Aris Perperoglou

Relationship of cell proliferation (Ki-67) to 99mTc-(V)DMSA uptake in breast cancer

IntroductionThe aim of the present study was to identify the relationships between the uptake of radiotracers – namely pentavalent dimercaptosuccinic acid [(V)DMSA] and sestamibi (MIBI) – and the following parameters in primary breast cancer: steroid receptor concentrations (i.e. estrogen receptor [ER] and progesterone receptor [PR]), Ki-67 expression, tumor size, tumor grade, age, and levels of expression of p53 and c-erbB-2. In addition, by multivariate regression analysis, we further isolated those factors with independent associations with (V)DMSA and/or MIBI uptake in primary breast cancer.MethodsThirty-four patients with histologically confirmed breast carcinoma underwent preoperative scintimammography with technetium-99m (99mTc)-(V)DMSA and/or 99mTc-MIBI in consecutive sessions 10 and 60 min after administration of 925–1110 MBq of each radiotracer. The tumor-to-background ratio was calculated and correlated with the presence of ER, PR, Ki-67, tumor size, tumor grade, p53, and c-erbB-2. ER, PR, p53, and c-erbB-2 were determined immunohistochemically. The analysis included tumor-to-background ratio of (V)DMSA and MIBI uptake as dependent and all of the other parameters as independent variables.ResultsCorrelation was positive between Ki-67 and (V)DMSA (r = 0.37 at 10 min, P = 0.038; r = 0.42 at 60 min, P = 0.018) and inverse between PR and (V)DMSA uptake (r = -0.46 at 10 min, P = 0.010; r = -0.51 at 60 min, P = 0.003). Multivariate regression analysis demonstrated a positive correlation between Ki-67 and (V)DMSA at 60 min (P = 0.045). Ki-67 was not significantly correlated with MIBI uptake, whereas tumor size was positively correlated with MIBI uptake at 60 min both in univariate (r = 0.45, P = 0.027) and multivariate analysis (P = 0.024). Negative correlations were observed between (V)DMSA uptake and ER, as well as between ER/PR and MIBI uptake, but these were not significant.ConclusionKi-67 appears to represent the major independent factor affecting (V)DMSA uptake in breast cancer. Tumor size was the only independent parameter influencing MIBI uptake in breast cancer. (V)DMSA appears to have an advantage over MIBI in that it can be used to visualize tumors with intense proliferative activity, and thus it can identify those tumors that are more aggressive.

A feature selection method for classification within functional genomics experiments based on the proportional overlapping score.

BackgroundMicroarray technology, as well as other functional genomics experiments, allow simultaneous measurements of thousands of genes within each sample. Both the prediction accuracy and interpretability of a classifier could be enhanced by performing the classification based only on selected discriminative genes. We propose a statistical method for selecting genes based on overlapping analysis of expression data across classes. This method results in a novel measure, called proportional overlapping score (POS), of a feature’s relevance to a classification task.ResultsWe apply POS, along‐with four widely used gene selection methods, to several benchmark gene expression datasets. The experimental results of classification error rates computed using the Random Forest, k Nearest Neighbor and Support Vector Machine classifiers show that POS achieves a better performance.ConclusionsA novel gene selection method, POS, is proposed. POS analyzes the expressions overlap across classes taking into account the proportions of overlapping samples. It robustly defines a mask for each gene that allows it to minimize the effect of expression outliers. The constructed masks along‐with a novel gene score are exploited to produce the selected subset of genes.

93 Ejection Fraction by Cardiovascular Magnetic Resonance Predicts Adverse Outcomes Post Aortic Valve Replacement

Introduction Predicting prognosis following aortic valve replacement (AVR) in patients with aortic stenosis (AS) remains challenging. Current guidelines recommend that surgery should be offered when ejection fraction (EF) is <50%. We sought to investigate the prognostic significance of EF calculated by cardiovascular magnetic resonance (CMR) in the long term survival of patients following AVR. Methods 80 patients (69 ± 11 years old at time of surgery; 55 male) scheduled for AVR underwent CMR assessment. 52 patients had severe AS (area <1cm2), 28 patients had moderate AS (area 1.0–1.5cm2) and other qualifying reasons for AVR. 44 patients had additional coronary artery disease.Patients were categorised into three groups according to EF prior to surgery: Group 1 (EF <50%; n = 26), Group 2 (EF of 50–70%; n = 26) and Group 3 (EF >70%; n = 28). A median 5.0 ± 1.8 years follow-up was completed using the National Strategic Tracing Scheme and hospital notes. Results Univariate analysis of all cause mortality using the Kaplan-Meier estimator demonstrated significantly higher mortality in patients with Group 1 (EF <50%) compared to those in group 3 (EF >70%; .03).There was no statistical difference between group 2 (EF of 50–70%) and the remaining 2 groups. Abstract 93 Figure 1 Kaplan-Meier survival curve of all cause mortality in Group 1 (EF <50%), Group 2 (EF 50–70%) and Group 3 (EF >70%) Conclusion Pre-operative EF is a significant predictor of mortality following AVR. Patients with EF <50% have the worst prognosis whereas those with EF >70% have the best prognosis. We aim to incease the sample size to determine whether a progressive decrease in EF per se even when above 50% should initiate consideration for AVR.

A fast routine for fitting Cox models with time varying effects of the covariates.

The S-plus and R statistical packages have implemented a counting process setup to estimate Cox models with time varying effects of the covariates. The data set has to be re-arranged in a repeated measurement setting: the time is divided into small time intervals where a single event occurs and for each time interval, the covariate values and outcome in the interval for each subject still under observation are stacked to a large data set. This is the known (Tstart,Tstop] algorithm implemented in Therneaus Survival library (S-plus), which has been ported into an R package by Thomas Lumley. However, the expansion of a data set leads to a larger set, which can be hard to handle even with fast modern computers. We propose the use of a fast and efficient algorithm, written in R, which works on the original data without the use of an expansion. The computations are done on the original data set, with significant less memory resources used. This improves the computational time by orders of magnitude. The algorithm can also fit reduced rank Cox models with time varying effects. We illustrate the method on a large data set of 2433 breast cancer patients, a smaller study of 358 ovarian cancer patients, and compare the computational times on simulated data of up to 10,000 cases with SAS proc phreg and survival package in R. For larger data sets our algorithm was several times faster, and was able to handle larger data sets then SAS and R.

Cough affects Quality of Life in asthmatic children aged 8-14 more than other asthma symptoms

BACKGROUND Asthma may influence childrens health-related quality of life (QoL) differently by various symptoms, at different severity. The primary aim of this study was to evaluate the QoL in children with asthma and describe the impact of each asthma symptom on the childs well-being at different severity levels. MATERIAL AND METHODS Two hundred randomly selected children and one of their parents who consulted an outpatient asthma clinic, participated in the study. Qol was assessed with DISABKIDS-Smiley measure for children aged 4-7 years and with DISABKIDS DCGM-37 and Asthma Module for children 8-14 year old. RESULTS Most of the children suffered from mild or moderate persistent asthma. Children with uncontrolled asthma stated lower QoL compared to partly controlled or controlled in both age groups (p < 0.05 in all domains). Cough appeared to affect QoL of 8-14 year olds more than other symptoms, especially in girls. In younger children, sex (boys, p = 0.039), age (p = 0.045), proxy sex (father, p = 0.048), frequency of doctor visits (4-6 months, p = 0.001), use of beta-2 agonists (p = 0.007) and fathers smoking habits (p = 0.015) were associated with the QoL of coughing children but no correlation between cough and QoL was detected. In the 8-14 year age group coughers reported lower QoL compared to their counterparts; moreover, cough was found to affect QoL more than other symptoms (p < 0.05 in all domains). CONCLUSIONS Cough has a direct effect on asthmatic childrens QoL but there is still an obvious need for research to reveal all the determinats of this effect.

Cox models with dynamic ridge penalties on time-varying effects of the covariates

Analysis of long-term follow-up survival studies require more sophisticated approaches than the proportional hazards model. To account for the dynamic behaviour of fixed covariates, penalized Cox models can be employed in models with interactions of the covariates and known time functions. In this work, I discuss some of the suggested methods and emphasize on the use of a ridge penalty in survival models. I review different strategies for choosing an optimal penalty weight and argue for the use of the computationally efficient restricted maximum likelihood (REML)-type method. A ridge penalty term can be subtracted from the likelihood when modelling time-varying effects in order to control the behaviour of the time functions. I suggest using flexible time functions such as B-splines and constrain the behaviour of these by adding proper penalties. I present the basic methods and illustrate different penalty weights in two different datasets.

Midwall Fibrosis and 5-Year Outcome in Moderate and Severe Aortic Stenosis

Aortic stenosis (AS) is characterized by progressive narrowing of the valve and the hypertrophic response of the left ventricle (LV) that ensues [(1)][1]. Although initially adaptive, the hypertrophic response ultimately decompensates and patients transition from hypertrophy to heart failure,

Skin colonisation at the catheter exit site is strongly associated with catheter colonisation and catheter-related sepsis

The commonest mode of catheter colonisation is via the extraluminal route with skin bacteria. Catheter‐related sepsis causes significant mortality and morbidity in neonates. Our aim was to study the relationships between culture‐positive catheter exit site skin swabs, percutaneous central venous catheter segments and blood to determine the magnitude of associations between exit site skin colonisation, catheter colonisation and catheter‐related sepsis.

Ensemble of a subset of kNN classifiers

Combining multiple classifiers, known as ensemble methods, can give substantial improvement in prediction performance of learning algorithms especially in the presence of non-informative features in the data sets. We propose an ensemble of subset of kNN classifiers, ESkNN, for classification task in two steps. Firstly, we choose classifiers based upon their individual performance using the out-of-sample accuracy. The selected classifiers are then combined sequentially starting from the best model and assessed for collective performance on a validation data set. We use bench mark data sets with their original and some added non-informative features for the evaluation of our method. The results are compared with usual kNN, bagged kNN, random kNN, multiple feature subset method, random forest and support vector machines. Our experimental comparisons on benchmark classification problems and simulated data sets reveal that the proposed ensemble gives better classification performance than the usual kNN and its ensembles, and performs comparable to random forest and support vector machines.

An Ensemble of Optimal Trees for Class Membership Probability Estimation

Machine learning methods can be used for estimating the class membership probability of an observation. We propose an ensemble of optimal trees in terms of their predictive performance. This ensemble is formed by selecting the best trees from a large initial set of trees grown by random forest. A proportion of trees is selected on the basis of their individual predictive performance on out-of-bag observations. The selected trees are further assessed for their collective performance on an independent training data set. This is done by adding the trees one by one starting from the highest predictive tree. A tree is selected for the final ensemble if it increases the predictive performance of the previously combined trees. The proposed method is compared with probability estimation tree, random forest and node harvest on a number of bench mark problems using Brier score as a performance measure. In addition to reducing the number of trees in the ensemble, our method gives better results in most of the cases. The results are supported by a simulation study.