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At least 1,189 records · Page 66Linked to original sources
PubMed2005

Uncertainty and sensitivity analysis in assessment of the thyroid cancer risk related to Chernobyl fallout in Eastern France.

The increase in the thyroid cancer incidence in France observed over the last 20 years has raised public concern about its association with the 1986 nuclear power plant accident at Chernobyl. At the request of French authorities, a first study sought to quantify the possible risk of thyroid cancer associated with the Chernobyl fallout in France. This study suffered from two limitations. The first involved the lack of knowledge of spontaneous thyroid cancer incidence rates (in the absence of exposure), which was especially necessary to take their trends into account for projections over time; the second was the failure to consider the uncertainties. The aim of this article is to enhance the initial thyroid cancer risk assessment for the period 1991-2007 in the area of France most exposed to the fallout (i.e., eastern France) and thereby mitigate these limitations. We consider the changes over time in the incidence of spontaneous thyroid cancer and conduct both uncertainty and sensitivity analyses. The number of spontaneous thyroid cancers was estimated from French cancer registries on the basis of two scenarios: one with a constant incidence, the other using the trend observed. Thyroid doses were estimated from all available data about contamination in France from Chernobyl fallout. Results from a 1995 pooled analysis published by Ron et al. were used to determine the dose-response relation. Depending on the scenario, the number of spontaneous thyroid cancer cases ranges from 894 (90% CI: 869-920) to 1,716 (90% CI: 1,691-1,741). The number of excess thyroid cancer cases predicted ranges from 5 (90% UI: 1-15) to 63 (90% UI: 12-180). All of the assumptions underlying the thyroid cancer risk assessment are discussed.

Adolescent↗
PubMed2005

Model uncertainty and risk estimation for experimental studies of quantal responses.

Experimental animal studies often serve as the basis for predicting risk of adverse responses in humans exposed to occupational hazards. A statistical model is applied to exposure-response data and this fitted model may be used to obtain estimates of the exposure associated with a specified level of adverse response. Unfortunately, a number of different statistical models are candidates for fitting the data and may result in wide ranging estimates of risk. Bayesian model averaging (BMA) offers a strategy for addressing uncertainty in the selection of statistical models when generating risk estimates. This strategy is illustrated with two examples: applying the multistage model to cancer responses and a second example where different quantal models are fit to kidney lesion data. BMA provides excess risk estimates or benchmark dose estimates that reflects model uncertainty.

Animals↗
PubMed2006

Knowledge-based risk assessment under uncertainty for species invasion.

Management of invasive species depends on developing prevention and control strategies through comprehensive risk assessment frameworks that need a thorough analysis of exposure to invasive species. However, accurate exposure analysis of invasive species can be a daunting task because of the inherent uncertainty in invasion processes. Risk assessment of invasive species under uncertainty requires potential integration of expert judgment with empirical information, which often can be incomplete, imprecise, and fragmentary. The representation of knowledge in classical risk models depends on the formulation of a precise probabilistic value or well-defined joint distribution of unknown parameters. However, expert knowledge and judgments are often represented in value-laden terms or preference-ordered criteria. We offer a novel approach to risk assessment by using a dominance-based rough set approach to account for preference order in the domains of attributes in the set of risk classes. The model is illustrated with an example showing how a knowledge-centric risk model can be integrated with the dominance-based principle of rough set to derive minimal covering "if ... , then...," decision rules to reason over a set of possible invasion scenarios. The inconsistency and ambiguity in the data set is modeled using the rough set concept of boundary region adjoining lower and upper approximation of risk classes. Finally, we present an extension of rough set to evidence a theoretic interpretation of risk measures of invasive species in a spatial context. In this approach, the multispecies interactions in an invasion risk are approximated with imprecise probability measures through a combination of spatial neighborhood information of risk estimation in terms of belief and plausibility.

Animals↗
PubMed2001

A medical uncertainty principle.

Acquisition of diagnostic knowledge and improvement of patient health often are mutually exclusive goals. A perfect understanding of a disease process is only obtained through unrestricted testing that exposes the patient to adverse events and discomfort. In a patient completely undisturbed by test procedures, the physician remains ignorant about the nature of the disease. The interplay of diagnostic knowledge and patient health can be formulated in terms of an uncertainty principle, which allows one to mathematically derive the parameters that influence the balance between the two competing goals. The majority of clinical conditions permit the pursuit of perfect diagnostic knowledge. Occasional medical disasters occur because physicians underestimate the patient's functional reserve or the cumulative toll exerted by multiple tests. Especially in emergency situations or serious illness, the maximum amount of knowledge that can be achieved is far less than perfect. Under such circumstances, the best management option takes into account a "calculated" amount of diagnostic uncertainty.

Delivery of Health Care↗
PubMed1987

Psychiatric classification as a reflection of uncertainties.

Some problems of psychiatric classification are discussed and a shift of viewpoint is suggested. The existence of a natural and inherent structure for the class of psychiatric disorders is a matter of dispute, and it is postulated that a continuous search for such natural structure may be futile. Clinical psychiatry shall try to answer a series of questions which can be formulated as a hierarchy of uncertainties. It is now suggested that the existing pool of information used for psychiatric description may be a reflection of these uncertainties. The pool shall be analysed for core-syndromes which are the reliable components of an alternative classification.

Humans↗
PubMedDate not supplied

Vadose zone flow model uncertainty as conditioned on geophysical data.

An approach to estimating the uncertainty in model descriptions based on a landscape space to model space mapping concept is described. The approach is illustrated by an application making use of plot scale geophysical estimates of changes in water content profiles to condition a model of recharge to the Sherwood Sandstone Aquifer in the United Kingdom. It is demonstrated that the mapping is highly uncertain and that many different parameter sets give acceptable simulations of the observations. Multiple profile measurements over time offer only limited additional constraints on the mapping. The resulting mapping weights may be used to evaluate uncertainty in the predictions of vadose zone flow dynamics for the site.

Forecasting↗
PubMed1999

Uncertainty in risk characterization of weak carcinogens.

Epidemiologic inference is subject to uncertainty that is inherent to observational approaches. It was shown by the present analysis that an SMR of 1.4, as observed for 2,3,7,8-tetrachlorodibenzo-para-dioxin in the pooled analysis by IARC, would be observed in the absence of carcinogenic risk, if the smoking prevalence of cohort members was as low as 80%. It was also shown that a 1.4-fold increase in lung cancer risk among nonsmokers, which roughly corresponds to a daily consumption of one cigarette, might be extremely difficult to identify if the subjects are exposed to strong carcinogens such as those that result from cigarette smoking. On the other hand, a genotoxic agent that increases the lung cancer risk of smokers 1.4-fold, could increase the risk to nonsmokers by as much as 6.3-fold. In light of these uncertainties in epidemiologic inference, attempts to estimate the absolute risk in human populations by epidemiologic studies should be made with caution.

Carcinogens↗
PubMed1984

A design methodology for nonlinear systems containing parameter uncertainty.

A design methodology capable of dealing with nonlinear systems containing parameter uncertainty is presented. A generalized sensitivity analysis is incorporated which utilizes sampling of the parameter space and statistical inference. For a system with j adjustable and k nonadjustable parameters, this methodology (which includes an adaptive random search strategy) is used to determine the combination of j adjustable parameter values which maximizes the probability of the performance indices simultaneously satisfying design criteria given the uncertainty in the k nonadjustable parameters.

Algorithms↗
PubMed2003

Reducing uncertainties in volumetric image based deformable organ registration.

Applying volumetric image feedback in radiotherapy requires image based deformable organ registration. The foundation of this registration is the ability of tracking subvolume displacement in organs of interest. Subvolume displacement can be calculated by applying biomechanics model and the finite element method to human organs manifested on the multiple volumetric images. The calculation accuracy, however, is highly dependent on the determination of the corresponding organ boundary points. Lacking sufficient information for such determination, uncertainties are inevitable-thus diminishing the registration accuracy. In this paper, a method of consuming energy minimization was developed to reduce these uncertainties. Starting from an initial selection of organ boundary point correspondence on volumetric image sets, the subvolume displacement and stress distribution of the whole organ are calculated and the consumed energy due to the subvolume displacements is computed accordingly. The corresponding positions of the initially selected boundary points are then iteratively optimized to minimize the consuming energy under geometry and stress constraints. In this study, a rectal wall delineated from patient CT image was artificially deformed using a computer simulation and utilized to test the optimization. Subvolume displacements calculated based on the optimized boundary point correspondence were compared to the true displacements, and the calculation accuracy was thereby evaluated. Results demonstrate that a significant improvement on the accuracy of the deformable organ registration can be achieved by applying the consuming energy minimization in the organ deformation calculation.

Algorithms↗
PubMed2003

Limitations of a convolution method for modeling geometric uncertainties in radiation therapy. I. The effect of shift invariance.

Convolution methods have been used to model the effect of geometric uncertainties on dose delivery in radiation therapy. Convolution assumes shift invariance of the dose distribution. Internal inhomogeneities and surface curvature lead to violations of this assumption. The magnitude of the error resulting from violation of shift invariance is not well documented. This issue is addressed by comparing dose distributions calculated using the Convolution method with dose distributions obtained by Direct Simulation. A comparison of conventional Static dose distributions was also made with Direct Simulation. This analysis was performed for phantom geometries and several clinical tumor sites. A modification to the Convolution method to correct for some of the inherent errors is proposed and tested using example phantoms and patients. We refer to this modified method as the Corrected Convolution. The average maximum dose error in the calculated volume (averaged over different beam arrangements in the various phantom examples) was 21% with the Static dose calculation, 9% with Convolution, and reduced to 5% with the Corrected Convolution. The average maximum dose error in the calculated volume (averaged over four clinical examples) was 9% for the Static method, 13% for Convolution, and 3% for Corrected Convolution. While Convolution can provide a superior estimate of the dose delivered when geometric uncertainties are present, the violation of shift invariance can result in substantial errors near the surface of the patient. The proposed Corrected Convolution modification reduces errors near the surface to 3% or less.

Algorithms↗
PubMed2005

Incorporating organ movements in IMRT treatment planning for prostate cancer: minimizing uncertainties in the inverse planning process.

We investigate an off-line strategy to incorporate inter fraction organ movements in IMRT treatment planning. Nowadays, imaging modalities located in the treatment room allow for several CT scans of a patient during the course of treatment. These multiple CT scans can be used to estimate a probability distribution of possible patient geometries. This probability distribution can subsequently be used to calculate the expectation value of the delivered dose distribution. In order to incorporate organ movements into the treatment planning process, it was suggested that inverse planning could be based on that probability distribution of patient geometries instead of a single snapshot. However, it was shown that a straightforward optimization of the expectation value of the dose may be insufficient since the expected dose distribution is related to several uncertainties: first, this probability distribution has to be estimated from only a few images. And second, the distribution is only sparsely sampled over the treatment course due to a finite number of fractions. In order to obtain a robust treatment plan these uncertainties should be considered and minimized in the inverse planning process. In the current paper, we calculate a 3D variance distribution in addition to the expectation value of the dose distribution which are simultaneously optimized. The variance is used as a surrogate to quantify the associated risks of a treatment plan. The feasibility of this approach is demonstrated for clinical data of prostate patients. Different scenarios of dose expectation values and corresponding variances are discussed.

Artifacts↗
PubMed2006

A comparison of techniques for simulating set-up error and uncertainty in head and neck IMRT.

We have compared four computational methods for quantifying the effect of set-up error and uncertainty on delivered doses to targets and organs at risk in the intensity modulated radiation therapy treatment of head and neck cancer. These four methods were direct simulation, simple convolution, plus two modified convolution approaches. Discrepancies of up to 20% in the equivalent uniform dose (EUD) between direct simulation and simple convolution were estimated for the relatively superficial parotid gland at a systematic set-up error of 6 mm standard deviation and a random uncertainty of 2 mm standard deviation. Truncated convolution agreed with direct simulation to within 6% for all situations studied. However, of the four methods, only direct simulation can quantify the range of outcomes (EUD) associated with a finite number of courses and fractions. Our results are particularly relevant to the design of dose escalation studies in head and neck cancer.

Computer Simulation↗
PubMed2006

Dose uncertainties in photon pencil kernel calculations at off-axis positions.

The purpose of this study was to investigate the specific problems associated with photon dose calculations in points located at a distance from the central beam axis. These problems are related to laterally inhomogeneous energy fluence distributions and spectral variations causing a lateral shift in the beam quality, commonly referred to as off-axis softening (OAS). We have examined how the dose calculation accuracy is affected when enabling and disabling explicit modeling of these two effects. The calculations were performed using a pencil kernel dose calculation algorithm that facilitates modeling of OAS through laterally varying kernel properties. Together with a multi-source model that provides the lateral energy fluence distribution this generates the total dose output, i.e., the dose per monitor unit, at an arbitrary point of interest. The dose calculation accuracy was evaluated through comparisons with 264 measured output factors acquired at 5, 10, and 20 cm depth in four different megavoltage photon beams. The measurements were performed up to 18 cm from the central beam axis, inside square fields of varying size and position. The results show that calculations including explicit modeling of OAS were considerably more accurate, up to 4%, than those ignoring the lateral beam quality shift. The deviations caused by simplified head scatter modeling were smaller, but near the field edges additional errors close to 1% occurred. When enabling full physics modeling in the dose calculations the deviations display a mean value of -0.1%, a standard deviation of 0.7%, and a maximum deviation of -2.2%. Finally, the results were analyzed in order to quantify and model the inherent uncertainties that are present when leaving the central beam axis. The off-axis uncertainty component showed to increase with both off-axis distance and depth, reaching 1% (1 standard deviation) at 20 cm depth.

Algorithms↗
PubMedDate not supplied

Calculation of the uncertainty in the dose delivered during radiation therapy.

There is, inevitably, uncertainty in our knowledge of the dose at any point within an irradiated patient. A technique is presented for estimating this uncertainty by performing three parallel calculations, one using nominal values and the others extreme values of the parameters upon which the dose depends. Such calculations can be made with almost any algorithm for calculating dose. They result in an estimate, at some specified confidence level which is determined by the data used, of the range of dose likely at any point. Such calculations should help therapists to avert over- or underdosage which might not be evident in conventional calculations of the nominal dose.

Humans↗
PubMedDate not supplied

Sensitivity of helium beam-modulator design to uncertainties in biological data.

The goal in designing beam-modulating devices for heavy charged-particle therapy is to achieve uniform biological effects across the spread-peak region of the beam. To accomplish this, the linear-quadratic model for cell survival has been used to describe the biological response of the target cells to charged-particle radiation. In this paper, the sensitivity of the beam-modulator design in the high-dose region to the values of the linear-quadratic variables alpha and beta has been investigated for a 215-MeV/u helium beam, and implications for higher LET beams are discussed. The major conclusions of this work are that, for helium over the LET range of 2 to 16 keV/mu, uncertainties in measuring alpha and beta for a given cell type which are of the order of 20% or less have a negligible effect on the beam-modulator design (i.e., on the slope of the spread Bragg peak); uncertainties less than or equal to 10% in the dose-averaged LET at each depth are unimportant; and, if the linear-quadratic variables for the tumor differ from those used in the beam-modulator design by a constant factor between about 0.5 and 3, then the resultant nonuniformity in the photon-equivalent dose delivered to the tumor is within +/- 25%. It is also shown that for any ion, if the nominal values of alpha or beta used by the beam-modulator design program differ from their actual values by a constant factor, then the maximum errors possible in the beam-modulator design may be characterized by two limiting depth-dose curves such that the ratio of the dose at the proximal end of the spread Bragg curve to the dose at the distal end of the spread peak is given by alpha distal/alpha prox for the steepest curve, and square root of beta distal/beta prox for the flattest curve.

Cell Survival↗
PubMed1999

Quantization of setup uncertainties in 3-D dose calculations.

Random setup errors can lead to erroneous prediction of the dose distribution calculated for a patient using a static computed tomography (CT) model. Multiple recomputations of the dose distribution covering the range of expected patient positions provides a way to estimate a course of treatment. However, due to the statistical nature of the setup uncertainties, many courses of treatment must be simulated to calculate a distribution of average dose values delivered to a patient. Thus, direct simulation methods can be time consuming and may be impractical for routine clinical treatment planning applications. Methods have been proposed to efficiently calculate the distribution of average dose values via a convolution of the dose distribution (calculated on a static CT model) with a probability distribution function (generally Gaussian) that describes the nature of the uncertainty. In this paper, we extend the convolution-based calculation to calculate the standard deviation of potential outcomes sigmaD(x,y,z) about the distribution of average dose values, and we characterize the statistical significance of this quantity using the central limit theorem. For an example treatment plan based on a treatment protocol in use at our institution, we found that there is a 68% probability that the actual dose delivered to any point (x,y,z) will be within 3% of the average dose value at that point. The standard deviation also yields confidence limits on the dose distribution, and these may be used to evaluate treatment plan stability.

Algorithms↗
PubMed2000

Treatment planning algorithm corrections accounting for random setup uncertainties in fractionated stereotactic radiotherapy.

A number of relocatable head fixation systems have become commercially available or developed in-house to perform fractionated stereotactic radiotherapy (SRT) treatment. The uncertainty usually quoted for the target repositioning in SRT is over 2 mm, more than twice that of stereotactic radiosurgery (SRS) systems. This setup uncertainty is usually accounted for at treatment planning by outlining extra target margins to form the planning target volume (PTV). It was, however, shown by Lo et al. [Int. J. Radiat. Oncol., Biol., Phys. 34, 1113-1119 (1996)] that these extra margins partly offset the radiobiological advantages of SRT. The present paper considers dose calculations in SRT and shows that the dose predictions could be made at least as accurate as in SRS with no extra margins required. It is shown that the dose distribution from SRT can be calculated using the same algorithms as in SRS, with the measured off-axis ratios (OARs) replaced by "effective" OARs. These are obtained by convolving the probability density distribution of the isocenter positions (assumed to be normal) and the original OARs. An additional output correction factor has also been introduced accounting for the isocenter dose reduction (2.4% for a 7 mm collimator) due to the OARs "blurring." Another correction factor accommodates for the reduced (by 1% for 6 MV beam) dose rate at the isocenter due to x-ray absorption in the relocatable mask. Mean dose profiles and the standard deviations of the dose (STD) were obtained through simulating SRT treatment by a combination of normally distributed isocenters. These dose distributions were compared with those calculated using the convolution approach. Agreement of the dose distributions was within 1%. Since standard deviation reduces with the number of fractions, N, as STD/square root(N), the planning predictions in fractionated stereotactic radiotherapy can be made more accurate than in SRS by increasing N and using "effective" OARs along with corrected dose output.

Algorithms↗
PubMed2001

Maximum entropy approach for modeling random uncertainties in transient elastodynamics.

A new approach is presented for analyzing random uncertainties in dynamical systems. This approach consists of modeling random uncertainties by a nonparametric model allowing transient responses of mechanical systems submitted to impulsive loads to be predicted in the context of linear structural dynamics. The information used does not require the description of the local parameters of the mechanical model. The probability model is deduced from the use of the entropy optimization principle, whose available information is constituted of the algebraic properties related to the generalized mass, damping, and stiffness matrices which have to be positive-definite symmetric matrices, and the knowledge of these matrices for the mean reduced matrix model. An explicit construction and representation of the probability model have been obtained and are very well suited to algebraic calculus and to Monte Carlo numerical simulation in order to compute the transient responses of structures submitted to impulsive loads. The fundamental properties related to the convergence of the stochastic solution with respect to the dimension of the random reduced matrix model are analyzed. Finally, an example is presented.

Journal Article↗