1,721,032 research outputs found
The many faces of mathematical modelling in oncology
No full textThe application of modelling to solve problems in biology and medicine, and specifically in oncology and radiation therapy, is increasingly established and holds big promise. We provide an overview of the basic concepts of the field and its current state, along with new tools available and future directions for research. We will outline radiobiology models, examples of other anticancer therapy models, multiscale modelling, and we will discuss mechanistic and phenomenological approaches to modelling
Quantifying effects of lead shielding in electron beams: A Monte Carlo study
No full textLead shielding in contact with the patient's skin is often encountered in radiotherapy with electron beams. The influence of the lead shielding on dose distributions in the patient cannot fully be assessed using modern treatment planning systems. In this work the problem of quantifying the effect of lead shielding on dose distributions is addressed. Monte Carlo dose calculations were performed in a half-blocked water phantom shielded by lead, using a realistic model for the fluence of an electron linear accelerator. Electron beam energies of 6-20 MeV and lead thicknesses of 1-7 mm are used for 10 × 10 cm2 and 5 × 5 cm2 fields. The perturbation of the particle fluence and dose distributions in water introduced by the lead shielding is quantified. The effect of oblique electron beams on the dose perturbation is shown. A fictitious clinical example, the shielding of an eye in electron beam treatment, is used to demonstrate the usefulness of Monte Carlo based treatment planning algorithms that can incorporate the effects of lead shielding
Comparative analysis of dose volume histogram reduction algorithms for normal tissue complication probability calculations
No full textA model for estimating radiotherapy treatment outcome through the probability of damage to normal tissue and the probability of tumour control is a useful tool for treatment plan optimization, dose escalation strategies and other currently used procedures in radiation oncology. Normal tissue complication estimation (NTCP) is here analysed from the point of view of the reliability and internal consistency of the most popular model. Five different dose volume histogram (DVH) reduction algorithms, applied to the Lyman model for NTCP calculation, were analysed and compared. The study was carried out for sets of parameters corresponding to quite different expected dose-response relationships. In particular, we discussed the dependence of the models on the parameters and on the dose bin size in the DVH. The sensitivity of the different reduction schemes to dose inhomogeneities was analysed, using a set of simple DVHs representing typical situations of radiation therapy routine. Significant differences were substantiated between the various reduction methods regarding the sensitivity to the degree of irradiation homogeneity, to the model parameters and to the close bin size. Structural aspects of the reduction formalism allowed an explanation for these differences. This work shows that DVH reduction for NTCP calculation has still to be considered as avery delicate field and used with extreme care, especially for clinical applications, at least until the actual formulations are tuned against strong clinical data
Monte Carlo dose calculations and radiobiological modelling: Analysis of the effect of the statistical noise of the dose distribution on the probability of tumour control
No full textThe aim of this work is to investigate the influence of the statistical fluctuations of Monte Carlo (MC) dose distributions on the dose volume histograms (DVHs) and radiobiological models, in particular the Poisson model for tumour control probability (tcp). The MC matrix is characterized by a mean dose in each scoring voxel, d, and a statistical error on the mean dose, σ(d); whilst the quantities d and σ(d) depend on many statistical and physical parameters, here we consider only their dependence on the phantom voxel size and the number of histories from the radiation source. Dose distributions from high-energy photon beams have been analysed. It has been found that the DVH broadens when increasing the statistical noise of the dose distribution, and the tcp calculation systematically underestimates the real tumour control value, defined here as the value of tumour control when the statistical error of the dose distribution tends to zero. When increasing the number of energy deposition events, either by increasing the voxel dimensions or increasing the number of histories from the source, the DVH broadening decreases and tcp converges to the 'correct' value. It is shown that the underestimation of the tcp due to the noise in the dose distribution depends on the degree of heterogeneity of the radiobiological parameters over the population; in particular this error decreases with increasing the biological heterogeneity, whereas it becomes significant in the hypothesis of a radiosensitivity assay for single patients, or for subgroups of patients. It has been found, for example, that when the voxel dimension is changed from a cube with sides of 0.5 cm to a cube with sides of 0.25 cm (with a fixed number of histories of 108 from the source), the systematic error in the tcp calculation is about 75% in the homogeneous hypothesis, and it decreases to a minimum value of about 15% in a case of high radiobiological heterogeneity. The possibility of using the error on the tcp to decide how many histories to run for a given voxel size is also discussed
Dosimetric features of linac head and phantom scattered radiation outside the clinical photon beam: experimental measurements and comparison with treatment planning system calculations.
No full textBACKGROUND AND PURPOSE: Dosimetric measurements and treatment planning system (TPS) calculations in the region outside the clinical photon beams have been investigated. The aim was to estimate the calculation accuracy of a specific TPS in areas that are becoming increasingly relevant with the advent of new technologies, such as, for example, intensity modulation radiation therapy. MATERIALS AND METHODS: Measurements were performed on two different linacs to obtain, separately, the head scatter (electrons and photons), the transmission below the jaws and the phantom scatter outside the primary beam for different photon energies, distances from the field edge and field sizes. Calculations with a commercial TPS (Helax TMS) were then obtained and compared with these measurements. RESULTS: In general, reasonable agreement between calculations and measurements was obtained (1-2%), especially for photon scattering (head and phantom). Nevertheless, some discrepancies were found in the electron contamination computation, due probably to the approximations and assumptions made in the TPS calculation algorithm. CONCLUSIONS: The analyzed TPS presented good results, but for some particular clinical cases and moreover for advanced techniques such as intensity modulated radiation therapy, the calculation behaviour with respect to measurements and patient dose delivery should be carefully evaluated
Gene expression and hypoxia in breast cancer
No full textHypoxia is a feature of most solid tumors and is associated with poor prognosis in several cancer types, including breast cancer. The master regulator of the hypoxic response is the Hypoxia-inducible factor 1a (HIF--1a). It is becoming clear that HIF-1a expression alone is not a reliable marker of tumor response to hypoxia, and recent studies have focused on determining gene and microRNA (miRNA) signatures for this complex process. The results of these studies are likely to pave the way towards the development of a robust hypoxia signature for breast and other cancers that will be useful for diagnosis and therapy. In this review, we outline the existing markers of hypoxia and recently identified gene and miRNA expression signatures, and discuss their potential as prognostic and predictive biomarkers. We also highlight how the hypoxia response is being targeted in the development of cancer therapies. © 2011 BioMed Central Ltd
An analysis of the relationship between radiosensitivity and volume effects in tumor control probability modeling
No full textThe dependence of local tumor control probability (tcp) on tumor volume is analyzed and discussed with the help of radiobiological modeling; in particular the impact of possible correlations between mean tumor radiosensitivity and tumor dimensions on the tcp volume dependence is explored. The linear-quadratic Poissonian tumor control probability (tcp) model was modified to account for the possible dependence of clonogenic cell density and radiosensitivity parameters on tumor volume; then the original and modified versions of the model were fitted to published clinical and laboratory tumor control data. These different versions of the tcp model often fitted tumor control data equally well, because of the high degree of correlation between the parameters. Nevertheless the results were very different from a physical point of view and we suggest that sometimes it is possible to choose between equally good fits on the basis of physical considerations. Possible links between the volume dependence of the mean radiosensitivity and the degree of tumor hypoxia were also analyzed through a comparison of the results of the tcp fit to published measurements of oxygen tension in tumors. (C) 2000 American Association of Physicists in Medicine
Assessment of tumour hypoxia for prediction of response to therapy and cancer prognosis
No full textTumour cells exploit both genetic and adaptive means to survive and proliferate in hypoxic microenvironments, resulting in the outgrowth of more aggressive tumour cell clones. Direct measurements of tumour oxygenation, and surrogate markers of the hypoxic response in tumours (for instance, hypoxia inducible factor-1α, carbonic anhydrase 9 and glucose transporter-1) are well-established prognostic markers in solid cancers. However, individual markers do not fully capture the complex, dynamic and heterogeneous hypoxic response in cancer. To overcome this, expression profiling has been employed to identify hypoxia signatures in cohorts or models of human cancer. Several of these hypoxia signatures have demonstrated prognostic significance in independent cancer datasets. Nevertheless, individual hypoxia markers have been shown to predict the benefit from hypoxia-modifying or anti-angiogenic therapies. This review aims to discuss the clinical impact of translational work on hypoxia markers and to explore future directions for research in this area. © 2009 The Authors Journal compilation © 2010 Foundation for Cellular and Molecular Medicine Blackwell Publishing Ltd
Concerning Roberts and Hendry IJROBP 1998;41:689-699
No full textLetter to discuss an issue with published mathematical mode
- …
