John Tsimikas

Rate of insufficient samples for fine-needle aspiration for nonpalpable breast lesions in a multicenter clinical trial: The Radiologic Diagnostic Oncology Group 5 study

Radiologic Diagnostic Oncology Group 5 is a multicenter clinical trial designed to evaluate fine‐needle aspiration (FNA) of nonpalpable breast lesions performed by multiple operators using the same protocol.

On training RBF neural networks using input--output fuzzy clustering and particle swarm optimization

This paper elaborates on the use of particle swarm optimization in training Gaussian type radial basis function neural networks under the umbrella of input-output fuzzy clustering. The problem being investigated concerns the selection of basis function centers that contribute most in networks performance, given that the clustering process in the input space is guided by the clustering in the output space. To accomplish this task, we quantify the effect of the input space fuzzy partition upon networks square error in terms of an objective function that describes the ability of the partition to accurately reconstruct the input training samples. We, then, theoretically prove that the minimization of the above function acts to minimize an upper bound of the networks square error. Therefore, the resulting solution corresponds to a minimal square error, while at the same time it maintains the structure of the input data. Due to the peculiarity of the aforementioned objective function, we treat it as the fitness function used by the particle swarm optimizer. The proposed methodology encompasses three design steps. The first step implements an independent fuzzy clustering in the output space to obtain a set of cluster centers. In the second step, unlike other approaches, the above centers are directly involved in the estimation of the membership degrees in the input-output space. In the third step, these membership degrees are used by the particle swarm optimizer in order to obtain optimal values for the centers. To summarize, the novelty of our contribution lies in: (a) the way we handle the information flow from output to input space, and (b) the way we handle the effect of the input space partition upon networks performance. The experiments indicate that the fitness function decreases as the number of hidden node increases. Finally, a comparison between the proposed method and other sophisticated approaches shows its statistically significant superiority.

Null mutation for Macrophage Migration Inhibitory Factor (MIF) is associated with less aggressive bladder cancer in mice

BackgroundInflammatory cytokines may promote tumorigenesis. Macrophage migration inhibitory factor (MIF) is a proinflammatory cytokine with regulatory properties over tumor suppressor proteins involved in bladder cancer. We studied the development of bladder cancer in wild type (WT) and MIF knockout (KO) mice given N-butyl-N-(4-hydroxybutyl)-nitrosamine (BBN), a known carcinogen, to determine the role of MIF in bladder cancer initiation and progression.Methods5-month old male C57Bl/6 MIF WT and KO mice were treated with and without BBN. Animals were sacrificed at intervals up to 23 weeks of treatment. Bladder tumor stage and grade were evaluated by H&E. Immunohistochemical (IHC) analysis was performed for MIF and platelet/endothelial cell adhesion molecule 1 (PECAM-1), a measure of vascularization. MIF mRNA was analyzed by quantitative real-time polymerase chain reaction.ResultsPoorly differentiated carcinoma developed in all BBN treated mice by week 20. MIF WT animals developed T2 disease, while KO animals developed only T1 disease. MIF IHC revealed predominantly urothelial cytoplasmic staining in the WT control animals and a shift toward nuclear staining in WT BBN treated animals. MIF mRNA levels were 3-fold higher in BBN treated animals relative to controls when invasive cancer was present. PECAM-1 staining revealed significantly more stromal vessels in the tumors in WT animals when compared to KOs.ConclusionMuscle invasive bladder cancer with increased stromal vascularity was associated with increased MIF mRNA levels and nuclear redistribution. Consistently lower stage tumors were seen in MIF KO compared to WT mice. These data suggest that MIF may play a role in the progression to invasive bladder cancer.

Fuzzy vector quantization for image compression based on competitive agglomeration and a novel codeword migration strategy

The implementation of fuzzy clustering in the design process of vector quantizers faces three challenges. The first is the high computational cost. The second challenge arises because a vector quantizer is required to assign each training sample to only one cluster. However, such an aggressive interpretation of fuzzy clustering results to a crisp partition of inferior quality. The third one is the dependence on initialization. In this paper we develop a fuzzy clustering-based vector quantization algorithm that deals with the aforementioned problems. The algorithm utilizes a specialized objective function, which involves the c-means and the fuzzy c-means along with a competitive agglomeration term. The joint effect is a learning process where the number of codewords (i.e. cluster centers) affected by a specific training sample is gradually reducing and therefore, the number of distance calculations is also reducing. Thus, the computational cost becomes smaller. In addition, the partition is smoothly transferred from fuzzy to crisp conditions and there is no need to employ any aggressive interpretation of fuzzy clustering. The competitive agglomeration term refines large clusters from small and spurious ones. Then, contrary to the classical competitive agglomeration method, we do not discard the small clusters but instead migrate them close to large clusters, rendering more competitive. Thus, the codeword migration process uses the net effect of the competitive agglomeration and acts to further reduce the dependence on initialization in order to obtain a better local minimum. The algorithm is applied to grayscale image compression. The main simulation findings can be summarized as follows: (a) a comparison between the proposed method and other related approaches shows its statistically significant superiority, (b) the algorithm is a fast process, (c) the algorithm is insensitive with respect to its design parameters, and (d) the reconstructed images maintain high quality, which is quantified in terms of the distortion measure.

Inference in generalized linear regression models with a censored covariate

The problem of estimating the parameters in a generalized linear model when a covariate is subject to censoring is studied. A new method based on an estimating function approach is proposed. The method does not assume a parametric form for the distribution of the response given the regressors and is computationally simple. In the linear regression case, the proposed approach implies the use of mean imputation of the censored regressor. The use of flexible parametric models for the distribution of the covariate is employed. When survival time is considered as the covariate subject to censoring, the use of the generalized gamma distribution is explored, since it is considered as a platform distribution covering a wide variety of hazard rate shapes. The method can be further robustified by considering models of nonparametric nature typically used in survival analysis such as the logspline for the censored covariate. For models involving additional, fully observed, covariates the use of a generalized gamma accelerated failure time regression model is explored. In this setting, no parametric family assumption for the extra covariates is needed. The proposed approach is broader than likelihood based multiple imputation techniques. Moreover, even in cases with a known parametric form for the response distribution, the method can be considered a feasible alternative to likelihood based estimation. Simulation studies are conducted for continuous, binary and count data to evaluate the performance of the proposed method and to compare the estimates to standard ones. An application using a well known data set of a randomized placebo controlled trial of the drug D-penicillamine (DPCA) for the treatment of primary biliary cirrhosis (PBC) conducted at the Mayo Clinic is presented. Possible extensions of the method regarding the robustness as well as the type of censoring are also discussed.

Reml and best linear unbiased prediction in state space models

This article takes a hierarchical model approach to the estimation of state space models with diffuse initial conditions. An initial state is said to be diffuse when it cannot be assigned a proper prior distribution. In state space models this occurs either when fixed effects are present or when modelling nonstationarity in the state transition equation. Whereas much of the literature views diffuse states as an initialization problem, we follow the approach of Sallas and Harville (1981,1988) and incorporate diffuse initial conditions via noninformative prior distributions into hierarchical linear models. We apply existing results to derive the restricted loglike-lihood and appropriate modifications to the standard Kalman filter and smoother. Our approach results in a better understanding of De Jongs (1991) contributions. This article also shows how to adjust the standard Kalman filter, the fixed inter- val smoother and the state space model forecasting recursions, together with their mean square errors, ...

Survival estimation through the cumulative hazard function with monotone natural cubic splines

In this paper we explore the estimation of survival probabilities via a smoothed version of the survival function, in the presence of censoring. We investigate the fit of a natural cubic spline on the cumulative hazard function under appropriate constraints. Under the proposed technique the problem reduces to a restricted least squares one, leading to convex optimization. The approach taken in this paper is evaluated and compared via simulations to other known methods such as the Kaplan Meier and the logspline estimator. Our approach is easily extended to address estimation of survival probabilities in the presence of covariates when the proportional hazards model assumption holds. In this case the method is compared to a restricted cubic spline approach that involves maximum likelihood. The proposed approach can be also adjusted to accommodate left censoring.

Organizational Attributes of Practices Successful at a Disease Management Program

OBJECTIVE To assess the contribution of organizational factors to implementation of 3 asthma quality measures: enrollment in a disease management program, development of a written treatment plan, and prescription of severity-appropriate anti-inflammatory therapy. STUDY DESIGN A total of 138 pediatric clinicians and 247 office staff in 13 urban clinics and 23 nonurban private practices completed questionnaires about their practices organizational characteristics (eg, leadership, communication, perceived effectiveness, job satisfaction). RESULTS 94% of the clinicians and 92% of the office staff completed questionnaires. When adjusted for confounders, greater practice activity and perceived effectiveness in meeting family needs were associated with higher rates of enrollment in the Easy Breathing program, whereas higher scores for 3 organizational characteristics--communication timeliness, decision authority, and job satisfaction--were associated with both higher enrollment and a greater number of written treatment plans. None of the organizational characteristics was associated with greater use of anti-inflammatory therapy. CONCLUSIONS Three organizational characteristics predicted 2 quality asthma measures: use of a disease management program and creation of a written asthma treatment plan. If these organizational characteristics were amenable to change, then our findings could help focus interventions in areas of effective and acceptable organizational change.

Analysis of Multi-Unit Variance Components Models with State Space Profiles

We apply the Kalman Filter to the analysis of multi-unit variance components models where each units response profile follows a state space model. We use mixed model results to obtain estimates of unit-specific random effects, state disturbance terms and residual noise terms. We use the signal extraction approach to smooth individual profiles. We show how to utilize the Kalman Filter to efficiently compute the restricted loglikelihood of the model. For the important special case where each units response profile follows a continuous structural time series model with known transition matrix we derive an EM algorithm for the restricted maximum likelihood (REML) estimation of the variance components. We present details for the case where individual profiles are modeled as local polynomial trends or polynomial smoothing splines.

Smooth ROC curves and surfaces for markers subject to a limit of detection using monotone natural cubic splines

The use of ROC curves in evaluating a continuous or ordinal biomarker for the discrimination of two populations is commonplace. However, in many settings, marker measurements above or below a certain value cannot be obtained. In this paper, we study the construction of a smooth ROC curve (or surface in the case of three populations) when there is a lower or upper limit of detection. We propose the use of spline models that incorporate monotonicity constraints for the cumulative hazard function of the marker distribution. The proposed technique is computationally stable and simulation results showed a satisfactory performance. Other observed covariates can be also accommodated by this spline-based approach.