A voxel‐based multiscale model to simulate the radiation response of hypoxic tumors. Issue 1 (16th December 2014)

• Record Type: Journal Article
• Authors: Espinoza, I.; Peschke, P.; Karger, C. P.
• Is Part Of: Medical physics. Volume 42:Issue 1(2015)
• Page Start: 90
• Page End: 102
• Publication Date: 2014-12-16
• Subjects: biological effects of ionising radiation -- cellular effects of radiation -- dosimetry -- probability -- radiation therapy -- spatiotemporal phenomena -- tumours; Therapeutic applications, including brachytherapy -- Dosimetry/exposure assessment -- Spatiotemporal pattern formation in cellular populations -- Growth and division -- Probability theory; Radiation therapy -- Scintigraphy; radiotherapy -- tumor hypoxia -- reoxygenation -- response simulation; Cancer -- Cell growth -- Dosimetry -- Biomedical modeling -- Medical image spatial resolution -- Tissues -- Radiation treatment -- Spatial analysis; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4903298
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: In radiotherapy, it is important to predict the response of tumors to irradiation prior to the treatment. This is especially important for hypoxic tumors, which are known to be highly radioresistant. Mathematical modeling based on the dose distribution, biological parameters, and medical images may help to improve this prediction and to optimize the treatment plan. Methods: A voxel‐based multiscale tumor response model for simulating the radiation response of hypoxic tumors was developed. It considers viable and dead tumor cells, capillary and normal cells, as well as the most relevant biological processes such as (i) proliferation of tumor cells, (ii) hypoxia‐induced angiogenesis, (iii) spatial exchange of cells leading to tumor growth, (iv) oxygen‐dependent cell survival after irradiation, (v) resorption of dead cells, and (vi) spatial exchange of cells leading to tumor shrinkage. Oxygenation is described on a microscopic scale using a previously published tumor oxygenation model, which calculates the oxygen distribution for each voxel using the vascular fraction as the most important input parameter. To demonstrate the capabilities of the model, the dependence of the oxygen distribution on tumor growth and radiation‐induced shrinkage is investigated. In addition, the impact of three different reoxygenation processes is compared and tumor control probability (TCP) curves for a squamous cells carcinoma of the head and neck (HNSSC) are simulated under normoxic and hypoxic conditions. Results: The model describes the spatiotemporal behavior of the tumor on three different scales: (i) on the macroscopic scale, it describes tumor growth and shrinkage during radiation treatment, (ii) on a mesoscopic scale, it provides the cell density and vascular fraction for each voxel, and (iii) on the microscopic scale, the oxygen distribution may be obtained in terms of oxygen histograms. With increasing tumor size, the simulated tumors develop a hypoxic core. Within the model, tumor shrinkage was found to be significantly more important for reoxygenation than angiogenesis or decreased oxygen consumption due to an increased fraction of dead cells. In the studied HNSSC‐case, the TCD50 values (dose at 50% TCP) decreased from 71.0 Gy under hypoxic to 53.6 Gy under the oxic condition. Conclusions: The results obtained with the developed multiscale model are in accordance with expectations based on radiobiological principles and clinical experience. As the model is voxel‐based, radiological imaging methods may help to provide the required 3D‐characterization of the tumor prior to irradiation. For clinical application, the model has to be further validated with experimental and clinical data. If this is achieved, the model may be used to optimize fractionation schedules and dose distributions for the treatment of hypoxic tumors.