Estimating causality between smoking and abdominal obesity by Mendelian randomization.

BACKGROUND AND AIMS: Smokers tend to have a lower body weight than non-smokers, but also more abdominal fat. It remains unclear whether or not the relationship between smoking and abdominal obesity is causal. Previous Mendelian randomization (MR) studies have investigated this relationship by relying upon a single genetic variant for smoking heaviness. This approach is sensitive to pleiotropic effects and may produce imprecise causal estimates. We aimed to estimate causality between smoking and abdominal obesity using multiple genetic instruments. DESIGN: MR study using causal analysis using summary effect estimates (CAUSE) and latent heritable confounder MR (LHC-MR) methods that instrument smoking using genome-wide data, and also two-sample MR (2SMR) methods. SETTING: Genome-wide association studies (GWAS) summary statistics from participants of European ancestry, obtained from the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN), Genetic Investigation of Anthropometric Traits (GIANT) Consortium and the UK Biobank. PARTICIPANTS: We used GWAS results for smoking initiation (n = 1 232 091), life-time smoking (n = 462 690) and smoking heaviness (n = 337 334) as exposure traits, and waist-hip ratio (WHR) and waist and hip circumferences (WC and HC) (n up to 697 734), with and without adjustment for body mass index (adjBMI), as outcome traits. MEASUREMENTS: Smoking initiation, life-time smoking, smoking heaviness, WHR, WC, HC, WHRadjBMI, WCadjBMI and HCadjBMI. FINDINGS: Both CAUSE and LHC-MR indicated a positive causal effect of smoking initiation on WHR (0.13 [95% confidence interval (CI) = 0.10, 0.16 and 0.49 (0.41, 0.57), respectively] and WHRadjBMI (0.07 (0.03, 0.10) and 0.31 (0.26, 0.37). Similarly, they indicated a positive causal effect of life-time smoking on WHR [0.35 (0.29, 0.41) and 0.44 (0.38, 0.51)] and WHRadjBMI [0.18 (0.13, 0.24) and 0.26 (0.20, 0.31)]. In follow-up analyses, smoking particularly increased visceral fat. There was no evidence of a mediating role by cortisol or sex hormones. CONCLUSIONS: Smoking initiation and higher life-time smoking may lead to increased abdominal fat distribution. The increase in abdominal fat due to smoking is characterized by an increase in visceral fat. Thus, efforts to prevent and cease smoking can have the added benefit of reducing abdominal fat.

Application of the iris filter for automatic detection of pulmonary nodules on computed tomography images.

We have developed a computer-aided diagnosis (CAD) system to detect pulmonary nodules on thin-slice helical computed tomography (CT) images. We have also investigated the capability of an iris filter to discriminate between nodules and false-positive findings. Suspicious regions were characterized with features based on the iris filter output, gray level and morphological features, extracted from the CT images. Functions calculated by linear discriminant analysis (LDA) were used to reduce the number of false-positives. The system was evaluated on CT scans containing 77 pulmonary nodules. The system was trained and evaluated using two completely independent data sets. Results for a test set, evaluated with free-response receiver operating characteristic (FROC) analysis, yielded a sensitivity of 80% at 7.7 false-positives per scan.

Selecting variables in non-parametric regression models for binary response. An application to the computerized detection of breast cancer.

In many biomedical applications, interest lies in being able to distinguish between two possible states of a given response variable, depending on the values of certain continuous predictors. If the number of predictors, p, is high, or if there is redundancy among them, it then becomes important to decide on the selection of the best subset of predictors that will be able to obtain the models with greatest discrimination capacity. With this aim in mind, logistic generalized additive models were considered and receiver operating characteristic (ROC) curves were applied in order to determine and compare the discriminatory capacity of such models. This study sought to develop bootstrap-based tests that allow for the following to be ascertained: (a) the optimal number q < or = p of predictors; and (b) the model or models including q predictors, which display the largest AUC (area under the ROC curve). A simulation study was conducted to verify the behaviour of these tests. Finally, the proposed method was applied to a computer-aided diagnostic system dedicated to early detection of breast cancer.

Categorical variables, interactions and generalized additive models. Applications in computer-aided diagnosis systems.

Recently, the generalized additive models (GAMs) have been presented as a novel statistical approach to distinguish lesion/non-lesion in computer-aided diagnosis (CAD) systems. In this paper, we present an extension of the GAM that allows for the introduction of factors and their interactions with continuous variables, for reducing false positives in a CAD system for detecting clustered microcalcifications in digital mammograms. The results obtained have shown an increase in the sensitivity from 83.12% to 85.71%, while the false positive rate was drastically reduced from 1.46 to 0.74 false detections per image.

Effects of JPEG2000 data compression on an automated system for detecting clustered microcalcifications in digital mammograms.

The functionalities of the JPEG2000 standard have led to its incorporation into digital imaging and communications in medicine (DICOM), which makes this compression method available for medical systems. In this study, we evaluated the compression of mammographic images with JPEG2000 (16 : 1, 20 : 1, 40 : 1, 60.4 : 1, 80: 1, and 106 : 1) for applications with a computer-aided detection (CAD) system for clusters of microcalcifications. Jackknife free-response receiver operating characteristic (JAFROC) analysis indicated that differences in the detection of clusters of microcalcifications were not statistically significant for uncompressed versus 16: 1 (T = -0.7780; p = 0.4370), 20 : 1 (T = 1.0361; p = 0.3007), and 40 : 1 (T = 1.6966; p = 0.0904); and statistically significant for uncompressed versus 60.4 : 1 (T = 5.8883; p < 0.008), 80 : 1 (T = 7.8414; p < 0.008), and 106 : 1 (T = 17.5034; p = < 0.008). Although there is a small difference in peak signal-to-noise ratio (PSNR) between compression ratios, the true-positive (TP) and false-positive (FP) rates, and the free-response receiver operating characteristic (FROC), figure of merit values considerably decreased from a 60 : 1 compression ratio. The performance of the CAD system is significantly reduced when using images compressed at ratios greater than 40 : 1 with JPEG2000 compared to uncompressed images. Mammographic images compressed up to 20 : 1 provide a percentage of correct detections by our CAD system similar to uncompressed images, regardless of the characteristics of the cluster. Further investigation is required to determine how JPEG2000 affects the detectability of clusters of microcalcifications as a function of their characteristics.