Dataset for reporting of prostate carcinoma in radical prostatectomy specimens: recommendations from the International Collaboration on Cancer Reporting

This project was designed to harmonise the Royal College of Pathologists, College of American Pathologists and Royal College of Pathologists of Australasia datasets, checklists and structured reporting protocols for examination of radical prostatectomy specimens, with the aim of producing a common, internationally agreed, evidence‐based dataset for prostate cancer reporting. The International Collaboration on Cancer Reporting prostate cancer expert review panel analysed the three existing datasets, identifying concordant items and classified these data elements as ‘required’ (mandatory) or ‘recommended’ (non‐mandatory), on the basis of the published literature up to August 2011. Required elements were defined as those that have agreed evidentiary support at NHMRC level III‐2 or above. Consensus response values were formulated for each item. Twelve concordant pathology data elements were identified, and, on review, all but one were included as required elements for tumour staging, grading, or prediction of prognosis. There was minor discordance between the three existing datasets for another eight items, with two of these being added to the required data set. Another 11 elements with a lesser level of evidentiary support were included in the recommended dataset. This process was found to be an efficient method for producing an evidence‐based dataset for prostate cancer. Such internationally agreed datasets should facilitate meaningful comparison of benchmarking data, epidemiological studies, and clinical trials.     

Economic Evaluation of the effect of Quitting Smoking on Weight Gains: Evidence from the United Kingdom

ObjectiveThis article estimated the causal effect of quitting smoking on body weight gains in the United Kingdom to evaluate whether savings in health costs deriving from smoking prevention and its related diseases are greater than the costs associated with increased obesity.MethodsWe used a longitudinal data set extracted from two waves (2004–2006) of the British Household Panel Survey, which includes information on smoking and a large number of sociodemographic variables. We modeled the effect of quitting smoking on body weight accounting for heterogeneous responses from individuals belonging to different clinical classes of body mass index (BMI) (i.e., overweight and obese individuals). National Health Service costs associated with smoking were then used to implement a cost-benefit analysis, comparing the advantages of smoking reductions with the costs associated with increased obesity.ResultsThe BMI was found to increase by 0.26 points for quitters compared with those who continued to smoke. The estimated BMI increase was larger for overweight (0.49 points) and obese (0.76 points) people. This result does not change when different control groups are examined. From an economic perspective, the National Health Service cost reductions attributable to quitting smoking were £156.81 million whereas the lost benefit for unintended increases in body weight was £24.07 million.ConclusionsThis article found that the health benefits associated with quitting smoking are greater than the costs associated with increased overweight and obesity.     

Significance Analysis of ROC Indices for Comparing Diagnostic Markers: Applications to Gene Microarray Data

Abstract

A common objective in microarray experiments is to select genes that are differentially expressed between two classes (two treatment groups). Selection of differentially expressed genes involves two steps. The first step is to calculate a discriminatory score that will rank the genes in order of evidence of differential expressions. The second step is to determine a cutoff for the ranked scores. Summary indices of the receiver operating characteristic (ROC) curve provide relative measures for a ranking of differential expressions. This article proposes using the hypothesis-testing approach to compute the raw p-values and/or adjusted p-values for three ROC discrimination measures. A cutoff p-value can be determined from the (ranked) p-values or the adjusted p-values to select differentially expressed genes. To quantify the degree of confidence in the selected top-ranked genes, the conditional false discovery rate (FDR) over the selected gene set and the “Type I” (false positive) error probability for each selected gene are estimated. The proposed approach is applied to a public colon tumor data set for illustration. The selected gene sets from three ROC summary indices and the commonly used two-sample t-statistic are applied to the sample classification to evaluate the predictability of the four discrimination measures.     

中药开放数据集建设

数据挖掘技术应用于中药学研究中产生了一系列研究成果.但是,由于使用的数据集不公开,导致很多研究成果缺乏可验证性和可比较性,统计结果也难于评价.本研究基于<中华本草>中的文献资源,开发了一套中药数据集,并对数据收集、整理的过程和使用方法都进行了详细说明.该数据集为中药信息学研究人员提供了一套可以免费获取的开放数据资源,有利于中药信息学的推广发展.     

The incidence of implant fractures after total hip arthroplasty

Purpose

Implant fractures after total hip arthroplasty (THA) are considered as rare in clinical practice. Nevertheless they are relevant complications for patients, physicians, and the public health system leading to high socioeconomic burdens. The aim of this study was to assess the incidence of fractures after THAs in a comparative analysis of clinical studies and worldwide arthroplasty register datasets.

Methods

We calculated the pooled incidence of revision operations after fractures of THAs in a comparison of clinical studies published in Medline-listed journals and annual reports of worldwide arthroplasty registers in a structured literature analysis based on a standardised methodology.

Results

Included clinical studies (sample-based datasets) were mono-centre trials comprising a cumulative number of approximately 70,000 primary implantations whereas worldwide national arthroplasty register datasets referred to 733,000 primary implantations, i.e. approximately ten times as many as sample-based datasets. In general, sample-based datasets presented higher revision rates than register datasets with a maximum deviation of a 14.5 ratio for ceramic heads, respectively. The incidence of implant fractures in total hip arthroplasty in pooled worldwide arthroplasty register datasets is 304 fractures per 100,000 implants. In other words, one out of 323 patients has to undergo revision surgery due to an implant fracture after THA in their lifetime.

Conclusions

Implant fractures in total hip arthroplasty occur in a relevant number of patients. The authors believe that comprehensive arthroplasty register datasets allow more general evaluations and conclusions on that topic in contrast to clinical studies.     

Identifying β-thalassemia carriers using a data mining approach: The case of the Gaza Strip,Palestine

Thalassemia is considered one of the most common genetic blood disorders that has received excessive attention in the medical research fields worldwide. Under this context, one of the greatest challenges for healthcare professionals is to correctly differentiate normal individuals from asymptomatic thalassemia carriers. Usually, thalassemia diagnosis is based on certain measurable characteristic changes to blood cell counts and related indices. These characteristic changes can be derived easily when performing a complete blood count test (CBC) using a special fully automated blood analyzer or counter. However, the reliability of the CBC test alone is questionable with possible candidate characteristics that could be seen in other disorders, leading to misdiagnosis of thalassemia. Therefore, other costly and time-consuming tests should be performed that may cause serious consequences due to the delay in the correct diagnosis. To help overcoming these challenging diagnostic issues, this work presents a new novel dataset collected from Palestine Avenir Foundation for persons tested for thalassemia. We aim to compile a gold standard dataset for thalassemia and make it available for researchers in this field. Moreover, we use this dataset to predict the specific type of thalassemia known as beta thalassemia (β-thalassemia) based on hybrid data mining model. The proposed model consists of two main steps. First, to overcome the problem of the highly imbalanced class distribution in the dataset, a balancing technique called SMOTE is proposed and applied to handle this problem. In the second step, four classification models, namely k-nearest neighbors (k-NN), naïve Bayesian (NB), decision tree (DT) and the multilayer perceptron (MLP) neural network are used to differentiate between normal persons and those patients carrying β-thalassemia. Different evaluation metrics are used to assess the performance of the proposed model.The experimental results show that the SMOTE oversampling method can effectively improve the identification ratio of β-thalassemia carriers in a highly imbalanced class distribution. The results reveal also that the NB classifier achieved the best performance in differentiating between normal and β-thalassemia carriers at oversampling SMOTE ratio of 400%. This combination shows a specificity of 99.47% and a sensitivity of 98.81%.