Reproducibility study of intravoxel incoherent motion and apparent diffusion coefficient parameters in normal pancreas

BACKGROUND The consistency of pancreatic apparent diffusion coefficient(ADC)values and intravoxel incoherent motion(IVIM)parameter values across different magnetic resonance imaging(MRI)devices significantly impacts the patient’s diagnosis and treatment.AIM To explore consistency in image quality,ADC values,and IVIM parameter values among different MRI devices in pancreatic examinations.METHODS This retrospective study was approved by the local ethics committee,and informed consent was obtained from all participants.In total,22 healthy volunteers(10 males and 12 females)aged 24-61 years(mean,28.9±2.3 years)underwent pancreatic diffusion-weighted imaging using 3.0T MRI equipment from three vendors.Two independent observers subjectively scored image quality and measured the pancreas’s overall ADC values and signal-to-noise ratios(SNRs).Subsequently,regions of interest(ROIs)were delineated for the IVIM parameters(true diffusion coefficient,pseudo-diffusion coefficient,and perfusion fraction)using post-processing software.These ROIs were on the head,body,and tail of the pancrease.The subjective image ratings were assessed using the kappa consistency test.Intraclass correlation coefficients(ICCs)and mixed linear models were used to evaluate each device’s quantitative parameter values.Finally,a pairwise analysis of IVIM parameter values across each device was performed using Bland-Altman plots.RESULTS The Kappa value for the subjective ratings of the different observers was 0.776(P<0.05).The ICC values for interobserver and intra-observer agreements for the quantitative parameters were 0.803[95% confidence interval(CI):0.684-0.880]and 0.883(95%CI:0.760-0.945),respectively(P<0.05).The ICCs for the SNR between different devices was comparable(P>0.05),and the ICCs for the ADC values from different devices were 0.870,0.707,and 0.808,respectively(P<0.05).Notably,only a few statistically significant inter-device agreements were observed for different IVIM parameters,and among those,the ICC values were generally low.The mixed linear model results indicated differences(P<0.05)in the f-value for the pancreas head,D-value for the pancreas body,and D-value for the pancreas tail obtained using different MRI machines.The Bland-Altman plots showed significant variability at some data points.CONCLUSION ADC values are consistent among different devices,but the IVIM parameters’repeatability is moderate.Therefore,the variability in the IVIM parameter values may be associated with using different MRI machines.Thus,caution should be exercised when using IVIM parameter values to assess the pancreas.

The Values of the Prognostic Nutrition Index and the Patient Generated-Subjective Global Assessment during the Nutritional Assessment of Patients with Gastrointestinal Tumors

Objective To compare the value and consistency among the Patient Generated-Subjective Global Assessment(PG-SGA)and the Prognostic Nutrition Index(PNI)for assessing nutritional status in gastrointestinal tumor patients.Methods 251 patients from gastric cancer surgical ward from January 2019 to January 2020 were recruited through convenience sampling in this respective study.Nutritional screening and assessment were conducted for 251 gastrointestinal tumor patients using the nutritional risk screening 2002(NRS 2002)PG-SGA,and the PNI.PNI was calculated using the serum albumin level and the total lymphocyte count obtained from the patients’routine laboratory examination when they were admitted to the hospital.The receiver operating characteristic(ROC)of the PG SGA and the PNI were plotted with the NRS 2002 used as the gold standard,and the diagnostic value of the PG-SGA and PNI was reflected by the area under the curve(AUC),sensitivity,specificity and Youden index.We then determined the optimal cut-off for the PNI and tested the consistency of the PG-SGA and PNI.Results The optimal cut-off point for the PNI was calculated to be 50.78.The AUC of the PG-SGA was 0.908(95%CI 0.871-0.944).The sensitivity was 89.9%,specificity was 76.2%and the Youden index was 0.661.The AUC of the PNI was 0.594(95%CI 0.516-0.572).The sensitivity was 73.8%,specificity was 44.3%and the Youden index was 0.181.In the consistency test,the kappa value was 0.838(P<0.001).Conclusion The PNI is of limited value for assessing malnutrition,although it did have good consistency with the PG-SGA.The combination of the PNI and PG-SGA can be used for diagnosing assessing malnutrition in clinical practice.

The Application of Multiple Evaluating Methods on the Comprehensive Evaluation of Urban-rural Interaction and Conjunctional Development of China

Urban-rural interaction and conjunctionaldevelopment is the effective way of harmonizingurban-rural relationship and promoting urban-ruraldevelopment. However, there are differences in thelevel of urban-rural interaction and conjunctionaldevelopment of China. How to measure and reflectthe difference properly is a decision-makingfoundation for optimizing urban-rural developmentof different regions. The paper employs multiplemethods to evaluate and order the urban-ruralinteraction and conjunctional development of 31provinces (municipalities) in China. Kendall’s Wconsistency coefficient is employed to give a consistenttest for the evaluation result series, and the resultsof multiple methods are aggregated as the finalevaluation value. The results reflect the level ofregional urban-rural interaction and conjunctionaldevelopment in China.

Generalized Method of Moments and Generalized Estimating Functions Based on Probability Generating Function for Count Models

Generalized method of moments based on probability generating function is considered. Estimation and model testing are unified using this approach which also leads to distribution free chi-square tests. The estimation methods developed are also related to estimation methods based on generalized estimating equations but with the advantage of having statistics for model testing. The methods proposed overcome numerical problems often encountered when the probability mass functions have no closed forms which prevent the use of maximum likelihood (ML) procedures and in general, ML procedures do not lead to distribution free model testing statistics.