微乳头状膀胱癌患者预后模型的建立与内部验证

目的:探讨微乳头状膀胱癌(MPBC)患者预后的独立预测因素,并为其建立可以个体化预测预后的列线图模型。方法:回顾性分析SEER数据库中168例MPBC患者的临床资料,采用Kaplan-Meier法计算总体生存率(OS)和癌症特异性生存率(CSS),采用log-rank检验评价生存差异的显著性;采用Cox多因素回归分析确定CSS的独立预测因素,使用R软件整合所有具有独立预测意义的变量生成列线图,并采用Bootstrap法计算C-index、绘制校准曲线对模型进行内部验证。结果:年龄75岁、黑人患者、婚姻状况异常、T_3和T_4期、≥N_1期和M_1期是CSS的独立危险因素;术后盆腔淋巴结清扫是独立保护因素。预测模型可准确预测患者预后,且其区分度优于TNM分期系统(0.745 vs.0.652)。结论:本研究基于SEER数据库建立了国内外首个可以个体化预测MPBC患者预后的列线图模型;且经内部验证,其预测性能良好。列线图模型的建立将有助于设计临床试验并促进医患沟通。     

基于SEER数据库的胃癌肝转移预后因素分析与预后模型构建

背景与目的：胃癌其因具有恶性程度高、易早期转移等特点而导致患者往往具有较差的临床预后,其中胃癌肝转移（GCLM）更是导致患者死亡的主要因素,然而,目前对于GCLM的预后评价手段仍然存在着一定的不足。因此,本研究利用SEER数据库分析GCLM患者的临床病理特征和预后风险因素,从而建立具有良好预测能力的评估模型,以提升对患者个体化预后的评估能力。 方法：从SEER数据库中提取2010—2015年确诊的GCLM患者的临床资料。根据纳入和排除标准,严格筛选后纳入研究病例共2 554例,按7:3比例随机分配为建模集（1 790例）和验证集（764例）,比较建模集与验证集中患者的临床基线特征差异,用Cox等比例回归模型与Fine-Gray竞争风险模型分别筛选出GCLM患者总体生存期（OS）与癌症特异性生存期（CSS）的独立危险因素。基于建模集Cox或Fine-Gray风险模型的多元回归分析及AIC因素优化的结果,构建预测GCLM患者OS或CSS的列线图模型。最后,采用一致性指数、ROC曲线和校正曲线评估模型预测的可靠性。 结果： 建模集与验证集患者的基线特征无明显差异。分析结果显示,患者年龄、化疗、肿瘤分级、原发灶切除和原发灶数目是影响GCLM患者OS预后的独立危险因素,而化疗、肿瘤分级、原发灶切除和原发灶数目是影响GCLM患者CSS预后的独立危险因素（均P<0.05）。基于上述指标分别构建列线图模型并进行评价,预测OS与CSS列线图模型的一致性指数均明显高于AJCC-TNM分期系统（建模集：0.706 vs. 0.560、0.670 vs. 0.554;验证集：0.769 vs. 0.534、0.744 vs. 0.518）,并且ROC曲线分析亦展示出预测模型具有较高的准确度。最后,校正曲线分析显示,构建的列线图模型预测患者OS或CSS的生存率与实际观察值均具有良好的一致性。 结论： 基于SEER数据库分析构建的列线图模型在预测GCLM患者OS和CSS方面有较高的准确性,将有助于临床医师对GCLM患者制定个体化的治疗策略。     

基于SEER数据库的弥漫型胃癌患者预后列线图模型构建及验证

目的：筛选弥漫型胃癌患者预后因素并构建预后列线图，并验证其预测准确性。方法 ：从SEER数据库收集2006—2018年病理诊断为弥漫型胃癌的2877例患者的临床病理特征，随机将患者分为训练队列（1439例）和验证队列（1438例）。利用单因素Log-rank及多因素COX分析筛选出独立预后因素并构建预后列线图，预测1、3、5年的总生存期（OS），使用一致性指数和校准曲线确定列线图预测的准确性和判别能力。结果：年龄、T、N、M、TNM、手术状态、化疗状态7个指标均是OS的独立预后因素（P<0.05），基于独立预后因素构建了1、3、5年OS的列线图。训练队列中列线图的c-指数为0.750(95%CI:0.734～0.766)，高于TNM分期系统0.658(95%CI:0.639～0.677)；验证队列列线图的c-指数为0.753(95%CI:0.737～0.769)，高于TNM分期系统0.679(95%CI:0.503～0.697)。校准曲线表明了列线图预测生存率与实际生存率具有良好的一致性。结论：预后列线图能够准确预测弥漫性胃癌患者预后，有助于临床医师对弥漫型胃癌患者进行个体化的预...     

骨肉瘤预后个体化预测模型列线图的建立

 目的 通过对骨肉瘤患者的临床、病理学、影像学及随访资料进行预后因素分析,建立骨肉瘤预后预测模型列线图,并验证其准确度。方法 收集1998至2008年确诊且符合入组标准的235例骨肉瘤患者组成建模组,2009年的55例骨肉瘤患者组成验证组。单因素生存分析采用Kaplan-Meier法绘制生存曲线,Log-rank法进行统计学分析;应用Cox比例风险模型进行多因素分析,确定独立预后因子;然后应用R软件建立预测模型列线图,内部验证运用Bootstrap法,外部验证运用验证组,一致性指数（C-index）用来评价模型准确度,并绘制出列线图预测和实际观察的五年生存率校准曲线。结果 建模组和验证组五年总体生存率分别为46.1%±6.7%和61.8% ±12.9%。多因素分析结果显示,病理性骨折、入院时碱性磷酸酶水平、肿瘤大小、肿瘤分期和术后化疗次数是独立预后因素。校准曲线显示列线图预测与实际观察的五年生存率有很好的一致性。列线图预测五年生存率的C-index为0.74（95%CI,0.70~0.78）,明显高于Enneking分期系统。应用验证组进行外部验证,列线图、Enneking分期和美国癌症联合委员会（AJCC）分期系统的C-index分别为0.71、0.54和0.56,提示列线图比Enneking分期及AJCC分期的预后预测准确性更高。结论 成功建立的预测骨肉瘤患者总体生存列线图能实现个体化预测,且与其他预后预测系统相比更直观、准确。     

基于SEER数据库验证淋巴结清扫术对N0期胆囊癌患者预后的影响

目的 基于监测、流行病学和最终结局（SEER）数据库评估淋巴结清扫术对N0期胆囊癌患者预后的影响。方法 下载、整理并分析于2004—2015年明确诊断为胆囊癌患者的临床资料，使用R语言将患者分为淋巴结清扫组（LNR）和淋巴结未清扫组（non-LNR），结局指标为肿瘤特异性生存率（CSS）。通过倾向性评分（PSM）保证两组基线资料可比性，采用单因素和多因素Cox回归筛选危险因素并构建相关列线图，Kaplan-Meier法描述生存曲线，Log-rank检验比较生存差异。采用一致性指数（C-index）、ROC曲线和校准曲线对列线图进行评估，同时应用综合判别改善指数（IDI）比较列线图与AJCC分期的临床适用性。结果 经PSM，本研究纳入2 272例患者。Cox回归筛选出年龄、分化程度、AJCC分期、T分期与LNR为独立危险因素。列线图预测准确性为0.738，标准误为0.007。与AJCC分期相比，模型预测1、3和5年的CSS的IDI分别为0.084、0.111和0.115，具有正改善作用。根据列线图计算得分，Kaplan-Meier法显示高分险与低分险患者的预后差异有统计学意义，有较好的临...     

初诊乳腺癌肝转移患者列线图预后模型的构建

背景与目的 乳腺癌肝转移（BCLM）患者预后较差,其预后因不同因素而有较大差别。关于BCLM预后的研究很少,且目前缺乏准确预测BCLM的预后的手段。因此,本研究构建列线图来预测初诊BCLM患者的3、5年总生存率（OS）和特异性生存率（CSS）,以期为临床提供参考。方法 在SEER数据库中提取2010—2016年的初诊为BCLM患者资料,根据纳入和排除标准,严格筛选后纳入研究病例共1 994例,按7∶3比例随机分配为建模组（1 398例）和验证组（596例）,将单因素分析差异有统计学意义的变量纳入多因素Cox回归模型进行分析,得到影响BCLM患者生存情况的独立危险因素。基于影响BCLM患者预后的独立危险因素构建预测OS和CSS的列线图模型,通过一致性指数和校正曲线评估列线图的可靠性。结果 年龄、种族、婚姻、组织学分级、激素受体状态、手术、化疗、骨转移、脑转移、肺转移是BCLM患者预后的独立影响因素（均P<0.05）,这些因素均用于构建列线图预后模型,建模组和验证组OS的一致性指数为0.709、0.731,建模组和验证组CSS的一致性指数为0.709、0.732。模型的校正曲线显示该列线图的生存率预测值与实际观测值之间具有良好的一致性。结论 所构建的列线图预后模型能够准确预测初诊BCLM患者预后状态,为临床医生制定个体化的治疗方案提供参考。     

泌尿生殖系统横纹肌肉瘤列线图预后模型的构建及验证

目的:本研究目的是明确泌尿生殖系统横纹肌肉瘤(rhabdomyosarcoma, RMS)患者的临床病理特征,同时制作预测泌尿生殖系统RMS患者的1、3、5年生存率的列线图。方法:对1975—2016年SEER数据库确诊的泌尿及生殖系统RMS患者进行筛选,最终有990例患者纳入本研究。采用单因素及多因素Cox回归分析筛选泌尿及生殖系统RMS的独立危险因素,并以此来构建预测泌尿及生殖系统RMS生存率的列线图。选取2012—2018年郑州大学第一附属医院确诊的26例泌尿生殖系统RMS患者作为外部验证队列,然后采用C指数和校准曲线对模型进行内部及外部验证。结果:在泌尿及生殖系统RMS患者中,多因素Cox回归分析结果显示,患者的年龄、肿瘤部位、病理类型、肿瘤大小、总分期、N分类、M分类、手术、化疗均具有独立预测价值(P<0.05)。根据上述变量构建预测模型,此列线图内部及外部验证的C指数分别为0.841、0.838,具有良好的区分度,同时内部及外部数据的校准曲线均显示出此预测模型具有较好的一致性。结论:本研究所构建的列线图可为泌尿及生殖系统RMS患者提供更为简洁的预后评估,为临床的个体化...     

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。     

甲状腺髓样癌术后生存影响因素分析及列线图的构建

背景与目的 目前用于评估甲状腺髓样癌（MTC）预后的主要方式采用TNM分期系统，但该系统不能个体化预测患者的预后。因此，需要建立专门针对MTC的精准预后指标体系。本研究分析影响MTC患者术后生存的因素，并构建MTC术后生存列线图。方法 选取2004—2015年SEER数据库MTC数据，共筛选出符合条件的1 884例患者纳入研究。将患者按3∶1随机分为训练集（1 413例）和验证集（471例），比较两组临床数据基线特征差异。采用单因素和多因素Cox回归模型筛选影响MTC生存的独立因素，Kaplan-Meier生存曲线分析其对预后的影响。基于Cox回归分析筛选出的结果建立MTC术后患者生存列线图。通过一致性指数（C-index）、ROC曲线、曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）对列线图进行验证和评估。结果 单因素分析结果显示，性别、年龄、原发肿瘤分期、淋巴结转移、远处转移、是否甲状腺全切除、肿瘤是否侵犯甲状腺被膜、是否行放射治疗均影响患者预后（均P<0.05）；Cox回归分析结果显示，性别、年龄、远处转移、侵犯甲状腺被膜、是否行甲状腺全切除术、是否放疗为MTC患者的独立预后因素（均P<0.05）。Kaplan-Meier生存曲线显示，男性患者、年龄≥49岁、伴远处转移、肿瘤侵犯甲状腺被膜、未行甲状腺全切除术、接收放疗患者预后更差。用患者性别、年龄、远处转移、甲状腺被膜受侵、手术方式构建了MTC患者2、5、10年的生存列线图。该列线图训练集的C-index为0.755（95% CI=0.741~0.769），验证集为0.725（95% CI=0.699~0.769）。ROC曲线用于评估列线图的区分度，在训练集2、5、10年的AUC值分别为0.79、0.779、0.766；在验证集分别为0.78、0.725、0.733。校准曲线结果显示该列线图预测的生存率和实际生存率具有一致性。DCA将列线图与AJCC第6版TNM分期的临床相比，该列线图的在5年和10年生存评估中均显示出更大的净收益。结论 性别、年龄、远处转移、甲状腺被膜侵犯、手术方式是影响MTC患者生存的独立因素；MTC术后生存列线图模型在一定程度上能够更准确地进行患者个体生存预测，帮助临床医师做出适当的个体化临床决策。     

基于术前系统免疫炎症指数及控制营养状况评分对胰腺癌根治术后列线图预后预测模型的构建及评价

目的基于术前系统免疫炎症指数(SII)及控制营养状况(CONUT)评分探讨影响胰腺导管腺癌(PDAC)根治术后预后的危险因素并建立预后预测模型。方法回顾性收集兰州大学第二医院2014年1月至2019年12月期间确诊为PDAC患者的临床病理资料。采用X-tile软件确定SII最佳截断值,使用Kaplan-Meier法进行生存分析,采用Cox比例风险回归模型对PDAC根治术后预后影响因素进行多因素分析,使用R4.0.5软件绘制1、2、3年生存率的列线图预测模型,然后评价该预测模型的效能并建立网页计算器。结果共131例患者纳入本研究,中位生存时间18.6个月,术后1、2、3年累积总生存率分别为73.86%、36.44%、11.95%。术前SII最佳截断值为313.1,Kaplan-Meier生存曲线分析显示SII>313.1者较SII≤313.1者的预后更差(χ2=8.917,P=0.003)。Cox比例风险回归模型多因素分析结果显示,年龄>65岁、临床分期晚(Ⅲ、Ⅳ期)、术前SII>313.1、CONUT评分>4分是PDAC根治术后影响预后(总生存时间)的独立危险因素(P<0.05)。包括年龄、临床分期、术前SII、CONUT评分及术后化疗构建的列线图预测模型内部验证一致性指数(C指数)为0.669,绘制的列线图校正曲线预测的生存情况与实际观察到的生存情况拟合良好,决策曲线分析显示列线图具有更广的临床净获益(阈值概率为0.05~0.95),网页计算器运行良好。结论年龄、临床分期、术前SII、CONUT评分是PDAC根治术后预后的独立影响因素。通过将年龄、临床分期、术前SII、CONUT评分及术后化疗纳入构建的列线图对PDAC根治术后预后预测更精确,建立的网页计算器更方便医生和患者使用。     

Prognostic indicators for survival in renal cell carcinoma with venous thrombus and development of predictive nomograms

BackgroundPrevious predictive models of prognosis of patients with renal cell carcinoma (RCC) and venous tumour thrombus (VTT) didn’t included patients have not undergoing radical nephrectomy (RN). We analysed both patients receive RN or not to investigate the prognostic factors of survival for patients with RCC and VTT comprehensively.MethodsThe clinical data of patients with RCC and VTT diagnosed from 2000–2018 in the Surveillance Epidemiology and End Results (SEER) database were downloaded and compared with the clinical data of patients with VTT admitted to the Department of Urology of the Tongji Hospital (TJH) from 2004–2020. The matched cases were divided into a training set and a validation set. The training set was used to establish nomograms based on key prognostic factors. The reliability of the nomograms for predicting the survival of patients in the training set, those in the validation set and TJH patients and was evaluated by C-indexes, ROC curves and calibration curves.ResultsMultivariate Cox regression analysis identified nine prognostic factors for overall survival (OS): age, tumour size, histologic classification, nuclear grade, location of VTT, N stage, M stage, surgery, and systemic treatments (P<0.001). Nomograms for OS and cancer specific survival (CSS) were established based on key prognostic factors obtained from the multivariate analysis. The C-indexes of the nomogram for predicting OS in the training set, validation set, TJH cohort were 0.762 (95% CI: 0.746–0.778), 0.718 (95% CI: 0.687–0.749), and 0.819 (95% CI: 0.745–0.893), respectively. The calibration curves are all close to a straight line with a slope of 1. Based on the ROC curves, the nomograms had greater areas under the curve (AUCs) than the tumor, node and metastasis (TNM) staging system in predicting the 3-year OS and CSS. All three validations showed that the nomograms established based on key prognostic factors have reliable accuracy in predicting the survival of both TJH and SEER patients who developed RCCs with VTT.ConclusionsBeside the location of VTT, the tumour size can also predict the survival of patients with RCC and VTT. Nomograms based on key prognostic factors can predict the survival of patients from both America and central China with reliable accuracy.     

Construction of a predictive model for clinical survival in male patients with non-metastatic rectal adenocarcinoma

BackgroundNo clinical prediction model is available for non-metastatic rectal adenocarcinoma in males. Based on demographic and clinicopathological characteristics, we constructed a survival prediction model for the study population.MethodsAt a ratio of 7:3, 3450 eligible patients were divided into training and validation sets. Optimal cutoff values were calculated using X-tile software. Cox proportional hazards regression was used to find prognostic factors for cancer-specific survival (CSS) and overall survival (OS). Corresponding nomogram prognostic models were also constructed based on predictors.The validity, discriminative ability, predictability, and clinical usefulness of the model were analyzed and assessed.ResultsWe identified predictors of survival in the target population and successfully constructed nomograms. In the nomogram prediction model for OS and CSS, the C-index was 0.724 and 0.735, respectively, for the training group and 0.754 and 0.760, respectively, for the validation group. In the validation group, the area under the curve (AUC) of the receiver operating characteristic curve for OS and CSS nomograms was 0.768 and 0.769, respectively, for the 3-year survival rate and 0.755 and 0.747, respectively, for the 5-year survival rate. Kaplan–Meier Survival Curves showed excellent risk discrimination performance of the nomogram (P < 0.05) Calibration curves, time-dependent AUC and decision curve analysis showed that the prediction model constructed in this study had excellent clinical prediction and decision ability and performed better than the TNM staging system.ConclusionOur nomogram is helpful to evaluate the prognosis of non-metastatic male patients with rectal adenocarcinoma and has guiding significance for clinical treatment.     

Nomograms Predict Cancer‐Specific and Overall Survival of Patients With Primary Limb Leiomyosarcoma

To date, there have been no data to predict the survival of patients with leiomyosarcoma from soft limb tissue because of the rarity of this disease. Nomograms have been widely applied in clinical oncology to precisely predict the survival of individual patients. This was a retrospective study to construct and validate nomograms to predict the cancer‐specific survival (CSS) and overall survival (OS) of patients with primary limb leiomyosarcoma (PL‐LMS). A total of 1,208 patients with LMS from limb soft tissue were collected from the Surveillance, Epidemiology, and End Results database from 1975 to 2015. We identified independent prognostic factors using univariate and multivariate Cox analyses. These prognostic factors were then included in the nomograms to predict 3‐ and 5‐year CSS and OS rates. Finally, we validated the nomograms internally and externally. A total of 1208 patients were collected and divided into validation (N = 604) and training (N = 604) groups. Age, race, grade, tumor size, stage, and surgical types were demonstrated as independent prognostic factors for CSS and OS (all p < 0.05) and further used to construct the nomograms. The concordance index (C‐index) for CSS was 0.857 for internal validation and 0.727 for external validation. The C‐index for OS and CSS both demonstrated that the nomogram prediction agreed perfectly with actual survival. We developed nomograms to predict CSS and OS in PL‐LMS patients and can benefit from using them to identify patients’ mortality risk and make more precise assessments regarding survival. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:1649–1657, 2019.     

Population-based outcome of muscle-invasive bladder cancer following radical cystectomy: who can benefit from adjuvant chemotherapy?

BackgroundThe benefit of adjuvant chemotherapy remains controversial in muscle-invasive bladder cancer (MIBC) after radical cystectomy. The present study’s primary objective was to construct a predictive tool for the reasonable application of adjuvant chemotherapy.MethodsAll of the patients analyzed in the present study were recruited from the Surveillance Epidemiology and End Results program between 2004 and 2015. Propensity score matching (PSM) was used to reduce inherent selection bias. Cox proportional hazards models were applied to identify the independent prognostic factors of overall survival (OS) and cancer-specific survival (CSS), which were further used to construct prognostic nomogram and risk stratification systems to predict survival outcomes. The prognostic nomogram’s performance was assessed by concordance index (C-index), receiver-operating characteristic (ROC) and calibration curves. Decision curve analysis (DCA) was performed to evaluate the clinical net benefit of the prognostic nomogram.ResultsA total of 6,384 patients with or without adjuvant chemotherapy were included after PSM. Several independent predictors for OS and CSS were identified and further applied to establish a nomogram for 3-, 5- and 10-year, respectively. The nomogram showed favorable discriminative ability for the prediction of OS and CSS, with a C-index of 0.709 [95% confidence interval (CI): 0.699–0.719] for OS and 0.728 (95% CI: 0.718–0.738) for CSS. ROC and calibration curves showed satisfactory consistency. The DCA revealed high clinical positive net benefits of the prognostic nomogram. The different risk stratification systems showed that adjuvant chemotherapy resulted in better OS (P<0.001) and CSS (P<0.001) than without adjuvant chemotherapy for high-risk patients; while the OS (P=0.350) and CSS (P=0.260) for low-risk patients were comparable.ConclusionsWe have constructed a predictive model and different risk stratifications for selecting a population that could benefit from postoperative adjuvant chemotherapy. Adjuvant chemotherapy was found to be beneficial for high-risk patients, while low-risk patients should be carefully monitored.     

Nomograms for predicting long-term overall survival and cancer-specific survival in patients with primary urethral carcinoma: a population-based study

Background

Our aim was to identify the independent prognostic factors in patients with primary urethral carcinoma (PUC) and to predict their overall survival (OS) and cancer-specific survival (CSS) at 3, 5, and 8 years.

Methods

Patients with PUC identified in the Surveillance, Epidemiology, and End Results (SEER) database were divided into training and validation cohorts. Nomograms were constructed based on the results of Cox regression analysis. The predictive performance of each nomogram was evaluated using the consistency index (C-index), the area under the receiver operating characteristics curve (AUC), and calibration plots. Decision-curve analysis (DCA) was used to test the clinical value of the predictive models.

Results

Our study screened 822 patients with PUC. Multivariate analysis showed that the age at diagnosis, race, histology, American Joint Committee on Cancer (AJCC) stage, and surgery status were independent prognostic factors for CSS and age at diagnosis, race, histology, AJCC stage, surgery status, and chemotherapy for OS (all P?<?0.05). We used these prognostic factors to construct nomograms. The C-indexes for OS and CSS were 0.713 and 0.741 in training cohorts and 0.714 and 0.738 in validation cohorts, respectively. The AUC and calibration plots demonstrated the good performance of both nomograms. The DCA indicated the presence of clinical net benefits in both the training and validation cohorts.

Conclusion

We developed and validated nomograms for predicting OS and CSS in patients with PUC, which can help clinicians make treatment decisions.

     

基于SEER数据库男性乳腺癌手术切除病人预后分析及列线图建立

目的 分析男性乳腺浸润性导管癌手术切除病人的独立预后因素及构建预后列线图，同时验证该模型的准确性。方法 从美国国立癌症研究监测、流行病学和最终结果（SEER）数据库中下载2010—2018年间诊断为男性乳腺浸润性导管癌且经过手术切除的1662例病人的临床病理特征及治疗信息。随机数字分组法将病人按照3∶1分为训练队列（1246例）和验证队列（416例）。 通过单因素及多因素 COX分析筛选出独立预后因素并构建预测1、3、5年的总生存率（OS）的列线图。一致性指数（c-指数）和校准曲线确定列线图预测的准确性和判别能力。结果 年龄、肿瘤直径、临床TNM、病理学分级、婚姻状态5个指标均是OS的独立预后因素（P均<0.05）。基于独立预后因素构建了1、3、5年OS的列线图。训练队列中列线图的c-指数为0.730（95%CI 0.694-0.766），高于美国癌症联合委员会（AJCC）临床TNM分期系统 0.628（95%CI 0.588-0.668）；验证队列列线图的c-指数为0.737（95%CI 0.680-0.794），高于AJCC 临床TNM分期系统 0.584（95%CI 0.516-0.652）。校准曲线表明列线图预测生存率与实际生存率具有良好的一致性。结论 基于年龄、肿瘤直径、临床TNM、病理学分级、婚姻状态的独立预后因素构建的列线图能较准确地显示男性乳腺癌手术切除病人预后，有利于进行临床个体化预后评估。     

Development and validation of prognostic nomograms and a web-based survival rate calculator for sarcomatoid renal cell carcinoma in pre- and post-treatment patients

BackgroundTo develop a clinical prediction model and web-based survival rate calculator to predict the overall survival (OS) and cancer-specific survival (CSS) of sarcomatoid renal cell carcinoma (SRCC) for clinical diagnosis and treatment.MethodsSRCC patient data were retrieved from Surveillance, Epidemiology, and End Results (SEER) database. Factors independently associated with survival were identified by a Cox regression analysis. Nomograms of the prediction model were constructed using a SEER training cohort and validated with a SEER validation cohort. At the same time, the decision analysis curve, receiver operating characteristic curve, and calibration curve were also used to examine and evaluate the model. A web-based survival rate calculator was constructed to help assist in the assessment of the disease condition and clinical prognosis.ResultsThe records of 2,742 SRCC cases were retrieved from SEER, while 1,921 cases with a median OS of 14 and CSS of 32 months were used as the training cohort. The developed nomograms were more accurate than that of the American Joint Committee on Cancer staging (C-indexes of 0.767 versus 0.725 for OS and 0.775 versus 0.715 for CSS), with better discrimination than that of the American Joint Committee on Cancer (AJCC) stage model and the calibration was validated in the SEER validation cohort. The model’s 3- and 5-year OS and CSS were superior to AJCC and T staging on the analysis decision curve. The prognosis prediction of SRCC established by the prediction model could be evaluated through the web-based survival rate calculator, which plays a guiding role in clinical treatment.ConclusionsNomograms and a web-based survival rate calculator predicting the OS and CSS of SRCC patients with better discrimination and calibration were developed.     

Establishment and validation of a novel predictive model to quantify the risk of bone metastasis in patients with prostate cancer

BackgroundPatients with prostate cancer (PCa) commonly suffer from bone metastasis during disease progression. This study aims to construct and validate a nomogram to quantify bone metastasis risk in patients with PCa.MethodsClinicopathological data of patients diagnosed with PCa between 2010 and 2015 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Predictors for bone metastasis were identified by logistic regression analyses to establish a nomogram. The concordance index (c-index) and calibration plots were generated to assess the nomogram’s discrimination, and the area under the receiver operating characteristic curve (AUC) was used to compare the precision of the nomogram with routine staging systems. The nomogram’s clinical performance was evaluated by decision curve analysis (DCA) and clinical impact curves (CIC). Independent prognostic factors were identified by Cox regression analysis.ResultsA total of 168,414 eligible cases were randomly assigned to the training cohort or validation cohort at a ratio of 1:1. The nomogram, which was established based on independent factors, showed good accuracy, with c-indexes of 0.911 in the training set and 0.910 in the validation set. Calibration plots also approached 45 degrees. After other distant metastatic sites were included in the predictive model, the new nomogram displayed superior prediction performance. The AUCs and net benefit of the nomograms were both higher than those of other routine staging systems. Furthermore, bone metastasis prediction points were shown to be a new risk factor for overall survival.ConclusionsNovel validated nomograms can effectively predict the risk of bone metastasis in patients with PCa and help clinicians improve cancer management.     

Development and validation of a nomogram to predict postoperative cancer-specific survival of patients with nonmetastatic T3a renal cell carcinoma

PurposeTo establish a nomogram for the prediction of postoperative cancer-specific survival (CSS) in patients with nonmetastatic T3a renal cell carcinoma (RCC).MethodsThe Surveillance, Epidemiology, and End Results database were searched for patients with pT3aN0-1M0 RCC between 2010 and 2018. The patients were randomly stratified into the training and verification group (7:3 ratio). Using Cox regression analysis, the predictors for the CSS in the training group were integrated to establish the nomogram for predicting the 3-year and 5-year CSS. Harrell's concordance index (C-index), time-dependent receiver operating characteristic curve, decision curve analysis, and Kaplan–Meier survival analysis were used to evaluate the nomogram performance.ResultsA total of 5,791 pT3aN0-1M0 RCC cases with eligible data were selected from the Surveillance, Epidemiology, and End Results database. Age, tumor size, surgery type, Fuhrman grade, histological type, sarcomatoid, N stage, and invasion patterns were identified as the significant predictors for CSS to establish the nomogram. The C-indices of the nomogram were 0.774 (95% CI: 0.753–0.795) and 0.777 (95% CI: 0.745–0.809) for the training and verification group, respectively. The calibration of the nomogram revealed consistency between the predicted and observed survival. The area under the 3-year and 5-year CSS receiver operating characteristic curves were 0.773 and 0.786 in the training group, respectively. Decision curve analysis showed the optimal application of the model in clinical decision-making. According to the cutoff values of prognostic indices, patients with low-risk showed better CSS than those with high-risk in both training and verification groups (both P< 0.0001).ConclusionThe current nomogram could effectively predict the CSS of patients with nonmetastatic T3a RCC, and could be used to identify patients who might need a compact interval of follow-up and postoperative adjuvant systemic treatment. The limitations included the retrospective nature, absence of external validation, and several unmeasured variables related to the selection bias of surgery type. The results should be interpreted with caution.