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3D calculation of radiation-induced second cancer risk including dose and tissue response heterogeneities.

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Title: 3D calculation of radiation-induced second cancer risk including dose and tissue response heterogeneities.
Authors: Timlin, C.1, Warren, D. R.1, Rowland, B.1, Madkhali, A.1, Loken, J.1, Partridge, M.2, Jones, B.2, Kruse, J.3, Miller, R.3
Source: Medical Physics. Feb2015, Vol. 42 Issue 2, p866-876. 11p.
Subjects: Cancer risk factors, Radiation exposure, Radiotherapy treatment planning, Radiobiology, Parameter estimation
Abstract: Purpose: Tools for comparing relative induced second cancer risk, to inform choice of radiotherapy treatment plan, are becoming increasingly necessary as the availability of new treatment modalities expands. Uncertainties, in both radiobiological models and model parameters, limit the confidence of such calculations. The aim of this study was to develop and demonstrate a software tool to produce a malignant induction probability (MIP) calculation which incorporates patient-specific dose and allows for the varying responses of different tissue types to radiation. Methods: The tool has been used to calculate relative MIPs for four different treatment plans targeting a subtotally resected meningioma: 3D conformal radiotherapy (3DCFRT), volumetric modulated arc therapy (VMAT), intensity-modulated x-ray therapy (IMRT), and scanned protons. Results: Two plausible MIP models, with considerably different dose-response relationships, were considered. A fractionated linear-quadratic induction and cell-kill model gave a mean relative cancer risk (normalized to 3DCFRT) of 113% for VMAT, 16% for protons, and 52% for IMRT. For a linear no-threshold model, these figures were 105%, 42%, and 78%, respectively. The relative MIP between plans was shown to be significantly more robust to radiobiological parameter uncertainties compared to absolute MIP. Both models resulted in the same ranking of modalities, in terms of MIP, for this clinical case. Conclusions: The results demonstrate that relative MIP is a useful metric with which treatment plans can be ranked, regardless of parameter- and model-based uncertainties. With further validation, this metric could be used to discriminate between plans that are equivalent with respect to other planning priorities. [ABSTRACT FROM AUTHOR]
Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Feb2015, Vol. 42 Issue 2, p866-876. 11p.
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  Data: Purpose: Tools for comparing relative induced second cancer risk, to inform choice of radiotherapy treatment plan, are becoming increasingly necessary as the availability of new treatment modalities expands. Uncertainties, in both radiobiological models and model parameters, limit the confidence of such calculations. The aim of this study was to develop and demonstrate a software tool to produce a malignant induction probability (MIP) calculation which incorporates patient-specific dose and allows for the varying responses of different tissue types to radiation. Methods: The tool has been used to calculate relative MIPs for four different treatment plans targeting a subtotally resected meningioma: 3D conformal radiotherapy (3DCFRT), volumetric modulated arc therapy (VMAT), intensity-modulated x-ray therapy (IMRT), and scanned protons. Results: Two plausible MIP models, with considerably different dose-response relationships, were considered. A fractionated linear-quadratic induction and cell-kill model gave a mean relative cancer risk (normalized to 3DCFRT) of 113% for VMAT, 16% for protons, and 52% for IMRT. For a linear no-threshold model, these figures were 105%, 42%, and 78%, respectively. The relative MIP between plans was shown to be significantly more robust to radiobiological parameter uncertainties compared to absolute MIP. Both models resulted in the same ranking of modalities, in terms of MIP, for this clinical case. Conclusions: The results demonstrate that relative MIP is a useful metric with which treatment plans can be ranked, regardless of parameter- and model-based uncertainties. With further validation, this metric could be used to discriminate between plans that are equivalent with respect to other planning priorities. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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