基于术前FAR和SⅡ的预后模型与TNM分期系统在评估胰腺癌根治术患者预后中的比较

目的 探讨胰腺导管腺癌患者术前纤维蛋白原/白蛋白比值（FAR）和系统免疫炎症指数（SⅡ）对预后的预测价值。方法 受试者工作特征（ROC）曲线确定FAR、SⅡ的最佳截断值,并进行分组。Cox风险比例模型分析胰腺癌根治术的预后影响因素,依此建立列线图（Nomogram）预后模型。C-index、AUC和校准曲线评估列线图的辨别和校准能力。DCA曲线评估列线图的临床有效性。结果 术前FAR及SⅡ的最佳截断值分别为0.095和532.945。Cox比例风险回归模型显示：FAR≥0.095、SⅡ≥532.945、CA199≥450.9U/ml、肿瘤最大径≥4cm、术后未进行化疗是影响胰腺癌预后不佳的独立危险因素（P<0.05）。C-index、AUC、校准曲线和DCA曲线表明,列线图预后模型的辨别能力、校准能力和临床有效性均优于TNM分期系统预后模型。结论 构建的Nomogram预后模型较TNM分期预后模型具有更高的准确性、区分度及临床获益。     

基于SEER数据库绘制列线图分析软骨肉瘤患者预后相关因素

目的:分析软骨肉瘤流行病学特征及影响预后的相关因素,并且绘制列线图来个体化预测患者远期生存率。方法:收集SEER数据库2004至2015年诊断的1 453例软骨肉瘤患者的临床数据,回顾性分析软骨肉瘤患者流行病学特征及影响患者预后的相关危险因素,使用随机数字表法将所有纳入对象以7∶3分为建模组（1 017例）和验证组（436例）并构建列线图进行内部验证,预测软骨肉瘤患者3年、5年的生存率,使用多因素COX风险比例模型来确定独立因素,采用一致性指数(C-index)及校准曲线评估该预测模型的准确性。结果:软骨肉瘤患者男女性别比为:1.23∶1,年龄≥50岁患者占比为55.7%,最常见的发病部位是下肢骨,多因素分析提示,影响软骨肉瘤患者预后的因素包括年龄、性别、肿瘤原发部位、肿瘤大小、病理分级、AJCC TNM分期、手术方式、是否化疗、肿瘤大小;建模组与验证组1、3、5年ROC曲线AUC值分别为:0.87、0.838、0.807,0.864、0.754、0.755;列线图C-index指数为:0.805。结论:列线图可以准确预测软骨肉瘤患者生存率,具有较好的预测精度,有助于对患者进行个性化的预后评估和指导临床决策。     

结肠癌切除术患者预后列线图模型及危险分层系统构建与验证

  目的  基于大样本量,构建个体化预测模型及危险分层系统。  方法  从美国SEER临床数据库中,筛选结肠癌术后患者,进行模型构建,并筛选一组独立的中国人群,用于外部验证。经过单因素与多因素Cox回归分析,筛选出独立预后指标,并全部纳入用于构建列线图预测模型。通过计算一致性指数（C-index）及绘制校准曲线,检验模型准确性。  结果  列线图模型共纳入11个独立预后因子,C-index在训练组、内部验证组及外部验证组分别为0.768,0.761和0.759,均>0.7,且优于第7版美国癌症联合委员会（AJCC）-TNM分期系统（0.729,0.720,0.735）。校准曲线显示,模型预测效果与实际生存相吻合,进一步验证了模型的区分及校准能力。通过决策树分析,依据模型预测个体风险评分,进行危险分层,模型的实际应用价值得到确定。  结论  该列线图预测模型能够较准确预测结肠癌术后患者预后状态,并较传统TNM分期系统有所改善,基于预测模型的危险分层系统,能够更好地区分高危患者,并指导选择临床治疗措施。      

基于SEER数据库的儿童青少年室管膜瘤预后Nomogram模型的构建和验证

目的 基于SEER数据库构建并验证儿童青少年室管膜瘤的Nomogram预测模型。方法 获取1975—2016年SEER数据库临床病理信息,单变量和多变量Cox比例风险回归模型确定潜在的预测因素,构建Nomogram模型预测5年和10年总生存率。通过一致性指数、受试者工作特征曲线和校准曲线值来评估列线图的辨别能力。决策曲线分析评价列线图模型的临床实用性。结果 根据建模组多变量Cox比例风险回归模型筛选的变量建立风险Nomogram图,建模组和验证组的C-index分别为0.713（95%CI: 0.680~0.747）和0.734（95%CI: 0.681~0.787）。ROC曲线表明该模型具有较好的区分度。校准曲线显示Nomogram模型与理想模型一致性尚可,决策曲线分析获益性尚可。结论 基于诊断年龄、性别、种族、原发部位、组织学分级、手术方式和登记地点构建的儿童青少年室管膜瘤风险预测Nomogram模型具有良好的区分度与准确度,对临床上为患者提供较准确和个性化的生存预测具有指导作用。     

皮肤梭形细胞黑色素瘤患者生存预测模型的构建及验证

背景与目的：梭形细胞黑色素瘤（spindle cell melanoma,SCM）是一种罕见的黑色素瘤类型,有关SCM患者生存预后的研究较少。通过提取公共数据库中的SCM临床信息,构建并验证皮肤SCM患者5和10年癌症特异性生存率（cancer-specific survival,CSS）和总生存率（overall survival,OS）的生存预测模型。方法：从美国国立癌症研究所监测、流行病学和最终结果（Surveillance, Epidemiology, and End Results,SEER）数据库筛选出共1 445例患者,分成建模组（n=1 011）和验证组（n=434）。通过单因素和多因素COX回归分析确定独立预后影响因素,建立列线图预测模型。利用一致性指数（concordance index,C-index）、受试者工作特征（receiver operating characteristic,ROC）曲线和校准曲线评估模型的区分度和准确性,利用决策曲线分析（decision curve analysis,DCA）评估模型的临床实用性。结果：年龄、肿瘤部位、肿瘤厚度、溃疡、N分期、M分期及手术共7个独立预后影响因素纳入预测模型,CSS和OS预测模型在建模组中的C-index分别为0.778和0.753,在验证组中的C-index为0.749和0.712。建模组5和10年CSS的曲线下面积（area under curve,AUC）分别为0.815和0.825,5和10年OS的AUC分别为0.803和0.825,验证组5和10年CSS的AUC分别为0.777和0.836,5和10年OS的AUC分别为0.754和0.799。校准曲线与45°线贴合良好,DCA显示,列线图模型在较广泛阈概率范围内有临床净收益,具有良好的临床应用价值。结论：列线图对于皮肤SCM患者预后具有良好的预测能力和临床应用价值。     

胰腺导管腺癌术后患者Nomogram预后模型的构建

目的:分析胰腺导管腺癌（pancreatic dust adenocarcinoma，PDAC）术后患者的预后影响因素，建立Nomogram预后模型。方法:收集SEER数据库2004年至2015年PDAC术后患者的临床病理及随访数据。应用倾向得分匹配（propensity score matching，PSM）均衡放疗、化疗基线数据的差异。采用Kaplan-Meier法进行单因素生存分析，Log-rank检验比较生存率的差异；多因素Cox比例风险模型分析PDAC术后患者的独立预后影响因素并建立Nomogram预后模型，其性能通过一致性指数值及决策曲线分析法（decision curve analysis，DCA）进行验证。结果:本研究共纳入10 442例PDAC术后患者。经PSM后生存分析显示化疗（P<0.001）与放疗（P=0.003 7）可改善患者的预后。单因素及多因素分析显示年龄、婚姻状态、分化程度、TNM分期、肿瘤大小、化疗及放疗为PDAC术后患者的独立预后影响因素（P<0.05）。进一步建立的Nomogram预后模型在预测1年、3年及5年总生存方面表现出良好的准确性，内部验证的一致性指数为0.722，较TNM系统一致性指数高（C-index=0.656）。DCA显示Nomogram预后模型较TNM模型具有更高的临床获益。结论:年龄、婚姻状态、分化程度、TNM分期、肿瘤大小、化疗及放疗均为PDAC术后患者的独立预后影响因素（P<0.05），依此建立的Nomogram较传统的AJCC TNM系统具有更高的准确性及临床获益。     

基于SEER数据库构建小细胞肺癌术后患者生存预测模型

目的:证实手术治疗对于小细胞肺癌患者长期生存的作用。鉴定小细胞肺癌术后患者生存影响因素，构建小细胞肺癌术后患者的生存预测模型。与现有的AJCC分期系统、VALSG分期系统和IASLC的分期系统预测性能进行比较。 方法: 选取2004年至2012年SEER数据库中确诊为小细胞肺癌的患者（small cell lung cancer, SCLC），提取相应的变量数据。采用Kaplan-Meier比较不同分期下手术组与非手术组患者的生存状况，并绘制生存曲线。针对手术治疗的SCLC患者，利用赤池信息准则（AIC）筛选变量，基于Cox回归模型构建Nomogram预测模型。比较新模型与AJCC分期系统、VALSG分期系统和IASLC分期的一致性指数（C-index）,评价个模型的预测效能。 结果：通过数据检索，共有45226例SCLC患者入选本研究，其中867例为手术治疗患者。多因素分析发现，影响手术患者预后的因素包括年龄，性别，手术方式，放疗顺序，肿瘤大小，肿瘤侵犯范围，T分期，N分期，淋巴结清扫数量，病理分化程度和远端转移情况。经过赤池信息准则（AIC）筛选，年龄、性别、肿瘤大小、肿瘤侵犯范围、淋巴结侵犯情况、远端转移情况、手术方式、放疗情况、淋巴结清扫数量、病理分化程度共10个变量入选模型。比较4个模型的一致性指数，Nomogram为0.706，AJCC模型为0.700，IASLC模型为0.667，VALSG模型为0.665。Nomogram模型显示最佳的预测准确度。 结论：患者是否接受手术影响小细胞肺癌患者的生存时间。肿瘤的大小，肿瘤侵犯的范围是独立的预后因素。Nomogram生存预测模型的预测性能明显优于其它分期系统。     

局限期可手术食管癌术前放化疗预后因素分析及生存预测模型

目的 利用SEER数据库分析局限期可手术食管癌术前放化疗患者的预后及其相关因素,并建立生存预测列线图,为筛选术前放化疗患者提供一定参考。方法 选取SEER数据库2010-2015年食管癌接受术前放化疗且分期为T1b-4aN0-3M0(2010年AJCC第7版分期)的病例;生存率采用Kaplan-Meier法,单因素分析采用Logrank检验,多因素分析采用Cox模型检验;通过R软件建立预测模型列线图;一致性指数(C-index)及校准曲线用来评价模型准确度。结果 共1697例患者符合条件并可纳入分析。单因素分析显示性别、T分期、N分期、分化程度与总生存(OS)及癌症特异生存(CSS)均相关(P均<0.001),年龄与OS相关(P=0.027)。多因素分析显示年龄、性别、分化程度、N分期与OS相关;性别、分化程度、T分期、N分期与CSS相关(P均<0.05)。将预后相关因素纳入Nomogram预后模型,5年OS、CSS的C-index值分别为0.60、0.61。同样方法建立食管鳞癌亚组患者预后模型,OS及CSS的C-index值为0.62、0.64。结论 性别、临床分期、分化程度为局限期可手术食管癌行术前放化疗者CSS预后因素,根据以上数据建立的列线图可为是否采用术前放化疗联合手术治疗这一模式提供一定参考。     

残余淋巴结短径与食管鳞癌放化疗疗效和预后相关性及Nomogram模型建立

目的 分析残余淋巴结短径与食管鳞癌放化疗疗效及预后之间相关性,并创建Nomogram模型来预测患者预后。方法 收集淮安市第二人民医院自2018—2020年间行放化疗的143例食管鳞癌患者的临床资料,Kaplan-Meier法生存分析并log-rank检验和单因素预后分析,Cox模型多因素预后分析。通过建立Nomogram预测模型来预测食管鳞癌放化疗患者的1、2年无进展生存,并使用C-index、AUC及校准曲线来评估模型效能。结果 Logistic回归分析结果表明分化程度、TNM分期、放疗前后的患者自评-主观全面评定(PG-SGA)评分及残余淋巴结短径是食管鳞癌放化疗患者疗效的独立预测因素。Cox回归分析结果表明分化程度、TNM分期、放疗前后的PG-SGA评分及残余淋巴结短径是食管鳞癌放化疗患者预后的独立预测因素。结论 残余淋巴结短径与食管鳞癌放化疗疗效及预后显著相关,综合临床基线特征后所建立的Nomogram模型是预测患者预后的实用且可靠的工具。     

基于SEER数据库构建肺肉瘤样癌的生存预测模型

目的 分析影响肺肉瘤样癌（PSC）患者预后的因素,构建PSC患者预后列线图预测模型。方法 基于SEER数据库收集1988—2015年间诊断为PSC患者1671例,按照7:3的比例分为建模组和验模组。对建模组患者进行单因素和多因素Cox回归分析影响PSC患者预后的独立因素并构建列线图预测模型,通过一致性指数和校准曲线分别在建模组和验模组进行验证。结果 单因素和多因素分析年龄、性别、组织学类型、TNM分期、肿瘤直径>50 mm、手术、放疗和化疗都是影响PSC患者预后的独立因素。基于独立因素构建列线图预测模型并进行验证。建模组和验模组一致性指数分别为0.790（95%CI: 0.776~0.804）和0.781（95%CI: 0.759~0.803）。建模组和验模组的校准曲线提示预测生存率与实际生存率基本一致。结论 基于多因素分析结果构建的列线图预测模型可预测PSC患者的预后,并且具有较高的准确性和一致性。     

肺腺癌患者列线图预后模型的构建与验证

目的 基于SEER数据库的大样本数据,构建肺腺癌患者生存预后的列线图预测模型.方法 回顾性分析SEER数据库收集的2010—2015年诊断为肺腺癌患者的临床数据.根据影响肺腺癌患者预后的独立因素,采用Lasso Cox回归分析构建列线图模型.C指数和校准曲线评估列线图的判别和校准能力.使用NRI和DCA曲线评估列线图的...     

Development and validation of a new clinical staging system to predict survival for esophageal squamous cell carcinoma patients: Application of the nomogram

IntroductionSurvival of patients with the same clinical stage varies widely and effective tools to evaluate the prognosis utilizing clinical staging information is lacking. This study aimed to develop a clinical nomogram for predicting survival of patients with Esophageal Squamous Cell Carcinoma (ESCC).Materials and methodsOn the basis of data extracted from the SEER database (training cohort, n = 3375), we identified and integrated significant prognostic factors for nomogram development and internal validation. The model was then subjected to external validation with a separate dataset obtained from Jinling Hospital of Nanjing Medical University (validation cohort, n = 1187). The predictive accuracy and discriminative ability of the nomogram were determined by concordance index (C-index), Akaike information criterion (AIC) and calibration curves. And risk group stratification was performed basing on the nomogram scores.ResultsOn multivariable analysis of the training cohort, seven independent prognostic factors were identified and included into the nomogram. Calibration curves presented good consistency between the nomogram prediction and actual observation for 1-, 3-, and 5-year OS. The AIC value of the nomogram was lower than that of the 8th edition American Joint Committee on Cancer TNM (AJCC) staging system, whereas the C-index of the nomogram was significantly higher than that of the AJCC staging system. The risk groups stratified by CART allowed significant distinction between survival curves within respective clinical TNM categories.ConclusionsThe risk stratification system presented better discriminative ability for survival prediction than current clinical staging system and might help clinicians in decision making.     

Comparison of 4 lymph node staging systems for the prognostic prediction of esophagogastric junction adenocarcinoma with ≤15 retrieved lymph nodes

BackgroundDirectly applying the 8th American Joint Committee on Cancer (AJCC) Tumor Node Metastasis (TNM) staging system to evaluate the prognosis of patients with esophagogastric junction adenocarcinoma (AEG) might lead to under-staging, when insufficient lymph nodes were retrieved during surgery. The prognostic value of 4 lymph nodes staging systems, 8th AJCC TNM N stage, lymph node ratio (LNR), log odds of positive lymph nodes (LODDS), and negative lymph nodes (NLN), in AEG patients having ≤15 retrieved lymph nodes were compared.Methods869 AEG patients diagnosed between 2004 and 2012 with ≤15 retrieved lymph nodes were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression analyses were conducted to assess the association of cancer-specific survival (CSS) and overall survival (OS) with 8th AJCC TNM N stage, LNR, LODDS, and NLN respectively. Predictive survival ability was assessed and compared using linear trend χ2 score, likelihood ratio (LR) test, Akaike information criterion (AIC), Harrell concordance index (C-index), and Receiver Operative Curve (ROC).ResultsThe N stage, LNR, LODDS, and NLN were all independent prognostic predictors for CSS and OS in multivariate Cox models. Comparatively, LODDS demonstrated higher linear trend χ2 score, LR test score, C-index and integrated area under the curve (iAUC) value, and lower AIC in CSS compared to the other three systems. Moreover, for patients without regional lymph node metastasis, NLN showed higher C-index and lower AIC.ConclusionsLODDS showed better predictive performance than N, LNR, and NLN among patients with node-positive patients while NLN performed better in node-negative patients. A combination of LODDS and NLN has the potential to provide more prognostic information than the current AJCC TNM classification.     

Development and validation of prognostic nomogram based on log odds of positive lymph nodes for patients with gastric signet ring cell carcinoma

ObjectiveOur aims were to establish novel nomogram models, which directly targeted patients with signet ring cell carcinoma (SRC), for individualized prediction of overall survival (OS) rate and cancer-specific survival (CSS).MethodsWe selected 1,365 SRC patients diagnosed from 2010 to 2015 from Surveillance, Epidemiology and End Results (SEER) database, and then randomly partitioned them into a training cohort and a validation cohort. Independent predicted indicators, which were identified by using univariate testing and multivariate analyses, were used to construct our prognostic nomogram models. Three methods, Harrell concordance index (C-index), receiver operating characteristics (ROC) curve and calibration curve, were used to assess the ability of discrimination and predictive accuracy. Integrated discrimination improvement (IDI), net reclassification improvement (NRI) and decision curve analysis (DCA) were used to assess clinical utility of our nomogram models.ResultsSix independent predicted indicators, age, race, log odds of positive lymph nodes (LODDS), T stage, M stage and tumor size, were associated with OS rate. Nevertheless, only five independent predicted indicators were associated with CSS except race. The developed nomograms based on those independent predicted factors showed reliable discrimination. C-index of our nomogram for OS and CSS was 0.760 and 0.763, which were higher than American Joint Committee on Cancer (AJCC) 8th edition tumor-node-metastasis (TNM) staging system (0.734 and 0.741, respectively). C-index of validation cohort for OS was 0.757 and for CSS was 0.773. The calibration curves also performed good consistency. IDI, NRI and DCA showed the nomograms for both OS and CSS had a comparable clinical utility than the TNM staging system.ConclusionsThe novel nomogram models based on LODDS provided satisfying predictive ability of SRC both in OS and CSS than AJCC 8th edition TNM staging system alone.     

基于SEER数据库脊索瘤临床预测模型的建立及验证

目的 构建脊索瘤患者的预测模型并进行验证.方法 从SEER数据库(2004～2015年)中鉴定和收集597例脊索瘤患者.Nomogram是基于建模组420例拥有完整数据的患者建立的.C指数(C-index)和校正曲线确定Nomogram的预测精度和判别能力.结果 建立了基于年龄、种族、原发部位及数量、肿瘤分期(TNM)...     

The 2002 AJCC TNM classification is a better predictor of primary small cell esophageal carcinoma outcome than the VALSG staging system

Small cell carcinoma of the esophagus (SCCE) is a rare and aggressive malignant tumor with a poor prognosis. The optimal disease staging system and treatment approaches have not yet been defined. This study aimed to evaluate the prediction of different staging systems for prognosis and treatment options of SCCE. We retrospectively accessed the clinicopathologic characteristics, treatment strategy, and prognosis of 76 patients diagnosed with primary SCCE between 2001 and 2011. The 1-, 2-, 3-, and 5-year overall survival rates were 58%, 31%, 19%, and 13%, respectively. Univariate analysis showed that the 2002 American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) classification (P=0.002), Veterans Administration Lung Study Group (VALSG) stage (P=0.001), predisposing factors (P0.001), T category (P=0.023), and M category (P0.001) were prognostic factors for overall survival. Multivariate analysis showed that the 2002 AJCC TNM stage (P0.001) was the only independent prognostic factor for survival. The value of the area under the receiver operator characteristic (ROC) curve (AUC) of the 2002 AJCC TNM staging system was larger than that of VALSG staging system with regard to predicting overall survival (0.774 vs. 0.620). None of the single treatment regimens showed any benefit for survival by Cox regression analysis. Thus, the 2002 AJCC TMN staging system improved the prediction of SCCE prognosis; however, the optimal treatment regimen for SCCE remains unclear.