultimately requires a multiscale understanding of RBE, integrating subcellular, cellular, and patient-level processes.

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“…Due to the effect of dose uncertainties on the accuracy of model-based patient selection [ 17 ], the treatment plan robustness and differences between planned and delivered dose distributions need to be investigated. The accuracy of the beam delivery requires reliable beam profiles and minimal uncertainties in beam range and set-up errors.…”

Section: Proposed Systematic Development Of Tcp/ntcp Modelsmentioning

confidence: 99%

“…However, some statistical issues remain unresolved: for example, studies on NTCP models did not consider model uncertainties. This could lead to random errors and misinterpretations of the results [ 17 ]. Additionally, appropriate thresholds must be defined at specific endpoints for each particular tumor on a cost–effectiveness basis.…”

Section: Opinions Identifying the Shortcomings Of A Model-based Appromentioning

confidence: 99%

Present developments in reaching an international consensus for a model-based approach to particle beam therapy

Prayongrat

Umegaki

Schaaf

et al. 2018

Journal of Radiation Research

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Particle beam therapy (PBT), including proton and carbon ion therapy, is an emerging innovative treatment for cancer patients. Due to the high cost of and limited access to treatment, meticulous selection of patients who would benefit most from PBT, when compared with standard X-ray therapy (XRT), is necessary. Due to the cost and labor involved in randomized controlled trials, the model-based approach (MBA) is used as an alternative means of establishing scientific evidence in medicine, and it can be improved continuously. Good databases and reasonable models are crucial for the reliability of this approach. The tumor control probability and normal tissue complication probability models are good illustrations of the advantages of PBT, but pre-existing NTCP models have been derived from historical patient treatments from the XRT era. This highlights the necessity of prospectively analyzing specific treatment-related toxicities in order to develop PBT-compatible models. An international consensus has been reached at the Global Institution for Collaborative Research and Education (GI-CoRE) joint symposium, concluding that a systematically developed model is required for model accuracy and performance. Six important steps that need to be observed in these considerations include patient selection, treatment planning, beam delivery, dose verification, response assessment, and data analysis. Advanced technologies in radiotherapy and computer science can be integrated to improve the efficacy of a treatment. Model validation and appropriately defined thresholds in a cost-effectiveness centered manner, together with quality assurance in the treatment planning, have to be achieved prior to clinical implementation.

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Impact of model and dose uncertainty on model-based selection of oropharyngeal cancer patients for proton therapy

Abstract: Model and dose uncertainty highly influence the accuracy of model-based patient selection for proton therapy. A reduction of NTCP-model uncertainty is necessary to reach more accurate model-based patient selection.

Cited by 34 publications

References 24 publications

Assessing the uncertainty in a normal tissue complication probability difference (∆NTCP): radiation-induced liver disease (RILD) in liver tumour patients treated with proton vs X-ray therapy

Assessing the uncertainty in a normal tissue complication probability difference (∆NTCP): radiation-induced liver disease (RILD) in liver tumour patients treated with proton vs X-ray therapy

Proton relative biological effectiveness (RBE): a multiscale problem

Present developments in reaching an international consensus for a model-based approach to particle beam therapy

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