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.     

Utility of Tissue Similarity Maps Based on Relative Cerebral Blood Volume for Grading Gliomas as Validated by Histological Results

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Network Meta-Analysis of Randomized Controlled Trials: Efficacy and Safety of UDCA-Based Therapies in Primary Biliary Cirrhosis

Major ursodeoxycholic acid (UDCA)-based therapies for primary biliary cirrhosis (PBC) include UDCA only, or combined with either methotrexate (MTX), corticosteroids (COT), colchicine (COC), or bezafibrate (BEF). As the optimum treatment regimen is unclear and warrants exploration, we aimed to compare these therapies in terms of patient mortality or liver transplantation (MOLT) and adverse events (AE).PubMed, the Cochrane Library, and Scopus were searched for randomized controlled trials up to August 31, 2014. We estimated the hazard ratios (HRs) for MOLT and odds ratios (ORs) for AE. A sensitivity analysis based on the dose of UDCA was also executed.Thirty-one eligible articles were included. Compared with COT plus UDCA, UDCA (HR 0.38, 95% confidence interval [CI] 0.09–1.39), BEF plus UDCA (HR 0.29, 95% CI 0.02–4.83), COC plus UDCA (HR 0.39, 95% CI 0.07–2.25), MTX plus UDCA (HR 0.28, 95% CI 0.05–1.63), or OBS (HR 0.49, 95% CI 0.11–2.01) all provided an increased risk of MOLT. With respect to drug AE profile, although not differing appreciably, BEF plus UDCA was associated with more AEs compared with UDCA (OR 3.16, 95% CI 0.59–20.67), COT plus UDCA (OR 2.27, 95% CI 0.15–33.36), COC plus UDCA (OR 1.00, 95% CI 0.09–12.16), MTX plus UDCA (OR 2.03, 95% CI 0.23–17.82), or OBS (OR 3.00, 95% CI 0.53–20.75). The results of sensitivity analyses were highly consistent with previous analyses.COT plus UDCA was the optimal UDCA-based regimen for both MOLT and AEs. BEF plus UDCA was most likely to cause AEs, whereas monotherapy with UDCA and coadministriation of COT plus UDCA appeared to be associated with the fewest AEs for PBC treatment.     

Comparison between a new platelet count drop method PL-11, light transmission aggregometry,VerifyNow aspirin system and thromboelastography for monitoring short-term aspirin effects in healthy individuals

Platelet function has been described by many laboratory assays, and PL-11 is a new point-of-care platelet function analyzer based on platelet count drop method, which counts platelet before and after the addition of agonists in the citrated whole blood samples. The present study sought to compare PL-11 with other three major more established assays, light transmission aggregometry (LTA), VerifyNow? aspirin system and thromboelastography (TEG), for monitoring the short-term aspirin responses in healthy individuals. Ten healthy young men took 100?mg/d aspirin for 3-day treatment. Platelet function was measured via PL-11, LTA, VerifyNow and TEG, respectively. The blood samples were collected at baseline, 2 hour, 1 day during the aspirin treatment and 1 day, 5?±?1 days, 8?±?1 days after the aspirin withdrawal. Moreover, 90 additional healthy subjects were recruited to establish a reference range for PL-11. Platelet function of healthy subjects decreased significantly 2 hours after 100?mg/d aspirin intake and began to recover during 4–6 days after the aspirin withdrawal. Correlations between methods were PL-11 vs. LTA (r?=?0.614, p?<?0.01); PL-11 vs. VerifyNow (r?=?0.829, p?<?0.01); PL-11 vs. TEG (r?=?0.697, p?<?0.001). There was no significant bias between PL-11 and LTA at baseline (bias?=?1.94%, p?=?0.804) using Bland-Altman analysis, while the data of PL-11 were significantly higher than LTA (bias?=?24.02%, p?<?0.001) during the aspirin therapy. The reference range for PL-11 in healthy young individuals was from 66.8 to 90.5% (95%CI). When aspirin low-responsiveness was defined as LTA?>?20%, the cut-off values for each method were, respectively: PL-11?>?50%, VerifyNow?>?533 ARU, TEG?>?60.2%. The results of different platelet function assays were uninterchangeable for monitoring aspirin response and correlations among them were also varied. Correlations among PL-11 and other three major assays suggested the ability of PL-11 to assess the treatment effects of aspirin. But a large cohort study is needed to confirm the cut-off value of aspirin response detected by PL-11.     

经自然腔道取标本手术在结直肠外科手术中的应用进展

随着腔镜技术的进一步发展以及微创理念应用于结直肠外科疾病的诊治中,结直肠相关疾病的诊治发生了翻天覆地的变化。由传统的经腹手术到腹腔镜手术、经自然腔道手术,再到经自然腔道取标本手术(NOSES),结直肠疾病的外科诊治在微创领域取得了巨大成果。NOSES技术是目前结直肠外科在微创领域前沿的手术方式之一,它通过经直肠、阴道取标本来避免了腹壁的辅助取标本切口,从而将结直肠外科手术进一步微创化。NOSES技术集传统腹腔镜手术的优势与现代微创外科的理念于一体,它在确保手术效果的基础上集中体现了微创、加速康复外科、功能外科、"无疤"等理念的特点。本文主要就国内外各中心开展NOSES技术在结直肠外科诊治开展中的相关经验、心得和体会进行综述。     

Quantile regression and empirical likelihood for the analysis of longitudinal data with monotone missing responses due to dropout,with applications to quality of life measurements from clinical trials

The analysis of quality of life (QoL) data can be challenging due to the skewness of responses and the presence of missing data. In this paper, we propose a new weighted quantile regression method for estimating the conditional quantiles of QoL data with responses missing at random. The proposed method makes use of the correlation information within the same subject from an auxiliary mean regression model to enhance the estimation efficiency and takes into account of missing data mechanism. The asymptotic properties of the proposed estimator have been studied and simulations are also conducted to evaluate the performance of the proposed estimator. The proposed method has also been applied to the analysis of the QoL data from a clinical trial on early breast cancer, which motivated this study.