前列腺(厚径)~2/宽径计算值与高最大尿流率关系

本文分析82例男性高最大尿流率与前列腺（厚径）2／宽径计算值、排尿前膀胱容积、残余尿及分布、年龄等相关因素。高最大尿流率与前列腺大小形态有关．与通尿肌增生或膀胱内压升高相关更明显；其残余尿与尿流率大小无关，而残余尿分布与排尿前膀胱容积有关，出现在排尿前膀胱容积大者，年龄相对较小、最大尿流率随年龄增加而降低，但本组中已不再显示年龄均值差异。加之在最大尿流率诺模图标准差区间等综合分析，有助诊断与比较。     

cN0期甲状腺乳头状癌右喉返神经深层淋巴结转移的影响因素及预测模型构建

背景与目的：甲状腺乳头状癌（PTC）患者往往伴有中央区淋巴结转移,单/双侧甲状腺腺叶切除加中央区淋巴结清扫（CLND）是主要的治疗手段。然而因解剖结构的差异,对于颈部淋巴结在临床上呈阴性（c N0）的PTC患者是否需要进行右侧喉返神经深层淋巴结（LN-pr RLN）清扫,仍存在争议。目前,有关影响c N0期PTC患者LN-pr RLN转移因素的研究较少,缺乏个体化的定量预测LN-pr RLN转移风险的工具。因此,本研究旨在探讨影响c N0期PTC患者LN-pr RLN转移的因素,并构建个体化预测模型,以提供行LN-pr RLN清扫的决策依据。方法：回顾性分析2019年6月—2022年12月武汉大学人民医院乳腺甲状腺外科行甲状腺手术的410例PTC患者的临床病理资料,按7∶3的比例随机分为训练组和验证组。根据LN-pr RLN术后病理转移结果,将患者分为LN-pr RLN阳性组和LN-pr RLN阴性组。收集患者年龄、性别、体质量指数（BMI）、甲状腺超声结果、甲状腺功能、术后病理、淋巴结转移情况等资料,并通过单因素分析及多因素Logistic回归分析确定影响c N0期PTC LN-pr...     

胆囊鳞状细胞癌预后模型的构建与验证

背景与目的 胆囊鳞状细胞癌（GSCC）是胆囊癌中一种罕见的病理学类型,占胆囊癌的1%~4%。该类型肿瘤预后差,目前关于GSCC的文献报道主要是个案报道和小样本系列病例报道,由于缺乏大样本高质量的临床研究证据,目前临床上尚无针对GSCC的治疗指南、共识和个体化的预后评价工具。因此,本研究通过SEER数据库中的大样本数据构建GSCC患者预后列线图,旨在精准化、个体化评价GSCC患者的预后,为临床决策制定提供参考。方法 提取SEER 数据库中2000—2019年期间经病理确诊的GSCC患者的临床资料,按照7∶3的比例,将数据随机划分为训练集和验证集,在训练集中,分别采用多变量Cox比例风险模型和LASSO回归筛选影响GSCC患者预后的独立因素,利用这些因素,构建用于预测GSCC患者在3个月和6个月的肿瘤特异性生存期（CSS）和总生存期（OS）的列线图模型。随后,在训练集中,利用一致性指数（C指数）、ROC曲线和校准曲线,分别在训练集和验证集,对模型进行内部和外部验证,以评估模型的准确度和预测能力。结果 本研究共纳入257例患者,其中训练集179例,验证集78例。在训练集和验证集中,患者的中位随访时间分别为3（1~7）个月和4（2~8）个月。两组之间基线资料均衡可比。多变量Cox比例风险模型分析显示,年龄、SEER分期、手术和化疗是GSCC患者OS和CSS的独立影响因素（均P<0.05）。LASSO回归分析显示,年龄、SEER分期、放疗、手术和化疗与GSCC患者的OS相关;年龄、SEER分期、手术和化疗与GSCC患者的CSS相关。基于这些独立预后影响因素,构建了用于预测GSCC患者在3、6个月的OS和CSS的列线图。对模型的验证结果表明,训练集和验证集中,OS的C指数分别为0.739（95% CI=0.700~0.780）和0.729（95% CI=0.660~0.800）;CSS的C指数分别为0.750（95% CI=0.710~0.790）和0.741（95% CI=0.670~0.810）。ROC曲线分析显示,曲线在训练集和验证集的AUC值均>0.8;校准曲线分析表明,通过模型预测的3、6个月的OS和CSS与GSCC患者真实的3、6个月的OS和CSS有较好的重合,两者均靠近理想的45°参考线,表现出良好的一致性。结论 年龄、SEER分期、手术、放疗和化疗是GSCC患者预后的独立影响因素。所构建的列线图预测模型具有良好的预测价值,有利于临床对GSCC患者选择个性化治疗。     

预测T4a期胃癌腹膜转移危险因素的列线图模型的建立与验证

目的探究T_4a期胃癌发生腹膜转移的危险因素,以此为训练组构建列线图模型,并进行内验证及外验证。 方法回顾性分析2011年1月至2017年12月394例行胃切除术+D2淋巴结清扫术影像学诊断为T_4a期胃癌患者的临床资料,其中将2011年1月至2015年12月224例患者作为训练集,2016年1月至2017年12月170例患者作为验证集。根据术前影像学表现判断有无腹膜转移并且最终经病理资料证实,采用SPSS 24.0软件通过t检验、秩和检验或卡方检验对发生腹膜转移的危险因素进行统计学分析,经单因素和多因素Logistic回归筛选T_4a期胃癌患者发生腹膜转移的潜在危险因素,采用R软件（版本4.0.2）建立列线图模型。采用Bootstrap法进行内验证,采用ROC曲线评价模型的符合度并计算95%CI,绘制校准曲线评价模型的符合度。绘制DCA曲线评价模型的临床获益度。 结果224例训练集患者中,共37（16.5%）例患者发生腹膜转移,验证集患者中23（13.5%）例患者发生腹膜转移,多因素分析显示糖类抗原125（CA125）、腹水、术前白蛋白（ALB）和肿瘤分化程度是胃癌患者发生腹膜转移的独立危险因素。绘制ROC曲线结果提示,内部训练集AUC曲线下面积为0.783（95%CI：0.699-0.867）,外部验证集AUC曲线下面积为0.848（95%CI：0.763-0.932）;且以此建立的列线图模型具有良好的区分度、校准度和临床获益度。 结论基于4个独立危险因素的列线图模型对T_4a期胃癌患者腹膜转移的发生具有良好的区分度和校准度,可用于术前对T_4a期胃癌患者腹膜转移风险进行评估,具有一定的临床推广和参考价值。     

The prognostic value of the neutrophil-lymphocyte ratio in renal oncology: A review

Background

The neutrophil-lymphocyte ratio (NLR) is a biological marker of inflammation with a significant prognostic value in the field of oncology.

肝硬化患者高危食管静脉曲张列线图预测模型的构建研究

背景　食管静脉曲张破裂出血是肝硬化门静脉高压最致命的并发症,早期诊断和治疗食管静脉曲张能显著降低肝硬化门脉高压患者的病死率。然而,目前临床上尚缺乏统一的预测食管静脉曲张破裂出血风险的无创模型。目的　构建肝硬化患者高危食管静脉曲张的列线图预测模型。方法　选取2018年1月至2020年10月在浙江大学医学院附属金华医院确诊为肝硬化的患者为研究对象,收集其入院24 h内的实验室检查指标,并进行胃镜检查,评估食管静脉曲张程度。采用CT扫描仪对患者进行腹部增强CT扫描。采用LASSO回归模型、十折交叉验证法获得高危食管静脉曲张的最佳风险预测因子子集,并采用多因素Logistic回归模型确定高危食管静脉曲张的风险预测因子。根据风险预测因子,应用R软件构建列线图预测模型。采用受试者工作特征（ROC）曲线评估列线图预测模型的预测能力。采用Bootstrap法进行模型内验证,计算一致性指数（C-index）以评估模型区分度;绘制校准曲线以评估模型的校准度。结果　共纳入204例患者,其中低危食管静脉曲张92例,高危食管静脉曲张112例。胃左静脉内径〔OR=2.174,95%CI（1.547,3.056）,P<0.001〕、血小板计数〔OR=0.970,95%CI（0.950,0.991）,P<0.001〕、脾厚径〔OR=1.061,95%CI（1.003,1.123）,P=0.039〕、腹腔积液〔OR=3.091,95%CI（1.768,5.404）,P<0.001〕是高危食管静脉曲张的独立风险预测因子。构建的列线图预测模型预测肝硬化患者发生高危食管静脉曲张的ROC曲线下面积为0.949〔95%CI（0.921,0.978）〕;经内部验证,该模型的C-index为0.951;校准曲线显示该模型预测结果与实际结果的一致性良好。结论　本研究基于胃左静脉内径、血小板计数、脾厚径、腹腔积液4项独立风险预测因子构建出肝硬化患者高危食管静脉曲张的列线图预测模型,并具有良好的区分度与校准度,有助于在肝硬化患者中筛选出高危食管静脉曲张者。     

Predictive and Prognostic Models in Radical Prostatectomy Candidates: A Critical Analysis of the Literature

Context

Numerous predictive and prognostic tools have recently been developed for risk stratification of prostate cancer (PCa) patients who are candidates for or have been treated with radical prostatectomy (RP).

Objective

To critically review the currently available predictive and prognostic tools for RP patients and to describe the criteria that should be applied in selecting the most accurate and appropriate tool for a given clinical scenario.

Evidence acquisition

A review of the literature was performed using the Medline, Scopus, and Web of Science databases. Relevant reports published between 1996 and January 2010 identified using the keywords prostate cancer, radical prostatectomy, predictive tools, predictive models, and nomograms were critically reviewed and summarised.

Evidence synthesis

We identified 16 predictive and 22 prognostic validated tools that address a variety of end points related to RP. The majority of tools are prediction models, while a few consist of risk-stratification schemes. Regardless of their format, the tools can be distinguished as preoperative or postoperative. Preoperative tools focus on either predicting pathologic tumour characteristics or assessing the probability of biochemical recurrence (BCR) after RP. Postoperative tools focus on cancer control outcomes (BCR, metastatic progression, PCa-specific mortality [PCSM], overall mortality). Finally, a novel category of tools focuses on functional outcomes. Prediction tools have shown better performance in outcome prediction than the opinions of expert clinicians. The use of these tools in clinical decision-making provides more accurate and highly reproducible estimates of the outcome of interest. Efforts are still needed to improve the available tools’ accuracy and to provide more evidence to further justify their routine use in clinical practice. In addition, prediction tools should be externally validated in independent cohorts before they are applied to different patient populations.

Conclusions

Predictive and prognostic tools represent valuable aids that are meant to consistently and accurately provide most evidence-based estimates of the end points of interest. More accurate, flexible, and easily accessible tools are needed to simplify the practical task of prediction.     

Preoperative nomograms for predicting extracapsular extension in Korean men with localized prostate cancer: a multi-institutional clinicopathologic study

We developed a nomogram to predict the probability of extracapsular extension (ECE) in localized prostate cancer and to determine when the neurovascular bundle (NVB) may be spared. Total 1,471 Korean men who underwent radical prostatectomy for prostate cancer between 1995 and 2008 were included. We drew nonrandom samples of 1,031 for nomogram development, leaving 440 samples for nomogram validation. With multivariate logistic regression analyses, we made a nomogram to predicts the ECE probability at radical prostatectomy. Receiver operating characteristic (ROC) analyses were also performed to assess the predictive value of each variable alone and in combination. The internal validation was performed from 200 bootstrap re-samples and the external validation was also performed from the another cohort. Overall, 314 patients (30.5%) had ECE. Age, Prostate specific antigen (PSA), biopsy Gleason score, positive core ratio, and maximum percentage of biopsy tumor were independent predictors of the presence of ECE (all P values <0.05). The nomogram predicted ECE with good discrimination (an area under the ROC curve of 0.777). Our nomogram allows for the preoperative identification of patients with an ECE and may prove useful in selecting patients to receive nerve sparing radical prostatectomy.