Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.     

Impact of sampling rate on statistical significance for single subject fMRI connectivity analysis

A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross‐covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity.     

Free Water Loss–induced Heterogeneous Residual Strain and Reduced Fatigue Resistance in Root Dentin: A 3-dimensional Digital Image Correlation Analysis

IntroductionThis study evaluated free water loss–induced residual strain with and without axial compressive loading and assessed the mechanical effect of cyclic loading in fully hydrated and partially dehydrated root dentin.MethodsRoot dentin sections prepared from freshly extracted human premolars were used. Customized 3-dimensional digital image correlation was used to qualitatively and quantitatively analyze the residual strain induced by 2 hours of free water loss in different regions of root dentin. Residual strain in partially dehydrated root dentin during axial compressive loading was also analyzed using 3-dimensional digital image correlation. The effect of cyclic loading on load to fracture in fully hydrated and partially dehydrated dentin and their fractography were analyzed using micro–computed tomographic imaging.ResultsFree water loss resulted in a heterogeneous distribution of residual strain and an overall formation of residual compressive strain with areas of tensile strain localized to the root canal and outer dentin. More residual compressive strain was observed in the apical dentin compared with the cervical dentin (P < .05), and more residual shear strain was observed in outer dentin compared with inner dentin (P < .05). Axial loading resulted in an increase in the load-induced compressive strain in the direction perpendicular to dentinal tubules (P < .05). Fully hydrated roots displayed a higher mean (P < .05) and median (P < .05) number of cycles to fracture with microcracks characteristic of toughness.ConclusionsAfter free water loss, root dentin displayed an increased formation of heterogenous residual strain, which resulted in increased axial compressive load-induced strain and a decreased resistance to fatigue failure. The effect of free water loss in the loss of mechanical integrity of root-filled teeth needs further investigation.     

灰关联分析应用于因素分析时关联度指标的改进

本文指出在应用灰关联分析进行因素分析时传统的关联度计算方法的不足，并提出采用新的关联度指标来表示因素之间的关联关系，同时给出了实例介绍计算方法。     

Effect of maternal and infant covariates on sibship correlation in birth weight

Birth weight on 12,644 singleton infants from 6,196 sibships born in Maryland between 1980 and 1984 were used to estimate the effects of nine maternal and infant covariates on the sibship correlation in birth weight. Assuming a homogeneous correlation across all families, the estimated intraclass correlation was 0.4664 (+/- 0.0099). This high sibship correlation makes it possible to predict, with reasonable accuracy, the birth weight of a child given information on previous sibs, as well as covariates on the mother and/or infant pertinent to a given pregnancy. The reduction in variance associated with incorporating information on the nine covariates used here was approximately equal to that obtained by conditioning on a single previous sib. Testing for heterogeneity in correlation among different groups of families showed that a crude measure of parity (first live birth vs. other), time between births, mother's marital status, and maternal age at the birth of the last child significantly influenced the sibship correlation in birth weight.     

ABO血型与肝炎病毒感染关系的探讨

目的：探讨ABO血型与肝炎病毒感染的关系，方法：应用疾病关联分析方法对HBsAg和,抗－HCV阳性献血者与同期健康献血者的ABO血型资料进行比较分析。结果：ABO血型与HBsAg和抗－HCV阳性率之间无显著性相关关系（P＞0．05），结论：乙型肝炎和丙型肝炎病毒感染与ABO血型无显著性相关。     

In Vitro and in Vivo Evaluation of Whole and Half Tablets of Sustained-Release Adinazolam Mesylate

The mechanism of release from sustained-release adinazolam mesylate tablets was assessed by the Higuchi equation and by analysis of drug release profiles through 60% released using the Peppas equation. Computed values of the diffusional exponent, n, ranged from 0.59 to 0.66. Values of n in this range are consistent with a mixed mechanism of release, with diffusion of drug through the hydrated polymer matrix and relaxation of this matrix being the principal processes controlling release. The rate of in vitro drug release was increased for half tablets relative to whole tablets and is attributed to an increase in the surface to volume ratio of half tablets of about 16%. This increase in surface-to-volume ratio of half tablets was reflected by an increase in the constant, k, from the Peppas equation of 20–23% and by an increase in the slope of Higuchi plots of 12–18% for four lots of tablets. In vivo/in vitro relationships from two bioavailability studies were thoroughly evaluated. Using either a linear or a quadratic relationship, an in vivo/in vitro correlation exists for sustained-release adinazolam mesylate tablets.     

Working‐correlation‐structure identification in generalized estimating equations

Selecting an appropriate working correlation structure is pertinent to clustered data analysis using generalized estimating equations (GEE) because an inappropriate choice will lead to inefficient parameter estimation. We investigate the well‐known criterion of QIC for selecting a working correlation structure, and have found that performance of the QIC is deteriorated by a term that is theoretically independent of the correlation structures but has to be estimated with an error. This leads us to propose a correlation information criterion (CIC) that substantially improves the QIC performance. Extensive simulation studies indicate that the CIC has remarkable improvement in selecting the correct correlation structures. We also illustrate our findings using a data set from the Madras Longitudinal Schizophrenia Study. Copyright © 2008 John Wiley & Sons, Ltd.