3D calculation of radiation‐induced second cancer risk including dose and tissue response heterogeneities. Issue 2 (26th January 2015)

• Record Type: Journal Article
• Authors: Timlin, C.; Warren, D. R.; Rowland, B.; Madkhali, A.; Loken, J.; Partridge, M.; Jones, B.; Kruse, J.; Miller, R.
• Is Part Of: Medical physics. Volume 42:Issue 2(2015)
• Page Start: 866
• Page End: 876
• Publication Date: 2015-01-26
• Subjects: biological effects of ionising particles -- biological effects of X‐rays -- cancer -- cellular effects of radiation -- dosimetry -- radiation therapy; Effects of ionizing radiation on biological systems -- Therapeutic applications, including brachytherapy -- Dosimetry/exposure assessment -- Cancer -- Cell processes; Radiation therapy -- Scintigraphy; radiation‐induced cancer -- proton therapy -- radiotherapy; Dosimetry -- Cancer -- Radiation treatment -- Tissues -- Biomedical modeling -- Protons -- Intensity modulated radiation therapy -- Computer software -- Intracellular signaling; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4905158
• Languages: English
• ISSNs: 0094-2405

Abstract

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.