卵巢癌中OPN、αv133和VEGF的表达与侵袭转移的关系

目的探讨骨桥蛋白（OPN）、整合素αvB3和血管内皮生长因子（VEOF）在上皮性卵巢癌（EOC）中的表达及其临床意义,寻求卵巢癌可能的肿瘤标记物。方法采用免疫组化方法,检测50例卵巢癌组织、20例卵巢良性肿瘤组织及10例正常卵巢组织标本中骨桥蛋白、整合素αvβ3和血管内皮生长因子的表达,分析它们与患者临床病理参数的关系及其之间的相互关系。结果骨桥蛋白、整合素αvβ3和血管内皮生长因子在卵巢癌组的表达率均明显高于正常组和良性肿瘤组（X^2值分别为41．302、28．544、28．848,均P〈0．05）。在晚期卵巢癌中的骨桥蛋白、整合素αvβ3和血管内皮生长因子阳性表达率均高于早期的卵巢癌（X^2值分别为7．728、23．000、9．432,均P〈0．05）;分化差卵巢癌中骨桥蛋白和整合素αvβ3阳性表达率明显高于分化好的卵巢癌（X^2值分别为6．542、12．167,均P〈0．05）;有淋巴结转移的卵巢癌中,骨桥蛋白、整合素αvβ3和血管内皮生长因子阳性表达率高于无淋巴结转移的卵巢癌（X^2值分别为6．629、13．782、4．726,均P〈0．05）。骨桥蛋白与整合素αvβ3、血管内皮生长因子在卵巢癌中的表达均显著相关（,值分别为4．973、5．821,均P〈0．05）。骨桥蛋白、整合素αvβ3和血管内皮生长因子的共表达与卵巢癌的淋巴结转移显著相关（X^2=9．343,P〈0．05）。结论骨桥蛋白、整合素αvβ3和血管内皮生长因子的过度表达可能促进了卵巢癌的浸润转移,骨桥蛋白表达与整合素αvβ3、血管内皮生长因子表达分别具有正协同作用。联合检测骨桥蛋白、整合检测骨桥蛋白、整合素αvβ和血管内皮生长因子可作为预测卵巢癌浸润转移及评价患者预后的生物学指标。     

Predictive value of tumor markers in patients with recurrent hepatocellular carcinoma in different vascular invasion pattern

BACKGROUND: Four tumor markers for hepatocellular carcinoma(HCC), alpha-fetoprotein(AFP), glypican-3(GPC3), vascular endothelial growth factor(VEGF) and des-gammacarboxy prothrombin(DCP), are closely associated with tumor invasion and patient's survival. This study estimated the predictability of preoperative tumor marker levels along with pathological parameters on HCC recurrence after hepatectomy.METHODS: A total of 140 patients with HCC who underwent hepatectomy between January 2012 and August 2012 were enrolled. The demographics, clinical and follow-up data were collected and analyzed. The patients were divided into two groups: patients with macroscopic vascular invasion(Ma VI +) and those without Ma VI(Ma VI-). The predictive value of tumor markers and clinical parameters were evaluated by univariate and multivariate analysis.RESULTS: In all patients, tumor size(8 cm) and Ma VI were closely related to HCC recurrence after hepatectomy. For Ma VI+ patients, VEGF(900 pg/m L) was a significant predictor for recurrence(RR=2.421; 95% CI: 1.272-4.606; P=0.007). The 1- and 2-year tumor-free survival rates for Ma VI+ patients with VEGF ≤900 pg/m L versus for those with VEGF 900 pg/m L were 51.5% and 17.6% versus 19.0% and 4.8%(P0.001). For Ma VI- patients, DCP 445 m Au/m L and tumor size 8 cm were two independent risk factors for tumor recurrence(RR=2.307, 95% CI: 1.132-4.703, P=0.021; RR=3.150, 95% CI: 1.392-7.127, P=0.006; respectively). The 1- and 2-year tumor-free survival rates for the patients with DCP ≤445 m Au/m L and those with DCP 445 m Au/m L were 90.4% and 70.7% versus 73.2% and 50.5% respectively(P=0.048). The 1-and 2-year tumor-free survival rates for the patients with tumor size ≤8 cm and 8 cm were 83.2% and 62.1% versus 50.0% and 30.0%, respectively(P=0.003).CONCLUSIONS: The Ma VI+ patients with VEGF ≤900 pg/m L had a relatively high tumor-free survival than those with VEGF 900 pg/m L. In the Ma VI- patients, DCP 445 m Au/m L and tumor size 8 cm were predictive factors for postoperative recurrence.     

C反应蛋白和白蛋白比值预测单发小肝癌患者微血管侵犯的价值研究

背景 既往对肝癌微血管侵犯病理诊断的重要性重视不够，目前国内外缺乏对微血管侵犯统一的病理诊断标准，也未将微血管侵犯列为病理常规诊断指标。C反应蛋白/白蛋白比值（CAR）作为新型系统性炎性因子，与肝癌的增殖、侵袭转移等恶性生物学行为密切相关。 目的 探讨CAR预测单发小肝癌患者微血管侵犯的价值。 方法 选择2017年6月至2021年6月在新疆医科大学第一附属医院行肝切除术的单个、肿瘤直径≤5 cm的术后病理检查证实为肝癌的患者346例为研究对象。收集患者一般资料，并计算CAR。绘制CAR预测单发小肝癌患者微血管侵犯的受试者工作特征（ROC）曲线，并计算CAR的最佳诊断截点，根据CAR最佳诊断截点将患者进行分组，采用1∶1最近邻居倾向性评分匹配（PSM）法将Logistic模型估计的倾向性评分相近患者进行配对，得到两组间各临床特征比较均衡性较高的样本。比较匹配后两组患者微血管侵犯率，采用Logistic回归分析评估匹配前、后CAR对单发小肝癌患者微血管侵犯的预测价值。 结果 346例患者中微血管侵犯阳性131例（37.9%），微血管侵犯阴性215例（62.1%）。ROC曲线分析结果显示，CAR预测单发小肝癌患者微血管侵犯的灵敏度为82.9%，特异度为76.4%，ROC曲线下面积为0.787〔95%CI（0.697，0.877）〕，最佳诊断截点为0.03。根据CAR最佳诊断截点，将患者分为CAR<0.03组（A组，n=145）和CAR≥0.03组（B组，n=201）。采用1∶1最近邻居PSM法，共92对匹配成功，匹配后两组临床资料均衡。匹配后，B组患者微血管侵犯发生率〔43.5%（40/92）〕高于A组〔13.0%（12/92）〕（χ2=6.314，P=0.013）。采用3种Logistic回归分析结果显示，匹配前、后CAR均为单发小肝癌患者微血管侵犯的独立影响因素（P<0.05）。 结论 CAR作为新型系统性炎症指标，可用于预测单发小肝癌微血管侵犯，当CAR≥0.03时提示单发小肝癌微血管侵犯发生率较高。     

A novel prognostic nomogram based on microvascular invasion and hematological biomarkers to predict survival outcome for hepatocellular carcinoma patients

PurposeThis study aimed to develop and validate a nomogram for overall survival (OS) prediction in which combine clinical characteristics and hematological biomarkers in patients with hepatocellular carcinoma (HCC).MethodsWe performed a retrospective analysis of 807 HCC patients. All the clinical data of these patients were collected through electronic medical record (EMR). The independent predictive variables were identified by cox regression analysis. We tested the accuracy of the nomograms by discrimination and calibration, and then plotted decision curves to assess the benefits of nomogram-assisted decisions in a clinical context, and compared with the TNM staging systems and microvascular invasion (MVI) on HCC prognosis.ResultsThe primary cohort consisted of 545 patients with clinicopathologically diagnosed with HCC from 2008 to 2013, while 262 patients from 2014 to 2016 in external validation cohort. Variables included in the nomograms were TNM Stage, microvascular invasion (MVI), alpha fetoprotein (AFP), platelet to lymphocyte ratio (PLR) and prothrombin time (PT). The C-index of nomogram was 0.768, which was superior than the C-index of TNM Stage (0.660, P < 0.001) and MVI(0.664, P < 0.001) alone in the primary cohort. In the validation cohort, the models had a C-index of 0.845, and were also statistically higher when compared to C-index values for TNM Stage (0.687, P < 0.001) and MVI(0.684, P < 0.001). Calibration curves showed adequate calibration of predicted and reported OS prediction throughout the range of HCC outcomes. Decision curve analysis demonstrated that the nomogram was clinically useful than the TNM Stage and MVI alone. Moreover, patients were divided into three distinct risk groups for OS by the nomogram: low risk group, middle risk group and a high risk group, respectively.ConclusionThe nomogram presents more accurate and useful prognostic power, which could be used to predict OS for patients with HCC.     

The impact of additional margin coagulation with radiofrequency in liver resections with subcentimetric margin: can we improve the oncological results? A propensity score matching study

BackgroundWhereas the usefulness of radiofrequency (RF) energy as haemostatic method in liver surgery has become well established in the last decades, its intentional application on resection margins with the aim of reducing local recurrence is still debatable. Our goal was to compare the impact of an additional application of RF energy on the top of the resection surface, namely additional margin coagulation (AMC), on local recurrence (LR) when subjected to a subcentimeter margin.MethodsWe retrospectively analyzed 185 patients out of a whole cohort of 283 patients who underwent radical hepatic resection with subcentimetric margin. After propensity score adjustment, patients were classified into two balanced groups according to whether RF was applied or not.ResultsNo significant differences were observed within groups in baseline characteristics after PSM adjustment. The LR rate was significantly higher in the Control than AMC Group: 12 patients (14.5%) vs. 4 patients (4.8%) (p = 0.039). The estimated 1, 3, and 5-year LR-free survival rates of patients in the Control and AMC Group were: 93.5%, 86.0%, 81.0% and 98.8%, 97.2%, 91.9%, respectively (p = 0.049). Univariate Cox analyses indicated that the use of the RF applicator was significantly associated with lower LR (HR = 0.29, 95% confidence interval 0.093–0.906, p = 0.033). The Control Group showed smaller coagulation widths than the AMC group (p < 0.001).ConclusionsAn additional application of RF on the top of the resection surface is associated with less local hepatic recurrence than the use of conventional techniques.     

Renal cell carcinoma staging: pitfalls,challenges, and updates

Renal cell carcinoma (RCC) is unusual among cancers in that it often grows as a spherical, well‐circumscribed mass. Increasing tumour size influences the pathological pT stage category within pT1 and pT2, with cutoffs of 40, 70 and 100 mm; however, with increasing size also comes a sharp increase in the likelihood of renal sinus or renal vein tributary invasion, such that clear cell RCC rarely reaches 70 mm without invading one of these. To clarify some previous challenges in assigning tumour stage, the American Joint Committee on Cancer 2016 tumor–node–metastasis classification has removed the requirements than vein invasion be recognised grossly and that vein walls contain muscle for the diagnosis of vein invasion. Renal pelvis invasion has also been added as an additional route to pT3a. Multinodularity or finger‐like extensions from a renal mass should be viewed with great suspicion for the possibility of vein or renal sinus invasion, and, as tumour size increases to over 40–50 mm, thorough sampling of the renal sinus interface should always be undertaken. With increasing interest in adjuvant therapy in renal cancer, the pathologist's role in RCC staging will continue to be an important prognostic parameter and a tool for selection of patients for enrolment in clinical trials.     

Deep learning radiomics based on contrast enhanced computed tomography predicts microvascular invasion and survival outcome in early stage hepatocellular carcinoma

ObjectiveTo evaluate the performance of a deep learning (DL)-based radiomics strategy on contrast-enhanced computed tomography (CT) to predict microvascular invasion (MVI) status and clinical outcomes, recurrence-free survival (RFS) and overall survival (OS) in patients with early stage hepatocellular carcinoma (HCC) receiving surgical resection.MethodsAll 283 eligible patients were included retrospectively between January 2008 and December 2015, and assigned into the training cohort (n = 198) and the testing cohort (n = 85). We extracted radiomics features via handcrafted radiomics analysis manually and DL analysis of pretrained convolutional neural networks via transfer learning automatically. Support vector machine was adopted as the classifier. A clinical-radiological model for MVI status integrated significant clinical features and the radiological signature generated from the radiological model with the optimal area under the receiver operating characteristics curve (AUC) in the testing cohort. Otherwise, DL-based prognostic models were constructed in prediction of recurrence and mortality via Cox proportional hazard analysis.ResultsThe clinical-radiological model for MVI represented an AUC of 0.909, accuracy of 96.47%, sensitivity of 90.91%, specificity of 97.30%, positive predictive value of 83.33%, and negative predictive value of 98.63% in the testing cohort. The clinical-radiological models for identification of RFS and OS outperformed prediction performance of the clinical model or the DL signature alone. The DL-based integrated model for prognostication showed great predictive value with significant classification and discrimination abilities after validation.ConclusionsThe integrated DL-based radiomics models achieved accurate preoperative prediction of MVI status, and might facilitate predicting tumor recurrence and mortality in order to optimize clinical decisions for patients with early stage HCC.