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%.     

对药代动力学实验的两点改进

通过具体的实验数据，讨论在药代动力学实验中如何加入曲线下面积（AUC）的计算，以帮助学生理解并掌握这一概念；在实验室设置不同剂量组，给药后比较剂量与浓度是否呈等比例关系，以此加深学生对于一级动力学消除及其临床意义的理解。     

Bayesian estimation of a random effects heteroscedastic probit model

Summary Bayesian analysis is given of a random effects binary probit model that allows for heteroscedasticity. Real and simulated examples illustrate the approach and show that ignoring heteroscedasticity when it exists may lead to biased estimates and poor prediction. The computation is carried out by an efficient Markov chain Monte Carlo sampling scheme that generates the parameters in blocks. We use the Bayes factor, cross‐validation of the predictive density, the deviance information criterion and Receiver Operating Characteristic (ROC) curves for model comparison.     

Greater curve approach for laparoscopic gastric bypass: total stapler gastrojejunostomy

BACKGROUND: Gastric bypass is an established bariatric procedure that has undergone multiple modifications to improve its effectiveness. The side-to-side stapled technique is well recognized, but closure of the gastrotomy/enterotomy by the stapler can potentially narrow the Roux limb. Because of this, many surgeons will hand suture the closure of the gastrotomy/enterotomy. To obviate this difficulty, we inserted the linear stapler from the stomach's greater curvature, using a double-stapled anastomosis that minimized the need for hand suturing. METHODS: We performed a retrospective analysis of 307 patients undergoing this technique for laparoscopic gastric bypass. The weight loss and 30-day morbidity and mortality were tabulated and compared with those in other published series. RESULTS: Of the 307 patients, none died postoperatively. The overall 30-day morbidity rate was 15%. Two leaks from the gastrojejunostomy and 2 from the jejunojejunostomy (1.2%) developed. The mean percentage of excess weight loss was 34% at 3 months, 52% at 6 months, 73% at 1 year, 71% at 2 years, and 69% at 3 years. CONCLUSION: The greater curve approach avoids Roux limb obstruction, minimizes the need for hand suturing, and uses standard trocar incisions. Our short-term follow-up results are similar to those of series of other techniques.     

应用ROC曲线评价DR后处理技术对胸部影像的影响

目的:运用ROC曲线,探讨DR胸部摄影最佳的影像后处理技术。方法:使用DR系统设置的正常胸部摄影条件,对胸部体模与专用于X线摄影的ROC曲线评价体模(TRG)进行组合并曝光。分析未使用后处理技术及使用不同后处理技术,对DR胸部摄影胸片影像的影响。结果:使用单纯噪声消除技术、噪声消除加图像反转及噪声消除加全屏放大三种后处理技术所得的ROC曲线下面积Az值依次为0.85、0.825及0.925,均明显大于未使用后处理技术的面积Az值0.6;单纯噪声消除技术获得的ROC曲线下面积略大于噪声消除加图像反转技术的面积;噪声消除加全屏放大技术获得的ROC曲线下面积Az值最大。结论:应对胸部原始影像实施后处理调节。单纯噪声消除技术可明显增加影像的信息量,若在噪声消除基础上实施全屏放大技术,可获得DR胸部摄影的最大影像信息量。     

Nerve conduction as a means of estimating early post-mortem interval

Summary Methods in current practice for ascertaining time of death are largely based on the cooling of the body after death and are somewhat unreliable. A theoretica relationship is known to exist between the decline in the properties defining nerve conduction and time after death caused by the gradual cessation of metabolic activity in nerves. A number of such properties were measured in rats during life and after death. In most cases the relationship was found to be inconsistent. The chronaxie of the strength duration curve for the sciatic nerve was, however, found to increase consistently and reproducibly in a linear fashion over the first 90 min after death to a plateau value which was maintained beyond 135 min. These findings are discussed as the possible basis of a forensic method of determining the duration of the “post mortem interval” within the first few hours after death.      

An evaluation of numerical integration algorithms for the estimation of the area under the curve (AUC) in pharmacokinetic studies

Six numerical integration algorithms based on linear and log trapezoidal methods as well as four cubic-spline methods were proposed for estimation of area under the curve (AUC). These six different algorithms were implemented using IMSL/IDL^TM command language and evaluated using data simulated under five different dosing conditions and two different sampling conditions. Comparisons between AUC estimations using these six different algorithms and the theoretical results were made in terms of both overall AUC values and the superimposability of the concentration-time profiles. In well designed studies with ample data points, the algorithm based on IMSL/IDL^TM function CSSHAPE with concavity preservation gave the best performance. In contrast, when the frequency of blood collection was limited, the algorithm based on the log trapezoidal rule proved to be stable with reasonable accuracy, and is recommended as the practical method for numerical interpolation and integration in pharmacokinetic studies. Algorithms based on the combination of the log trapezoidal rule and cubic-spline methods using IMSL/IDL^TM function CSSHAPE can be developed to enhance overall performance.