Tratamiento del síndrome de Budd-Chiari agudo intraoperatorio mediante colocación de prótesis de silicona

Complete liver mobilization for major resections sometimes causes liver tilting due to the release of the suspensory elements of the liver. Rarely this may take to a liver abnormal position with acute obstruction to venous flow at the suprahepatic level (Budd-Chiari syndrome). To avoid this complication, techniques such as post-operative stent implantation have been described. The case of a patient who underwent a complete mobilization of the liver for resection of the inferiour venous cava and a right renal tumor, was reported. After that, an acute Budd-Chiari Syndrome was observed caused of the liver malposition, which was solved with the placement of two silicone prostheses in the liver cell.     

Comparing Radiomics features of tumour and healthy liver tissue in a limited CT dataset: A machine learning study

IntroductionLiver cancer lesions on Computed Tomography (CT) withholds a great amount of data, which is not visible to the radiologists and radiographer. Radiomics features can be extracted from the lesions and used to train Machine Learning (ML) algorithms to predict between tumour and liver tissue. The purpose of this study was to investigate and classify Radiomics features extracted from liver tumours and normal liver tissue in a limited CT dataset.MethodsThe Liver Tumour Segmentation Benchmark (LiTS) dataset consisting of 131 CT scans of the liver with segmentations of tumour tissue and healthy liver was used to extract Radiomic features. Extracted Radiomic features included size, shape, and location extracted with morphological and statistical techniques according to the International Symposium on Biomedical Imaging manual. Relevant features was selected with chi2 correlation and principal component analysis (PCA) with tumour and healthy liver tissue as outcome according to a consensus between three experienced radiologists. Logistic regression, random forest and support vector machine was used to train and validate the dataset with a 10-fold cross-validation method and the Grid Search as hyper-parameter tuning. Performance was evaluated with sensitivity, specificity and accuracy.ResultsThe performance of the ML algorithms achieved sensitivities, specificities and accuracy ranging from 96.30% (95% CI: 81.03%–99.91%) to 100.00% (95% CI: 86.77%–100.00%), 91.30% (95% CI: 71.96%–98.93%) to 100.00% (95% CI: 83.89%–100.00%)and 94.00% (95% CI: 83.45%–98.75%) to 100.00% (95% CI: 92.45%–100.00%), respectively.ConclusionML algorithms classifies Radiomics features extracted from healthy liver and tumour tissue with perfect accuracy. The Radiomics signature allows for a prognostic biomarker for hepatic tumour screening on liver CT.Implications for practiceDifferentiation between tumour and liver tissue with Radiomics ML algorithms have the potential to increase the diagnostic accuracy, assist in the decision-making of supplementary multiphasic enhanced medical imaging, as well as for developing novel prognostic biomarkers for liver cancer patients.     

Interactions between human microbiome,liver diseases,and immunosuppression after liver transplant

In liver transplant patients, solid tumors and post-transplant lymphoproliferative disorders have emerged as significant long-term mortality causes. In addition, it is assumed that de novo malignancy after liver transplantation (LT) is the second-leading cause of death after cardiovascular complications. Well-established risk factors for post-transplant lymphoproliferative disorders and solid tumors are calcineurin inhibitors, tacrolimus, and cyclosporine, the cornerstones of all immunosuppressive therapies used after LT. The loss of immunocompetence facilitated by the host immune system due to prolonged immunosuppressive therapy leads to cancer development, including LT patients. Furthermore, various mechanisms such as bacterial dysbiosis, activation through microbe-associated molecular patterns, leaky gut, and bacterial metabolites can drive cancer-promoting liver inflammation, fibrosis, and genotoxicity. Therefore, changes in human microbiota composition may contribute further to de novo carcinogenesis associated with the severe immunosuppression after LT.     

复发性肝癌再次手术方式的选择:一项基于倾向评分匹配的研究

目的本研究基于倾向评分匹配(PSM)比较挽救性肝移植(sLT)及再次肝切除(RR)的疗效,同时探讨影响复发性肝癌患者预后的危险因素。方法回顾分析2012年1月至2018年8月宁波大学附属李惠利医院124例肝癌切除术后肝内复发再手术的患者,按照再次所行手术方式的不同,分成sLT组(46例)和RR组(78例),通过1∶1倾向评分匹配(PSM),筛选出34例患者用于数据分析,比较匹配前后两组的临床基本资料、总生存期(OS)及无瘤生存期(DFS)来探讨两种手术方式的优劣以及影响患者预后的因素。结果匹配前sLT组的术前血清总胆红素、多发肿瘤的比例、术前行经导管动脉化疗栓塞术(TACE)的比例及符合米兰标准的比例均要高于RR组(P<0.05),最大肿瘤直径sLT组要小于RR组(P<0.05);手术时间、术中出血量、微血管侵犯(MVI)阳性率及术后ClavienⅢ级以上的并发症的比例均要高于RR组(P<0.05)。匹配后sLT组的手术时间、术中出血量、术后并发症ClavienⅢ以上的比例均高于RR组(P<0.05);sLT组的1、3、5年OS与RR组相比差异无统计学意义(P>0.05),sLT组的1、3、5年DFS与RR组相比较优(P<0.05);甲胎蛋白(AFP)≥100μg/L是OS的独立危险因素,手术方式、AFP≥100μg/L是DFS的独立危险因素。结论sLT组术前病情更重,手术时间、术中出血量及术后严重并发症率高于RR组;sLT组较RR组可取得更长的DFS,但对OS来说,两组之间差异无统计学意义。     

缺氧诱导因子-1α参与肝纤维化形成机制研究进展

目的 肝纤维化是一种由于反复肝损伤而导致肝组织细胞外基质过多沉积导致的疾病。缺氧损伤为肝损伤的一部分,缺氧诱导因子-1α(HIF-1α)是响应缺氧应激的关键转录因子,在肝纤维化组织和活化的肝星状细胞(HSC)表达显著增加。目前,通过对大量HIF-1α依赖性基因和信号通路的研究,确认这些基因及其通路的变化参与肝纤维化发展过程,并可能在肝纤维化发生发展过程中起关键作用。本文综述了HIF-1α相关的信号通路参与肝纤维化发展的相关机制,并对上游影响HIF-1α合成和降解的相关信号通路进行了阐述,为其作为新型治疗靶点的可能潜力提供依据。