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.     

VATS解剖性肺段切除术与肺叶切除术治疗Ⅰa期NSCLC患者的手术情 况及对肺功能影响的比较

目的:探讨电视胸腔镜（VATS）解剖性肺段切除术与肺叶切除术治疗Ia 期非小细胞肺癌（NSCLC）患者的手术情况及对患者肺功能的影响。方法:选取我院手术治疗的Ⅰa期NSCLC患者，收集时间2014年1月至2016年12月，根据术式不同分为两组，均采用VATS手术治疗，A组（54例）患者采用解剖性肺段切除术、B组（60例）采用肺叶切除术治疗，对比两组患者的手术效果及术后肺功能变化。结果:A组患者的手术时间、清扫淋巴结数目与B组比较差异无统计学意义（P>0.05）；A组患者的手术出血量、术后胸腔引流量、术后拔管时间、术后住院时间均显著的低于B组患者（P<0.05）；术前，A组和B组患者的FEV1%、FVC%、MVV%测定值差异无统计学意义（P>0.05），术后3个月复查，A组患者的FEV1%、FVC%、MVV%测定值均显著高于B组患者（P<0.05）；手术后，A组患者的并发症发生率（7.41%）低于B组患者（13.33%），但是差异无统计学意义（P>0.05）。结论:VATS解剖性肺段切除术治疗Ⅰa期NSCLC患者具有手术创伤小、术后恢复快、对患者肺功能影响更小的优势。     

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。     

Bone Microarchitecture Phenotypes Identified in Older Adults Are Associated With Different Levels of Osteoporotic Fracture Risk

Prevalence of osteoporosis is more than 50% in older adults, yet current clinical methods for diagnosis that rely on areal bone mineral density (aBMD) fail to detect most individuals who have a fragility fracture. Bone fragility can manifest in different forms, and a “one-size-fits-all” approach to diagnosis and management of osteoporosis may not be suitable. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides additive information by capturing information about volumetric density and microarchitecture, but interpretation is challenging because of the complex interactions between the numerous properties measured. In this study, we propose that there are common combinations of bone properties, referred to as phenotypes, that are predisposed to different levels of fracture risk. Using HR-pQCT data from a multinational cohort (n = 5873, 71% female) between 40 and 96 years of age, we employed fuzzy c-means clustering, an unsupervised machine-learning method, to identify phenotypes of bone microarchitecture. Three clusters were identified, and using partial correlation analysis of HR-pQCT parameters, we characterized the clusters as low density, low volume, and healthy bone phenotypes. Most males were associated with the healthy bone phenotype, whereas females were more often associated with the low volume or low density bone phenotypes. Each phenotype had a significantly different cumulative hazard of major osteoporotic fracture (MOF) and of any incident osteoporotic fracture (p < 0.05). After adjustment for covariates (cohort, sex, and age), the low density followed by the low volume phenotype had the highest association with MOF (hazard ratio = 2.96 and 2.35, respectively), and significant associations were maintained when additionally adjusted for femoral neck aBMD (hazard ratio = 1.69 and 1.90, respectively). Further, within each phenotype, different imaging biomarkers of fracture were identified. These findings suggest that osteoporotic fracture risk is associated with bone phenotypes that capture key features of bone deterioration that are not distinguishable by aBMD. © 2021 American Society for Bone and Mineral Research (ASBMR).     

Patient preferences and predicted relative uptake for targeted therapies in metastatic colorectal cancer: a discrete choice experiment

Abstract

Objective

Ras wild-type metastatic colorectal cancers (mCRC) may be treated with anti-vascular endothelial growth factor (VEGF) or anti-epidermal growth factor receptor (EGFR) agents. We aim to estimate patients’ preferences for mCRC treatment and relative importance of cost, efficacy improvement, avoidance of side effects and therapy convenience, and relative uptake between profiles that resemble Bevacizumab (anti-VEGF) and Cetuximab (anti-EGFR), two commonly prescribed mCRC targeted therapies.