Quantification of Pancreas Surface Lobularity on CT: A Feasibility Study in the Normal Pancreas

Sartoris Riccardo,Calandra Alberto,Lee Kyung Jin,Gauss Tobias,Vilgrain Valérie,Ronot Maxime 대한영상의학회 2021 Korean Journal of Radiology Vol.22 No.8

Objective: To assess the feasibility and reproducibility of pancreatic surface lobularity (PSL) quantification derived from abdominal computed tomography (CT) in a population of patients free from pancreatic disease. Materials and Methods: This retrospective study included 265 patients free from pancreatic disease who underwent contrast-enhanced abdominal CT between 2017 and 2019. A maximum of 11 individual PSL measurements were performed by two abdominal radiologists (head [5 measurements], body, and tail [3 measurements each]) using dedicated software. The influence of age, body mass index (BMI), and sex on PSL was assessed using the Pearson correlation and repeated measurements. Inter-reader agreement was assessed using the intraclass correlation coefficient (ICC) and Bland Altman (BA) plots. Results: CT images of 15 (6%) patients could not be analyzed. A total of 2750 measurements were performed in the remaining 250 patients (143 male [57%], mean age 45 years [range, 18–91]), and 2237 (81%) values were obtained in the head 951/1250 (76%), body 609/750 (81%), and tail 677/750 (90%). The mean ± standard deviation PSL was 6.53 ± 1.37. The mean PSL was significantly higher in male than in female (6.89 ± 1.30 vs. 6.06 ± 1.31, respectively, p < 0.001). PSL gradually increased with age (r = 0.32, p < 0.001) and BMI (r = 0.32, p < 0.001). Inter-reader agreement was excellent (ICC 0.82 [95% confidence interval 0.72–0.85], with a BA bias of 0.30 and 95% limits of agreement of -1.29 and 1.89). Conclusion: CT-based PSL quantification is feasible with a high success rate and inter-reader agreement in subjects free from pancreatic disease. Significant variations were observed according to sex, age, and BMI. This study provides a reference for future studies.

A new extended Birnbaum-Saunders model with cure fraction: classical and Bayesian approach

Ortega, Edwin M.M.,Cordeiro, Gauss M.,Suzuki, Adriano K.,Ramires, Thiago G. The Korean Statistical Society 2017 Communications for statistical applications and me Vol.24 No.4

A four-parameter extended fatigue lifetime model called the odd Birnbaum-Saunders geometric distribution is proposed. This model extends the odd Birnbaum-Saunders and Birnbaum-Saunders distributions. We derive some properties of the new distribution that include expressions for the ordinary moments and generating and quantile functions. The method of maximum likelihood and a Bayesian approach are adopted to estimate the model parameters; in addition, various simulations are performed for different parameter settings and sample sizes. We propose two new models with a cure rate called the odd Birnbaum-Saunders mixture and odd Birnbaum-Saunders geometric models by assuming that the number of competing causes for the event of interest has a geometric distribution. The applicability of the new models are illustrated by means of ethylene data and melanoma data with cure fraction.

Regression models generated by gamma random variables with long-term survivors

Ortega, Edwin M.M.,Cordeiro, Gauss M.,Hashimoto, Elizabeth M.,Suzuki, Adriano K. The Korean Statistical Society 2017 Communications for statistical applications and me Vol.24 No.1

We propose a flexible cure rate survival model by assuming that the number of competing causes of the event of interest has the Poisson distribution and the time for the event follows the gamma-G family of distributions. The extended family of gamma-G failure-time models with long-term survivors is flexible enough to include many commonly used failure-time distributions as special cases. We consider a frequentist analysis for parameter estimation and derive appropriate matrices to assess local influence on the parameters. Further, various simulations are performed for different parameter settings, sample sizes and censoring percentages. We illustrate the performance of the proposed regression model by means of a data set from the medical area (gastric cancer).

The Marshall-Olkin generalized gamma distribution

Barriga, Gladys D.C.,Cordeiro, Gauss M.,Dey, Dipak K.,Cancho, Vicente G.,Louzada, Francisco,Suzuki, Adriano K. The Korean Statistical Society 2018 Communications for statistical applications and me Vol.25 No.3

Attempts have been made to define new classes of distributions that provide more flexibility for modelling skewed data in practice. In this work we define a new extension of the generalized gamma distribution (Stacy, The Annals of Mathematical Statistics, 33, 1187-1192, 1962) for Marshall-Olkin generalized gamma (MOGG) distribution, based on the generator pioneered by Marshall and Olkin (Biometrika, 84, 641-652, 1997). This new lifetime model is very flexible including twenty one special models. The main advantage of the new family relies on the fact that practitioners will have a quite flexible distribution to fit real data from several fields, such as engineering, hydrology and survival analysis. Further, we also define a MOGG mixture model, a modification of the MOGG distribution for analyzing lifetime data in presence of cure fraction. This proposed model can be seen as a model of competing causes, where the parameter associated with the Marshall-Olkin distribution controls the activation mechanism of the latent risks (Cooner et al., Statistical Methods in Medical Research, 15, 307-324, 2006). The asymptotic properties of the maximum likelihood estimation approach of the parameters of the model are evaluated by means of simulation studies. The proposed distribution is fitted to two real data sets, one arising from measuring the strength of fibers and the other on melanoma data.

The unique role of domain 2A of the hepatitis A virus precursor polypeptide P1-P2 in viral morphogenesis

( Graziella Morace ),( Yuri Kusov ),( Georgy Dzagurov ),( Francesca Beneduce ),( Verena Gauss Miiller ) 생화학분자생물학회 2008 BMB Reports Vol.41 No.9

Bivariate odd-log-logistic-Weibull regression model for oral health-related quality of life

Cruz, Jose N. da,Ortega, Edwin M.M.,Cordeiro, Gauss M.,Suzuki, Adriano K.,Mialhe, Fabio L. The Korean Statistical Society 2017 Communications for statistical applications and me Vol.24 No.3

We study a bivariate response regression model with arbitrary marginal distributions and joint distributions using Frank and Clayton's families of copulas. The proposed model is used for fitting dependent bivariate data with explanatory variables using the log-odd log-logistic Weibull distribution. We consider likelihood inferential procedures based on constrained parameters. For different parameter settings and sample sizes, various simulation studies are performed and compared to the performance of the bivariate odd-log-logistic-Weibull regression model. Sensitivity analysis methods (such as local and total influence) are investigated under three perturbation schemes. The methodology is illustrated in a study to assess changes on schoolchildren's oral health-related quality of life (OHRQoL) in a follow-up exam after three years and to evaluate the impact of caries incidence on the OHRQoL of adolescents.

The Bivariate Kumaraswamy Weibull regression model: a complete classical and Bayesian analysis

Fachini-Gomes, Juliana B.,Ortega, Edwin M.M.,Cordeiro, Gauss M.,Suzuki, Adriano K. The Korean Statistical Society 2018 Communications for statistical applications and me Vol.25 No.5

Bivariate distributions play a fundamental role in survival and reliability studies. We consider a regression model for bivariate survival times under right-censored based on the bivariate Kumaraswamy Weibull (Cordeiro et al., Journal of the Franklin Institute, 347, 1399-1429, 2010) distribution to model the dependence of bivariate survival data. We describe some structural properties of the marginal distributions. The method of maximum likelihood and a Bayesian procedure are adopted to estimate the model parameters. We use diagnostic measures based on the local influence and Bayesian case influence diagnostics to detect influential observations in the new model. We also show that the estimates in the bivariate Kumaraswamy Weibull regression model are robust to deal with the presence of outliers in the data. In addition, we use some measures of goodness-of-fit to evaluate the bivariate Kumaraswamy Weibull regression model. The methodology is illustrated by means of a real lifetime data set for kidney patients.

The influence of foreign vs. North American emissions on surface ozone in the US

Reidmiller, D. R.,Fiore, A. M.,Jaffe, D. A.,Bergmann, D.,Cuvelier, C.,Dentener, F. J.,Duncan, B. N.,Folberth, G.,Gauss, M.,Gong, S.,Hess, P.,Jonson, J. E.,Keating, T.,Lupu, A.,Marmer, E.,Park, R.,Schu Copernicus GmbH 2009 Atmospheric Chemistry and Physics Vol.9 No.14

<P>Abstract. As part of the Hemispheric Transport of Air Pollution (HTAP; http:// www.htap.org) project, we analyze results from 15 global and 1 hemispheric chemical transport models and compare these to Clean Air Status and Trends Network (CASTNet) observations in the United States (US) for 2001. Using the policy-relevant maximum daily 8-h average ozone (MDA8 O3) statistic, the multi-model ensemble represents the observations well (mean r2=0.57, ensemble bias = +4.1 ppbv for all US regions and all seasons) despite a wide range in the individual model results. Correlations are strongest in the northeastern US during spring and fall (r2=0.68); and weakest in the midwestern US in summer (r2=0.46). However, large positive mean biases exist during summer for all eastern US regions, ranging from 10-20 ppbv, and a smaller negative bias is present in the western US during spring (~3 ppbv). In nearly all other regions and seasons, the biases of the model ensemble simulations are ≤5 ppbv. Sensitivity simulations in which anthropogenic O3-precursor emissions (NOx + NMVOC + CO + aerosols) were decreased by 20% in four source regions: East Asia (EA), South Asia (SA), Europe (EU) and North America (NA) show that the greatest response of MDA8 O3 to the summed foreign emissions reductions occurs during spring in the West (0.9 ppbv reduction due to 20% emissions reductions from EA + SA + EU). East Asia is the largest contributor to MDA8 O3 at all ranges of the O3 distribution for most regions (typically ~0.45 ppbv) followed closely by Europe. The exception is in the northeastern US where emissions reductions in EU had a slightly greater influence than EA emissions, particularly in the middle of the MDA8 O3 distribution (response of ~0.35 ppbv between 35-55 ppbv). EA and EU influences are both far greater (about 4x) than that from SA in all regions and seasons. In all regions and seasons O3-precursor emissions reductions of 20% in the NA source region decrease MDA8 O3 the most - by a factor of 2 to nearly 10 relative to foreign emissions reductions. The O3 response to anthropogenic NA emissions is greatest in the eastern US during summer at the high end of the O3 distribution (5-6 ppbv for 20% reductions). While the impact of foreign emissions on surface O3 in the US is not negligible - and is of increasing concern given the recent growth in Asian emissions - domestic emissions reductions remain a far more effective means of decreasing MDA8 O3 values, particularly those above 75 ppb (the current US standard). </P>