18,557 research outputs found
Modelling the impact of treatment uncertainties in radiotherapy
Uncertainties are inevitably part of the radiotherapy process. Uncertainty in the dose deposited in the tumour exists due to organ motion, patient positioning errors, fluctuations in machine output, delineation of regions of interest, the modality of imaging used, and treatment planning algorithm assumptions among others; there is uncertainty in the dose required to eradicate a tumour due to interpatient variations in patient-specific variables such as their sensitivity to radiation; and there is uncertainty in the dose-volume restraints that limit dose to normal tissue.
This thesis involves three major streams of research including investigation of the actual dose delivered to target and normal tissue, the effect of dose uncertainty on radiobiological indices, and techniques to display the dose uncertainty in a treatment planning system. All of the analyses are performed with the dose distribution from a four-field box treatment using 6 MV photons. The treatment fields include uniform margins between the clinical target volume and planning target volume of 0.5 cm, 1.0 cm, and 1.5 cm. The major work is preceded by a thorough literature review on the size of setup and organ motion errors for various organs and setup techniques used in radiotherapy.
A Monte Carlo (MC) code was written to simulate both the treatment planning and delivery phases of the radiotherapy treatment. Using MC, the mean and the variation in treatment dose are calculated for both an individual patient and across a population of patients. In particular, the possible discrepancy in tumour position located from a single CT scan and the magnitude of reduction in dose variation following multiple CT scans is investigated. A novel convolution kernel to include multiple pretreatment CT scans in the calculation of mean treatment dose is derived. Variations in dose deposited to prostate and rectal wall are assessed for each of the margins and for various magnitudes of systematic and random error, and penumbra gradients.
The linear quadratic model is used to calculate prostate Tumour Control Probability (TCP) incorporating an actual (modelled) delivered prostate dose. The Kallman s-model is used to calculate the normal tissue complication probability (NTCP), incorporating actual (modelled) fraction dose in the deforming rectal wall. The impact of each treatment uncertainty on the variation in the radiobiological index is calculated for the margin sizes.Thesis (Ph.D.)--Department of Physics and Mathematical Physics, 2002
Different aspects of the lived experience of intellectual disabilities
The final, definitive version of this article has been published in the Journal, Journal of Intellectual Disabilities: JOID, Vol.14 Issue 1, 2010, Copyright SAGE Publications Ltd on SAGE Journals Online: http://online.sagepub.com/In this issue of Journal of Intellectual Disabilities the first three papers present different aspects of the lived experience of intellectual disabilities, and each of these is undertaken through the use of different methodological approaches. These experiences are the predominant feature in this issue, and are expressed through mothers and informal carers as well as through people with learning disabilities themselves. This is followed by a ‘case note’ follow up study concerning sterilisation of women with intellectual disabilities. The final paper moves to a ‘scoping review’ based on existing literature to explore the role, if any, for social care practitioners in the process of annual health checks for adults with learning disabilities in England.Peer reviewedFinal Accepted Versio
Nonlinear microwave response of MgB2
We calculate the intrinsic nonlinear microwave response of the two gap
superconductor MgB2 in the clean and dirty limits. Due to the small value of
the pi band gap, the nonlinear response at low temperatures is larger than for
a single gap Bardeen-Cooper-Schrieffer (BCS) s-wave superconductor with a
transition temperature of 40 K. Comparing this result with the intrinsic
nonlinear d-wave response of YBa2Cu3O7 (YBCO) we find a comparable response at
temperatures around 20 K. Due to its two gap nature, impurity scattering in
MgB2 can be used to reduce the nonlinear response if the scattering rate in the
pi band is made larger than the one in the sigma band.Comment: 4 pages, 4 figure
Comparative Monte Carlo Efficiency by Monte Carlo Analysis
We propose a modified power method for computing the subdominant eigenvalue
of a matrix or continuous operator. Here we focus on defining
simple Monte Carlo methods for its application. The methods presented use
random walkers of mixed signs to represent the subdominant eigenfuction.
Accordingly, the methods must cancel these signs properly in order to sample
this eigenfunction faithfully. We present a simple procedure to solve this sign
problem and then test our Monte Carlo methods by computing the of
various Markov chain transition matrices. We first computed for
several one and two dimensional Ising models, which have a discrete phase
space, and compared the relative efficiencies of the Metropolis and heat-bath
algorithms as a function of temperature and applied magnetic field. Next, we
computed for a model of an interacting gas trapped by a harmonic
potential, which has a mutidimensional continuous phase space, and studied the
efficiency of the Metropolis algorithm as a function of temperature and the
maximum allowable step size . Based on the criterion, we
found for the Ising models that small lattices appear to give an adequate
picture of comparative efficiency and that the heat-bath algorithm is more
efficient than the Metropolis algorithm only at low temperatures where both
algorithms are inefficient. For the harmonic trap problem, we found that the
traditional rule-of-thumb of adjusting so the Metropolis acceptance
rate is around 50% range is often sub-optimal. In general, as a function of
temperature or , for this model displayed trends defining
optimal efficiency that the acceptance ratio does not. The cases studied also
suggested that Monte Carlo simulations for a continuum model are likely more
efficient than those for a discretized version of the model.Comment: 23 pages, 8 figure
Supervised Classification Using Sparse Fisher's LDA
It is well known that in a supervised classification setting when the number
of features is smaller than the number of observations, Fisher's linear
discriminant rule is asymptotically Bayes. However, there are numerous modern
applications where classification is needed in the high-dimensional setting.
Naive implementation of Fisher's rule in this case fails to provide good
results because the sample covariance matrix is singular. Moreover, by
constructing a classifier that relies on all features the interpretation of the
results is challenging. Our goal is to provide robust classification that
relies only on a small subset of important features and accounts for the
underlying correlation structure. We apply a lasso-type penalty to the
discriminant vector to ensure sparsity of the solution and use a shrinkage type
estimator for the covariance matrix. The resulting optimization problem is
solved using an iterative coordinate ascent algorithm. Furthermore, we analyze
the effect of nonconvexity on the sparsity level of the solution and highlight
the difference between the penalized and the constrained versions of the
problem. The simulation results show that the proposed method performs
favorably in comparison to alternatives. The method is used to classify
leukemia patients based on DNA methylation features
Laplace Approximated EM Microarray Analysis: An Empirical Bayes Approach for Comparative Microarray Experiments
A two-groups mixed-effects model for the comparison of (normalized)
microarray data from two treatment groups is considered. Most competing
parametric methods that have appeared in the literature are obtained as special
cases or by minor modification of the proposed model. Approximate maximum
likelihood fitting is accomplished via a fast and scalable algorithm, which we
call LEMMA (Laplace approximated EM Microarray Analysis). The posterior odds of
treatment gene interactions, derived from the model, involve shrinkage
estimates of both the interactions and of the gene specific error variances.
Genes are classified as being associated with treatment based on the posterior
odds and the local false discovery rate (f.d.r.) with a fixed cutoff. Our
model-based approach also allows one to declare the non-null status of a gene
by controlling the false discovery rate (FDR). It is shown in a detailed
simulation study that the approach outperforms well-known competitors. We also
apply the proposed methodology to two previously analyzed microarray examples.
Extensions of the proposed method to paired treatments and multiple treatments
are also discussed.Comment: Published in at http://dx.doi.org/10.1214/10-STS339 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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