基于SEER数据库的转移性结肠癌患者早期死亡预测列线图模型构建

目的构建预测转移性结肠癌(mCC)患者早期死亡的列线图模型。方法从SEER数据库中选择6669例符合条件的mCC患者。根据多因素Logistic回归中的危险因素构建列线图。通过C-index、校准曲线和临床决策曲线分析(DCA)评估列线图的预测性能。结果原发肿瘤位置、肿瘤分化、T分期、M分期、骨转移、脑转移、CEA、肿瘤大小、年龄和婚姻状态是mCC患者早期死亡的独立影响因素。基于这些变量构建列线图,C-index和校准曲线显示模型具有很好的预测能力,DCA曲线显示列线图可以使患者有较好的临床获益。结论该列线图具有良好的预测能力,能够帮助医生识别可能早期死亡的高危mCC患者,有助于制定个性化治疗策略。     

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。     

早发型非转移性结直肠癌患者预后预测模型的构建及外部验证

目的:探讨影响早发型非转移性结直肠癌（early-onset non-metastatic colorectal cancer,EONCRC）患者预后的相关独立危险因素,并构建列线图预测EONCRC患者预后。方法:从美国监测、流行病学和结果数据库SEER数据库中收集了9 097例EONCRC患者的数据,患者按照7∶3比例随机分配到训练集(6 369例)和验证集(2 728例)。通过单变量、多变量COX比例风险回归分析确定独立的预后因素,并构建列线图。 使用C指数、ROC曲线和校准曲线评价列线图的区分度、预测效能和校准度。使用新疆军区总医院收治的EONCRC患者临床资料(n=171)对列线图进行了外部验证并对其预后影响因素进行了分析。结果:多因素分析确定了与总生存期有关的8个独立风险因素,分别是组织学分化程度、组织学类型、神经浸润、分期、T分期、手术、化疗和放疗,并将它们纳入列线图。SEER训练集、SEER验证集、外部验证集的C指数值分别为0.765(95%置信区间,0.749~0.781)、0.785(95%置信区间,0.763~0.807)、0.766(95%置信区间,0.713~0.819),校准曲线表明了列线图预测总生存率与实际总生存率具有良好的一致性。ROC曲线显示,列线图可以准确预测EONCRC患者1年(AUC=0.834 9)、3年(AUC=0.794 7)和5年(AUC=0.771 2)的生存率。根据列线图的风险评分将患者分为高风险、中风险和低风险组,在SEER训练集、SEER验证集、外部验证集中,低风险组的5年生存率均最高,其次是中风险组和高危组。结论:本研究确定了EONCRC患者预后相关的8个独立危险因素,列线图能准确预测中国及美国EONCRC患者1年、3年、5年总生存率,对EONCRC患者进行个体化的分层及预后评估,为临床的诊疗提供科学依据。     

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...     

基于SEER数据库恶性脑膜瘤预后列线图的构建与验证

目的 基于SEER数据库构建并验证恶性脑膜瘤患者的预后列线图模型。方法 通过SEER*Stat 8.4.0软件获取SEER数据库1992年至2019年恶性脑膜瘤患者的病例资料，以8∶2比例将筛选所得病例随机分为训练集及验证集。运用R软件4.1.3版进行Lasso回归及多因素Cox回归分析确定恶性脑膜瘤的独立预后因素，并构建其1年、3年及5年生存率的列线图模型。通过一致性指数(C-index)、受试者工作特征曲线(ROC)、校准曲线及风险分层分析评估列线图的可靠性。结果 共筛选出717例患者，其中训练集576例，验证集141例。经Lasso回归及多因素Cox回归分析后，确定年龄、性别、婚姻状态及手术情况为恶性脑膜瘤的独立预后因素(P<0.05)。训练集与验证集的C-index分别为0.683、0.681。对于1年、3年及5年生存率的曲线下面积(AUC)值，训练集分别为0.738、0.717和0.747,验证集分别为0.704、0.664和0.700。模型的校正曲线显示预测值与实际观测值具有良好一致性，决策曲线分析(DCA)显示列线图具有较好的获益性。根据预后因素将恶性脑膜瘤患者进行...     

基于SEER数据库的原发性纵隔及肺部软组织肉瘤相关生存及预后分析

目的 利用SEER数据库分析影响原发性纵隔及肺部软组织肉瘤预后的相关因素。方法 收集SEER数据库376例患者数据,随机分为训练集（263例）与验证集（113例）。使用Kaplan-Meier法和Cox比例风险回归分析各变量与患者生存及预后的关系,建立列线图预测患者总生存期。采用校准曲线、一致性指数及ROC曲线评价列线图性能。结果 组织学类型、手术、化疗、肿瘤大小、肿瘤分期是影响原发性纵隔及肺部软组织肉瘤预后的因素。建立的列线图模型可以预测其6个月、1年及2年总生存率,校准曲线显示与实测值基本一致,训练集与验证集C指数分别为0.754与0.745,ROC的曲线下面积分别为0.849与0.924,说明具有良好的预测准确度。结论 本研究建立的列线图可以预测原发性纵隔及肺部软组织肉瘤患者6个月、1年及2年总生存率。     

食管癌列线图预后预测模型的构建与验证

目的:构建列线图分析食管癌患者的预后因素并且预测其总生存期,协助临床诊疗。方法:从监测、流行病学及预后(Surveillance, Epidemiology, and End Result, SEER)数据库中按照纳入排除标准选择了2000年至2020年间食管癌患者41 783例,以7∶3随机划分为训练队列(29 249例)和内部验证队列(12 534例),从新疆医科大学附属肿瘤医院依据同样标准收集了2010年1月至2022年12月间食管癌患者5 472例,作为外部验证队列。采用单因素、多因素Cox回归分析筛选变量绘制预测食管癌患者1年、3年及5年总生存期的列线图。利用一致性指数(C-index)、时间依赖性ROC曲线下面积(time-dependent AUC)、校准曲线（calibration curve）、决策曲线（decision curve analysis,DCA）、Kaplan-Meier生存分析来评估列线图的判别和校准能力及临床效益。结果：最终纳入8个变量构建列线图。列线图的训练队列、内部验证队列和外部验证队列的一致性指数分别为0.700、0.679和0.644;1年、...     

基于SEER数据库老年晚期肺腺癌术后患者预后预测模型的构建

目的基于监测、流行病学和最终结果(SEER)数据库大样本数据, 构建并分析可视化预测老年晚期肺腺癌术后患者预后的列线图模型。方法使用SEER*Stat8.4.0.1软件筛选2000年至2019年SEER数据库中来自17个注册点的数据, 纳入4 453例经美国癌症联合会(AJCC)第7版分期标准诊断为Ⅲ期和Ⅳ期接受手术治疗、年龄≥65岁的肺腺癌患者, 按7∶3比例随机分为训练集(3 117例)和验证集(1 336例), 比较两组的流行病学资料和临床病理特征。采用LASSO回归进行数据降维, 从患者预后因素中选择最佳预测因子。采用Cox比例风险模型对筛选出来的变量进行单因素和多因素分析, 采用R软件rms包根据预后独立危险因素构建列线图, 预测患者1、3、5年肿瘤特异性生存(CSS)率。采用Bootstrap法对验证集进行1 000次等量有放回重复采样验证, 采用C指数、受试者工作特征(ROC)曲线及校正曲线验证列线图模型的准确性。结果训练集、验证集患者年龄、性别、种族、肿瘤位置、Grade分级、手术方式、淋巴结清扫数目、放疗方式、肿瘤长径、肿瘤转移、婚姻、居住环境、TNM分期、放化疗等比...     

基于SEER数据库低级别胶质瘤预后列线图的构建

目的:通过监测、流行病学及预后（surveillance，epidemiology，and end result,SEER）数据库开发列线图来分析低级别胶质瘤（low-grade glioma,LGG）患者的预后因素并且预测其生存率。方法:通过SEER数据库收集LGG患者5 439例，并统计其人口统计学信息及临床特征。随机抽取其中1 001例作为模型的内部验证集,并收集2010-2017年间就诊于山西省人民医院的LGG患者67例作为外部验证集。采用单因素、多因素Cox回归及Lasso回归分析LGG患者的独立危险因素，并考虑其临床效用性。将这些独立预测因素整合在一起，绘制预测LGG患者1年和3年生存率的列线图。通过内部验证集数据及外部验证集数据绘制ROC曲线和校准曲线图来评估列线图的性能。结果:纳入训练集患者4 438例，内部验证集患者1 001例，外部验证集患者67例。一般情况人群分布无显著统计学差异。通过单因素、多因素Cox回归及Lasso回归分析联合生存分析结果选择独立危险因素，纳入年龄、病理学分型、手术方式、肿瘤大小、婚姻状况、放化疗及发病部位为独立预测因素(P<0.001)。由上述7种因素构建预后预测模型，结果以列线图形式呈现。内部验证集验证列线图的ROC曲线下面积为0.841和0.804；外部验证集验证列线图的ROC曲线下面积为0.703和0.742，表明该模型的区分度与准确度较高。校准曲线显示其具有较好的一致性。结论:本列线图可用于预测LGG患者1年和3年生存率，并且拥有较高的临床价值，可以为LGG的个体化治疗提供参考。     

基于SEER数据库肺腺癌脑转移预测模型的构建及分析

目的 构建可视化预测肺腺癌（LUAD）脑转移风险概率的列线图模型,提高患者生存率。方法 研究纳入监测、流行病学和最终结果（SEER）数据库中58 928例LUAD患者,并按7∶3比例随机分为训练集和验证集。在训练集中采用Lasso回归与多因素Logistic回归分析筛选最有意义的预测变量,构建预测LUAD脑转移的列线图模型。采用受试者工作特征（ROC）曲线的曲线下面积（AUC）,Boostrap绘制校正曲线,Brier评分验证模型区分度及校准度,决策曲线分析（DCA）评价预测模型的临床效能。结果 最终筛选出7个独立影响因素构建列线图预测模型。训练集和验证集列线图预测LUAD患者发生脑转移概率的AUC分别为0.853(95%CI:0.849～0.858)和0.851(95%CI:0.844～0.857),校准曲线显示模型预测概率与实际观察概率具有较高的一致性,Brier评分均为0.092,DCA显示净收益率较高,模型临床效能较好。结论 本研究成功建立了预测LUAD脑转移的列线图模型,该模型能够准确区分脑转移高风险患者,可以有效指导临床医师制订个体化治疗方案。     

基于阴性淋巴结数目的胃印戒细胞癌预后评估模型的建立与验证

目的 探讨阴性淋巴结数目（NLNC）对胃印戒细胞癌（GSRC）患者预后的影响及构建G S R C 患者的预后预测模型。方法 基于SEER数据库收集GSRC患者2101例，随机分为建模组和验证组，检验临床病理特征与GSRC预后的关系。多因素Cox比例风险回归模型分析影响总生存的独立危险因素并建立预后预测模型。一致性指数（C?index）、校准曲线、净分类指数（NRI）、综合判别指数（IDI）和临床决策曲线（DCA）对列线图进行准确性和临床适用性评估。结果 所有患者按照7:3比例划分，建模组1473例，验证组628例。NLNC>10是GSRC患者预后的保护因素（HR=0.578, 95%CI: 0.504~0.662），根据多因素Cox比例风险回归模型筛选的变量建立Nomogram图，建模组和验证组的C-index分别为0.737（95%CI: 0.720~0.753）和0.724（95%CI: 0.699~0.749），区分度良好，校准曲线显示模型的一致性较高。NRI=17.77%，连续NRI=36.34%，IDI=4.2%，表明该模型较传统模型是正向收益，DCA决策曲线远离基准线表明模型临床适用性好。结论 NLNC增加是GSRC患者预后的有利因素。本研究建立的列线图相对准确，可预测GSRC患者的预后。     

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.     

宫颈癌术后患者列线图预测模型及危险分层系统构建

目的 构建宫颈癌术后患者列线图预测模型,基于列线图个体得分建立危险分层系统。方法 通过搜索美国SEER （Surveillance,Epidemiology,and End Results）数据库中1973—2015年的6 835例宫颈癌术后患者数据构建预测模型,同时选取120例于苏州大学附属第二医院接受宫颈癌手术的患者作为外部验证队列。通过单因素和多因素的Cox回归筛选预后因子并构建列线图,基于列线图模型建立危险分层系统。结果 Cox回归分析显示诊断年龄、人种、组织学分级、T分期、N分期、淋巴结清扫状况、肿瘤大小、肿瘤浸润深度是宫颈癌术后患者的独立预后指标。由此构建的列线图模型的一致性指数在建模队列、内部验证队列和外部验证队列分别为0.824、0.814、0.730,校准曲线显示模型预测效果与实际生存情况基本相符,危险分层系统能区分不同FIGO分期患者的生存情况（均P<0.05）。结论 本研究所建立的列线图模型能有效预测宫颈癌术后患者预后,基于该列线图预测模型的危险分层系统对区分高危患者具有一定临床价值。     

Prognostic nomogram for predicting cancer-specific survival in patients with resected hilar cholangiocarcinoma: a large cohort study

BackgroundThe aim of the study was to establish and validate a novel prognostic nomogram of cancer-specific survival (CSS) in resected hilar cholangiocarcinoma (HCCA) patients.MethodsA training cohort of 536 patients and an internal validation cohort of 270 patients were included in this study. The demographic and clinicopathological variables were extracted from the Surveillance, Epidemiology and End Results (SEER) database. Univariate and multivariate Cox regression analysis were performed in the training cohort, followed by the construction of nomogram for CSS. The performance of the nomogram was assessed by concordance index (C-index) and calibration plots and compared with the American Joint Committee on Cancer (AJCC) staging systems. Decision curve analysis (DCA) was applied to measure the predictive power and clinical value of the nomogram.ResultsThe nomogram incorporating age, tumor size, tumor grade, lymph node ratio (LNR) and T stage parameters was with a C-index of 0.655 in the training cohort, 0.626 in the validation cohort, compared with corresponding 0.631, 0.626 for the AJCC 8th staging system. The calibration curves exhibited excellent agreement between CSS probabilities predicted by nomogram and actual observation in the training cohort and validation cohort. DCA indicated that this nomogram generated substantial clinical value.ConclusionsThe proposed nomogram provided a more accurate prognostic prediction of CSS for individual patients with resected HCCA than the AJCC 8th staging system, which might be served as an effective tool to stratify resected HCCA patients with high risk and facilitate optimizing therapeutic benefit.     

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...     

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.     

A novel prognostic nomogram for hepatocellular carcinoma after thermal ablation

It remains impossible to accurately assess the prognosis after thermal ablation in patients with hepatocellular carcinoma (HCC). Our aim was to build a nomogram to predict the survival rate of HCC patients after thermal ablation. We developed and validated a nomogram using data of 959 HCC patients after thermal ablation from two centers. Harrell’s concordance index (C-index), calibration plot and Decision curve analysis (DCA) were used to measure the performance of the nomogram, and we compared it with the Barcelona Clinic Liver Cancer (BCLC) staging system and a previous nomogram. Six variables including age, serum albumin, operation method, risk area, tumor number and early recurrence were selected to construct the nomogram. In the training cohort, internal validation cohort, and external validation cohort, the nomogram all had a higher C-index to predict survival rate than both the BCLC staging system and the previous nomogram (0.736, 0.558 and 0.698, respectively; 0.763, 0.621 and 0.740, respectively; and 0.825, 0.551 and 0.737, respectively). Calibration plots showed a high degree of consistency between prediction and actual observation. Decision curve analysis (DCA) presented that compared with BCLC system and the previous nomogram, our nomogram had the highest net benefit. In all three cohorts, the nomogram could accurately divide patients into three subgroups according to predicted survival risk. A nomogram was developed and validated to predict survival of HCC patients who underwent thermal ablation, which is helpful for prognostic prediction and individual surveillance in clinical practice.     

A novel preoperative MRI-based radiomics nomogram outperforms traditional models for prognostic prediction in pancreatic ductal adenocarcinoma

To develop an efficient prognostic model based on preoperative magnetic resonance imaging (MRI) radiomics for patients with pancreatic ductal adenocarcinoma (PDAC), the preoperative MRI data of PDAC patients in two independent centers (defined as development cohort and validation cohort, respectively) were collected retrospectively, and the radiomics features of tumors were then extracted. Based on the optimal radiomics features which were significantly related to overall survival (OS) and progression-free survival (PFS), the score of radiomics signature (Rad-score) was calculated, and its predictive efficiency was evaluated according to the area under receiver operator characteristic curve (AUC). Subsequently, the clinical-radiomics nomogram which incorporated the Rad-score and clinical parameters was developed, and its discrimination, consistency and application value were tested by calibration curve, concordance index (C-index) and decision curve analysis (DCA). Moreover, the predictive value of the clinical-radiomics nomogram was compared with traditional prognostic models. A total of 196 eligible PDAC patients were enrolled in this study. The AUC value of Rad-score for OS and PFS in development cohort was 0.724 and 0.781, respectively, and the value of Rad-score was negatively correlated with PDAC’s prognosis. Moreover, the developed clinical-radiomics nomogram showed great consistency with the C-index for OS and PFS in development cohort was 0.814 and 0.767, respectively. In addition, the DCA demonstrated that the developed nomogram displayed better clinical predictive usefulness than traditional prognostic models. We concluded that the preoperative MRI-based radiomics signature was significantly related to the poor prognosis of PDAC patients, and the developed clinical-radiomics nomogram showed better predictive ability, it might be used for individualized prognostic assessment of preoperative patients with PDAC.     

Individualized model for predicting COVID-19 deterioration in patients with cancer: A multicenter retrospective study

The 2019 novel coronavirus has spread rapidly around the world. Cancer patients seem to be more susceptible to infection and disease deterioration, but the factors affecting the deterioration remain unclear. We aimed to develop an individualized model for prediction of coronavirus disease (COVID-19) deterioration in cancer patients. The clinical data of 276 cancer patients diagnosed with COVID-19 in 33 designated hospitals of Hubei, China from December 21, 2019 to March 18, 2020, were collected and randomly divided into a training and a validation cohort by a ratio of 2:1. Cox stepwise regression analysis was carried out to select prognostic factors. The prediction model was developed in the training cohort. The predictive accuracy of the model was quantified by C-index and time-dependent area under the receiver operating characteristic curve (t-AUC). Internal validation was assessed by the validation cohort. Risk stratification based on the model was carried out. Decision curve analysis (DCA) were used to evaluate the clinical usefulness of the model. We found age, cancer type, computed tomography baseline image features (ground glass opacity and consolidation), laboratory findings (lymphocyte count, serum levels of C-reactive protein, aspartate aminotransferase, direct bilirubin, urea, and d -dimer) were significantly associated with symptomatic deterioration. The C-index of the model was 0.755 in the training cohort and 0.779 in the validation cohort. The t-AUC values were above 0.7 within 8 weeks both in the training and validation cohorts. Patients were divided into two risk groups based on the nomogram: low-risk (total points ≤ 9.98) and high-risk (total points > 9.98) group. The Kaplan-Meier deterioration-free survival of COVID-19 curves presented significant discrimination between the two risk groups in both training and validation cohorts. The model indicated good clinical applicability by DCA curves. This study presents an individualized nomogram model to individually predict the possibility of symptomatic deterioration of COVID-19 in patients with cancer.     

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。