Radiomics model based on preoperative gadoxetic acid-enhanced MRI for predicting liver failure

BACKGROUND Postoperative liver failure is the most severe complication in cirrhotic patients with hepatocellular carcinoma(HCC) after major hepatectomy. Current available clinical indexes predicting postoperative residual liver function are not sufficiently accurate.AIM To determine a radiomics model based on preoperative gadoxetic acid-enhanced magnetic resonance imaging for predicting liver failure in cirrhotic patients with HCC after major hepatectomy.METHODS For this retrospective study, a radiomics-based model was developed based on preoperative hepatobiliary phase gadoxetic acid-enhanced magnetic resonance images in 101 patients with HCC between June 2012 and June 2018. Sixty-one radiomic features were extracted from hepatobiliary phase images and selected by the least absolute shrinkage and selection operator method to construct a radiomics signature. A clinical prediction model, and radiomics-based model incorporating significant clinical indexes and radiomics signature were built using multivariable logistic regression analysis. The integrated radiomics-based model was presented as a radiomics nomogram. The performances of clinical prediction model, radiomics signature, and radiomics-based model for predicting post-operative liver failure were determined using receiver operating characteristics curve, calibration curve, and decision curve analyses.RESULTS Five radiomics features from hepatobiliary phase images were selected to construct the radiomics signature. The clinical prediction model, radiomics signature, and radiomics-based model incorporating indocyanine green clearance rate at 15 min and radiomics signature showed favorable performance for predicting postoperative liver failure(area under the curve: 0.809-0.894). The radiomics-based model achieved the highest performance for predicting liver failure(area under the curve: 0.894; 95%CI: 0.823-0.964). The integrated discrimination improvement analysis showed a significant improvement in the accuracy of liver failure prediction when radiomics signature was added to the clinical prediction model(integrated discrimination improvement = 0.117, P =0.002). The calibration curve and an insignificant Hosmer-Lemeshow test statistic(P = 0.841) demonstrated good calibration of the radiomics-based model. The decision curve analysis showed that patients would benefit more from a radiomics-based prediction model than from a clinical prediction model and radiomics signature alone.CONCLUSION A radiomics-based model of preoperative gadoxetic acid–enhanced MRI can be used to predict liver failure in cirrhotic patients with HCC after major hepatectomy.     

舒芬太尼在肾母细胞瘤患儿术后镇痛中的应用效果及对患儿免疫功能的影响

目的探讨舒芬太尼在肾母细胞瘤患儿术后镇痛中的应用效果及对患儿免疫功能的影响。方法采用随机数字表法将42例肾母细胞瘤患儿分为低剂量、中剂量和高剂量组,每组14例,低剂量组、中剂量组、高剂量组患儿术后舒芬太尼镇痛剂量分别为1.0、2.5、4.0μg/kg。术后6、24、48 h,采用中文版儿童疼痛行为量表(FLACC)评估三组患儿的镇痛效果,以及不良反应发生情况;麻醉前和术后1、24、72 h,检测三组患儿免疫功能指标,包括免疫球蛋白(Ig)G、IgA、IgM。结果术后6、24、48 h,中剂量组、高剂量组患儿FLACC评分均低于低剂量组患儿,差异均有统计学意义(P﹤0.05)。术后1、24、72 h,低剂量组患儿IgG、IgA、IgM水平均低于本组麻醉前,差异均有统计学意义(P﹤0.05);术后1、24 h,中剂量组和高剂量组患儿IgG、IgA、IgM水平均低于本组麻醉前,差异均有统计学意义(P﹤0.05),但术后72 h,中剂量组和高剂量组患儿IgG、IgA、IgM水平与麻醉前比较,差异均无统计学意义(P﹥0.05)。低剂量组患儿未发生不良反应,中剂量组患儿发生恶心1例,高剂量组患儿发生恶心5例、呕吐3例。结论2.5μg/kg的舒芬太尼在肾母细胞瘤患儿术后镇痛中效果明显,且对患儿的免疫系统发挥一定的保护作用,可以减轻患儿术后免疫功能抑制。     

Sliding mode control of time‐varying delay Markov jump with quantized output

This paper investigates the quantized sliding mode control of Markov jump systems with time‐varying delay. A dynamical adjustment law is explored to quantize the system output. By constructing an observer‐based integral sliding surface, a sliding mode controller is designed to take over the dynamical motion of state estimation and ensure the reachability of sliding surface. A new scaling manner is developed to build the bound between the system output and quantized error. With the help of separation strategies for controller synthesis and general transition probabilities and a lower bound theorem for nonlinear integral terms, a new synthesis method to ensure the required stability and meet the required performance is proposed in the form of linear matrix inequalities. The validity of the proposed control method is illustrated by a numerical example.