无转移肾癌预后模型在中国人群中的应用及改良
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摘要
肾细胞癌是泌尿生殖系常见的恶性肿瘤,其患病率占全身恶性肿瘤的3%,占肾肿瘤的85%以上,约25%的患者存在肿瘤转移。影响肾癌预后的因素有很多,其中不乏独立的预后因素,如肿瘤的TNM分期、分级、组织学分型、肿瘤大小、肿瘤组织坏死、生活质量评分以及一些分子标志物的表达,这些指标已经能一定程度地指导肾癌患者的预后评估。但随着发现的影响因素越来越多,单独应用某一指标已经不足以完全评定预后,因此国外有学者提出可以使用预后分级系统,将肾癌的风险分级并且判断生存率,还可以帮助临床设计试验并指导随访策略。
肾癌预后分析系统是利用统计学方法建立的数学模型,它根据不同因素在肾癌预后过程中产生影响的大小判断疾病风险及生存率,并将结果转化为直观的公式或图表,具有良好的操作性和准确性。其方法包括公式法、评分法以及诺摩图法(nomogram)。同时,由于基因芯片及蛋白质组等新技术的引进,整合了临床信息和分子标志物的预后模型进一步提高了其分析的准确性。目前,已开发应用的肾癌预后分析系统共13种,用于无转移肾细胞癌的共9种,但其准确性在不同样本中还存在较大差异。此外,目前应用的肾癌预后模型无论其建立还是验证过程都是基于高加索人群,在其他人群中的适用性还有待于检验。
本研究旨在通过我院病例库中肾癌患者的随访结果,判断现有无转移肾癌预后模型在中国人群中的适用性,同时结合组织芯片技术,在进一步分析与肾癌预后相关同临床信息和分子标志物的基础上,建立首个基于中国人群的肾癌预后分析系统,并对其准确性进行验证。
第一部分应用临床与病理指标的肾癌预后模型在中国人群中的应用
目的探讨现有应用临床与病理指标的无转移肾癌预后模型在中国人群中的适用性及准确性。
方法回顾性分析我院1993年至2004年行手术治疗的653例无转移肾癌患者的临床及预后情况。应用现有的7种模型判断患者预后,包括Yaycioglu model、Cindolomodel、SSIGN、UISS、Kattan nomogram、Sorbellini nomogram、Karakiewicz nomogram。根据总生存时间(OS)、肿瘤特异性生存时间(CSS)以及无瘤生存时间(RFS),比较模型判断结果与实际随访结果之间的差异,从而推断不同模型在中国人群中的适用性与准确性。采用SPSS13.0及S-Plus统计软件,不同风险组间生存时间的比较应用Kaplan-Meier法,不同模型的预测准确性比较应用Harrell一致性指数(Harrell's c-index),P＜0.05为差异有显著性意义。
结果(1)至随访结束,653例患者中,123例死于肿瘤特异性原因,36例死于非肿瘤特异性原因,156例出现肿瘤复发,平均随访时间65个月。(2)按照不同模型的风险分组方式对患者进行分组,比较同一模型不同风险组间各种生存时间的差别,组间均有统计学意义(P＜0.05),显示7种模型均能对患者进行有效的风险分组。(3)通过计算Harrell一致性指数(C-indexes),比较不同模型判断预后的准确程度。Kattan nomogram在3种随访时间的比较中,准确性均为最高,其一致性指数为0.752(OS),0.793(CSS)及0.84(RFS)。另外两种nomogram模型Sorbellini nomogram与Karakiewicz nomogram的准确性虽然较Kattan nomogram略低,但之间没有统计学差异,且明显高于非nomogram模型。SSIGN model在非nomogram模型中准确性最高,其一致性指数为0.712(OS),0.751(CSS)及0.777(RFS)。Cindolo model虽然仅包括肿瘤大小与临床症状,但准确性与SSIGN模型没有统计学差异(P＜0.05)。在所有模型中,Yaycioglu model准确性最低,其一致性指数为0.616(OS),0.649(CSS)及0.661(RFS)。
结论肾癌预后模型的应用,较单独应用某一指标可大大提高预后判断的准确性。诺摩图法作为一种较新的统计方法,在肾癌预后判断中具有较高的应用价值。在中国人群的验证中,3种诺摩图模型均表现出较高的准确性。而评分法虽然包括较少的临床指标,其准确性也有待于提高,但在某些样本中同样可有较高的准确性,同时因其指标少,使此类模型能在术前患者评估方面发挥重要用。因此,应用肾癌预后分析系统要根据不同的环境和目的进行选择,同时在应用前要进行验证。
第二部分结合分子指标的肾癌预后模型在中国人群中的应用
目的应用组织芯片,探讨联合临床信息和分子标志物的预后模型在中国人群中的适用性及准确性。
方法检索相关文献,获得现有2种应用分子标志物的无转移肾癌预后模型。应用我院病例库中有明确随访记录的无转移肾癌患者的组织蜡块482例,构建组织芯片,芯片上同时加入10例正常肾组织作为对照。选择两种可应用于无转移肾癌的预后模型,其中一种可同时应用于转移和无转移肾癌,包括的分子标志物为CA9,Vimentin,P53,Gelsolin;另外一种只应用于无转移肾癌,包括的分子标志物为P53,Ki-67,VEGF-D,VEGFR-1。应用免疫组化SABC法检测模型中相关分子标志物的表达。根据总生存时间(OS)、肿瘤特异性生存时间(CSS)以及无瘤生存时间(RFS),应用Kaplan-Meier法比较同一模型内不同风险组间生存时间,应用Harrell一致性指数(Harrell's c-index)比较不同模型的预测准确性。采用SPSS13.0及S-Plus统计软件,P＜0.05为差异有显著性意义。
结果(1)至随访结束,482例患者中,131例死于肿瘤特异性原因,36例死于非肿瘤特异性原因,128例出现肿瘤复发,平均随访时间62个月。(2)肾癌组织中CA9,Vimentin,P53,Ki-67,Gelsolin,VEGFR-1及VEGF-D蛋白表达总阳性率分别为85.3%(411/482),65.1%(314/482),31.7%(153/482),61.6%(297/482),41-3%(199/482),70.1%(338/482),25.9%(125/482)。(3)为方便分组比较,将患者按诺摩图计算的生存率分为5组(＜0.6,0.6-0.7,0.7-0.8,0.8-0.9,0.9—1.0),比较不同组间各种生存时间的差别,组间均有统计学意义(P＜0.05),显示2种模型均能对患者进行有效的风险分组。(4)只针对无转移肾癌的预后模型其一致性指数为0.812(OS),0.833(CSS)及0.872(RFS),为现有模型中最高。针对所有肾癌的预后模型其一致性指数为0.742(OS),0.750(CSS)及0.761(RFS)。
结论在第一部分研究中,诺摩图法表现出较高的应用价值。而结合分子标志物的诺摩图模型,使预测准确性进一步提高。其中,针对无转移肾癌的预后模型在本研究样本中显示出较高的适用性,而另外一个模型由于其建立是同时针对转移与无转移的肾癌人群,因此在无转移样本中准确性受到一定影响。此外,包括不同分子标志物的模型其准确性存在差别,提示不同人群中的分子标志物可能存在差异,也从侧面反映了肿瘤的生长异质性。由于目前所有预后模型的统计数据均来自高加索人群的资料,因此,要建立中国人群的肾癌预后模型,其临床指标和分子标志物均应重新挑选验证。
第三部分基于中国人群的肾癌预后模型的建立与验证
目的建立基于中国人群肾癌资料的预后模型,并对其准确性进行验证。
方法检索肾癌预后相关的中英文文献,寻找现有预后模型以外其他的对肾癌预后有影响的临床指标与分子标志物。利用第二部分中建立的组织芯片,对找到的新分子标志物进行免疫组化检测。对所有临床指标和分子标志物染色结果按不同随访终点进行多因素Cox比例风险模型回归分析,筛选对预后有影响的指标。根据Cox回归分析结果,利用诺摩图法分别建立针对总生存时间,肿瘤特异性生存时间及肿瘤复发时间的肾癌预后模型。新模型建立后应用Kaplan-Meier法与Harrell一致性指数对模型的准确性进行检测。Cox回归分析及Kaplan-Meier法应用SPSS13.0软件完成,诺摩图的建立及一致性指数的计算应用统计软件S-plus及其扩展包Design and Hmisc libraries完成,所有统计计算中P＜0.05认为差异有显著性意义。
结果(1)通过检索文献,发现多种肾癌预后相关的分子生物学指标,除模型中的以外,还包括p27,PTEN,SKP-2,COX-2,HIF-1α,VEGF,Cyclin D1,CXCR3,EpCAM,Survivin。(2)多因素Cox回归分析结果显示,VEGF(P=0.027),p27(P=0.016),p53(P=0.006),T分期(P＜0.001),肿瘤大小(P＜0.001)以及ECOG-PS(P=0.001)与患者预后显著相关。(3)根据Cox回归分析结果建立预测无转移肾癌患者总生存时间、肿瘤特异性生存时间以及复发时间的预后模型,该组模型不但可将肾癌患者准确分组,并且获得较高的一致性指数,根据不同随访终点分别为0.832(OS),0.883(CSS)及0.901(RFS),并且其准确性显著高于既往预后模型。
结论利用诺摩图法建立的联合临床指标和分子标志物的肾癌预后模型,包括了肿瘤体积,T分期,ECOG评分以及分子标志物P27,P53,VEGF。该模型的准确性不但优于单用临床指标的模型,并且高于其他应用分子标志物的预后模型。该模型的建立,对于中国肾癌患者的预后判断将会提供一定帮助。另外,该模型在其他样本中的适用性还有待于进一步验证
在本研究中,我们利用我院肾癌患者资料首次在高加索以外人群中对现有无转移肾癌预后模型进行了验证,同时,在广泛检索文献的基础上,对未纳入模型,但可能影响肾癌预后的指标进行了验证,并在此基础上首次建立了基于中国人群的肾癌预后模型。由于我们在建立模型时考虑了不同的随访终点,因此针对总生存时间、肿瘤特异性生存时间及复发时间建立了一组预后模型,根据不同的模型,可对患者的总生成时间、肿瘤特异性生存时间及复发时间进行有效预测。后期的统计学检验证明该组模型具有较高的准确性。一个有效的肾癌预后模型的建立,将会极大的帮助临床医生向患者提供咨询,进而制定有效的诊疗计划和随访策略,同时此类模型也将成为临床试验设计的有效辅助工具。
Renal cell carcinoma(RCC) is the most common malignancy in adult kidney,represent over 85%of renal cell carcinomas,and accounts for 3%of all human malignancy. Furthermore,approximately 25%of patients will present with metastatic disease.Currently, there are several factors can be used as predictors of survival for patients with RCC, including TNM stage,Fuhrman grade,histologic type,tumor size,tumor necrosis,Eastern Cooperative Oncology Group(ECOG) performance status(PS) and some molecular markers. However,single variable often provide low accuracy in predicting prognoses of RCC,and new prognostic models have been recently proposed by some researchers.These models are able to classify patients in groups with different prognoses or calculate with a punctual precision the survival probabilities of each single patient.Furthermore,they are helpful tools in the planning of postoperative follow-up and the design and interpretation of the results of RCTs.
The prediction system of RCC is mathematical models developed based on the statistical method.The models were projected with the objective to calculate the survival probability containing all available clinical and pathological information.With a higher accuracy and easier manipuility,their use in clinical practice and research trials is widespread. According to their structure,the models are classified into formula,algorithm and nomogram. More recently,with the development of gene arrays and proteomics,the model including both the clinical variables and molecular markers was projected,which might provide more accurate prognostic information.Till now,13 prognostic models were proposed in the literature and 9 models were proposed for nonmetastatic renal cell carcinoma.However,these models have varying degrees of accuracy in different patient samples.Furthermore,the research of developing and validating the models were entirely based on Caucasian population,no application of the models in Asian population has been published in the literature to date.
The aim of this study was to better define the general applicability of the currently used prognostic models for nonmetastatic RCC in Chinese population based on 15-year experience in a large single center in China.And we also evaluated molecular prognostic markers selected based on a review of the scientific literature by a tissue microarray.Finally,a nomogram including molecular and clinical predictors has been developed based on the Chinese population.The accuracy of the models was also compared with currently used models for predicting survival.
Part 1.Validation of the current clinical prognostic models for Nonmetastatic Renal Cell Carcinoma after Nephrectomy in Chinese population
Objective:To explore the general applicability of the current clinical prognostic models for nonmetastatic renal cell carcinoma in Chinese population.
Methods:Clinical and pathological variables of 653 nonmetastatic renal cell carcinoma patients in our hospital from 1993 to 2004 were retrospectively reviewed.7 models were used to predict the prognosis,including the Yaycioglu model,the Cindolo model,the UISS model, the SSIGN model,the Kattan nomogram,Sorbellini nomogram and Karakiewicz nomogram. 3 different endpoints were used for validation,including overall survival(OS), cancer-specific survival(CSS),and recurrence-free survival(RFS).Survival was estimated by the Kaplan-Meier method.Discriminating ability was assessed by the Harrell's c-index. All statistical tests were two-sided,with significance defined as P＜0.05.Analyses were performed using SPSS version 13.0 and S-Plus 6 software packages with the Design and Hmisc libraries.
Results:(1) At last follow-up,159 patients had died of any causes,123 patients died of cancer progression,and disease recurrence occurred in 156 patients.Overall median follow-up is 65 months.(2) The discriminating ability of all models was confirmed in the Chinese population.Different groups in the same model had significant differences in survival analysis.(3) The Kattan nomogram was the most accurate,with the highest C-indexes of 0.752,0.793 and 0.841 for OS,CSS,and RFS,respectively.Sorbellini nomogram and Karakiewicz nomogram also presented high accuracy,though a little lower than Kattan nomogram,with no significant difference.SSIGN model is the most accurate model in algorithm models,with the C-indexes of 0.712,0.751 and 0.777 for OS,CSS,and RFS,respectively.And the Cindolo model performed as well as the SSIGN model,though only including clinical presentation and size of tumor.In all models,Yaycioglu model showed lowest accuracy,with the C-indexes of 0.616,0.649 and 0.661 for OS,CSS,and RFS, respectively.
Conclusions:Mathematical models have a prognostic accuracy higher than the one of the single clinical and/or pathological variables.The results showed that nomograms discriminate better than other models,regardless of endpoints.The Kattan model was found to be the most accurate.This study defines a better applicability of the nomograms for Chinese patients with nonmetastatic RCC treated with nephrectomy.Though with a lower accuracy,algorithms could be a useful tool for patient counseling.Therefore,models should be chosen according to different environments and purposes.
Part 2.Validation of the prognostic models including molecular markers for Nonmetastatic Renal Cell Carcinoma after Nephrectomy in Chinese population
Objective:To explore the applicability of the models including molecular markers for nonmetastatic renal cell carcinoma in Chinese population,based on a tissue microarray.
Methods:A custom tissue array was constructed from 482 RCC patients who underwent nephrectomy.Clinical and pathological variables of all patients were retrospectively reviewed. Immunohistochemistry was performed for protein markers in the 2 prognostic models both developed by Kim et al,including CA9,Vimentin,P53,ki-67,Gelsolin,VEGFR-1 and VEGF-D.3 different endpoints were used for validation,including overall survival(OS), cancer-specific survival(CSS),and recurrence-free survival(RFS).Survival was estimated by the Kaplan- Meier method.Discriminating ability was assessed by the Harrell's c-index. All statistical tests were two-sided,with significance defined as P＜0.05.Analyses were performed using SPSS version 13.0 and S-Plus 6 software packages with the Design and Hmisc libraries.
Results:(1) At last follow-up,167 patients had died of any causes,131 patients died of cancer progression,and disease recurrence occurred in 128 patients.Overall median follow-up is 65 months.(2) The expression of CA9,Vimentin,P53,Ki-67,Gelsolin, VEGFR-1 and VEGF-D were identified in 85.3%(411/482),65.1%(314/482),31.7% (153/482),61.6%(297/482),41.3%(199/482),70.1%(338/482),25.9%(125/482) of renal cell carcinomas,respectively.(3) For descriptive purposes only,individual probability values from the nomogram were arbitrarily categorized in 5 classes(＜0.6,0.6-0.7,0.7-0.8,0.8-0.9, and 0.9-1.0).Different groups in the same model had significant differences in survival analysis.The discriminating ability of all models was confirmed in the Chinese population.(4) The C-indexes of the model developed for nonmetastatic renal cell carcinoma only was 0.812, 0.833 and 0.872 for OS,CSS,and RFS,respectively,which was highest in all models.While the number of the model developed for both the metastatic and nonmetastatic renal cell carcinoma was 0.778,0.782 and 0.799 for OS,CSS,and RFS,respectively.
Conclusions:In patients with RCC,a prognostic model for survival that includes molecular and clinical predictors is significantly more accurate than a standard clinical model. The model developed for nonmetastatic renal cell carcinoma showed well applicability in our samples.But the other one did not perform well in the nonmetastatic RCCs because it was developed based on both the metastatic and nonmetastatic population.Furthermore,the different accuracy of the 2 models implied that prognostic implication of tumor markers might differ in various populations,due to the heterogeneity tumorigeness.Therefore,if we want to develop the Chinese version of prognostic model,clinical and molecular markers should be re-examination.
Part 3.Development and validation of a new prognostic models for renal cell carcinoma based on the Chinese population
Objective:To propose a prognostic model for renal cell carcinoma that includes molecular and clinical predictors in Chinese population.
Methods:A systematic literature review was performed to search for molecular markers influence prognosis in nonmetastatic RCC.Immunohistochemical analysis was done on the tissue microarray of all searched RCC related markers.Associations between predictors and survival time were evaluated with Cox models.According to the result of Cox model, prognostic models were developed for OS,CSS,and RFS,respectively,using markers and clinical predictors significantly affect prognosis.And the prognostic accuracy was assessed by the Harrell's c-index.
Results:(1) From the initial search,we selected 10 new makers which might influence the prognosis of RCC,including Ki67,p27,SKP-2,COX-2,HIF-1α,VEGF,Cyclin D1, CXCR3,EpCAM,Survivino(2) On multivariate Cox regression analysis that included all markers and clinical variables,VEGF(P=0.027),p27(P=0.016),p53(P=0.006),T category (P＜0.001),tumor size(P＜0.001) and ECOG-PS(P=0.001) were significant independent predictors of disease specific survival and they were used to construct a combined molecular and clinical prognostic model.(3) The constructed nomogram combined the clinical and molecular factors and approached the concordance index of 0.852,0.883 and 0.901 for OS, CSS and RFS,respectively.The C-index of the model was significantly higher than that of previous models.
Conclusions:A nomogram consisting of 6 predictors(VEGF,p27,p53,T category, tumor size and ECOG-PS) was constructed based on the Chinese population.To our knowledge,this is the first prognostic model in Chinese RCC patients.The prognostic ability of the nomogram may be superior to clinical factors alone and even previous models. However,independent extemal validation of the nomogram is required.
In our research,we validated the prognostic models for RCC based on the population other than Caucasian for the first time.Furthermore,we also evaluated individual candidate molecular markers for prognostic information.Based on the above research,we finally proposed the first prognostic model in Chinese population.By using the model,we could precisely predict the prognosis of RCC patients.With validation in independent patient samples,the model will be a useful tool for patient counseling,clinical trial design and patient follow-up planning.
肾癌预后分析系统是利用统计学方法建立的数学模型,它根据不同因素在肾癌预后过程中产生影响的大小判断疾病风险及生存率,并将结果转化为直观的公式或图表,具有良好的操作性和准确性。其方法包括公式法、评分法以及诺摩图法(nomogram)。同时,由于基因芯片及蛋白质组等新技术的引进,整合了临床信息和分子标志物的预后模型进一步提高了其分析的准确性。目前,已开发应用的肾癌预后分析系统共13种,用于无转移肾细胞癌的共9种,但其准确性在不同样本中还存在较大差异。此外,目前应用的肾癌预后模型无论其建立还是验证过程都是基于高加索人群,在其他人群中的适用性还有待于检验。
本研究旨在通过我院病例库中肾癌患者的随访结果,判断现有无转移肾癌预后模型在中国人群中的适用性,同时结合组织芯片技术,在进一步分析与肾癌预后相关同临床信息和分子标志物的基础上,建立首个基于中国人群的肾癌预后分析系统,并对其准确性进行验证。
第一部分应用临床与病理指标的肾癌预后模型在中国人群中的应用
目的探讨现有应用临床与病理指标的无转移肾癌预后模型在中国人群中的适用性及准确性。
方法回顾性分析我院1993年至2004年行手术治疗的653例无转移肾癌患者的临床及预后情况。应用现有的7种模型判断患者预后,包括Yaycioglu model、Cindolomodel、SSIGN、UISS、Kattan nomogram、Sorbellini nomogram、Karakiewicz nomogram。根据总生存时间(OS)、肿瘤特异性生存时间(CSS)以及无瘤生存时间(RFS),比较模型判断结果与实际随访结果之间的差异,从而推断不同模型在中国人群中的适用性与准确性。采用SPSS13.0及S-Plus统计软件,不同风险组间生存时间的比较应用Kaplan-Meier法,不同模型的预测准确性比较应用Harrell一致性指数(Harrell's c-index),P＜0.05为差异有显著性意义。
结果(1)至随访结束,653例患者中,123例死于肿瘤特异性原因,36例死于非肿瘤特异性原因,156例出现肿瘤复发,平均随访时间65个月。(2)按照不同模型的风险分组方式对患者进行分组,比较同一模型不同风险组间各种生存时间的差别,组间均有统计学意义(P＜0.05),显示7种模型均能对患者进行有效的风险分组。(3)通过计算Harrell一致性指数(C-indexes),比较不同模型判断预后的准确程度。Kattan nomogram在3种随访时间的比较中,准确性均为最高,其一致性指数为0.752(OS),0.793(CSS)及0.84(RFS)。另外两种nomogram模型Sorbellini nomogram与Karakiewicz nomogram的准确性虽然较Kattan nomogram略低,但之间没有统计学差异,且明显高于非nomogram模型。SSIGN model在非nomogram模型中准确性最高,其一致性指数为0.712(OS),0.751(CSS)及0.777(RFS)。Cindolo model虽然仅包括肿瘤大小与临床症状,但准确性与SSIGN模型没有统计学差异(P＜0.05)。在所有模型中,Yaycioglu model准确性最低,其一致性指数为0.616(OS),0.649(CSS)及0.661(RFS)。
结论肾癌预后模型的应用,较单独应用某一指标可大大提高预后判断的准确性。诺摩图法作为一种较新的统计方法,在肾癌预后判断中具有较高的应用价值。在中国人群的验证中,3种诺摩图模型均表现出较高的准确性。而评分法虽然包括较少的临床指标,其准确性也有待于提高,但在某些样本中同样可有较高的准确性,同时因其指标少,使此类模型能在术前患者评估方面发挥重要用。因此,应用肾癌预后分析系统要根据不同的环境和目的进行选择,同时在应用前要进行验证。
第二部分结合分子指标的肾癌预后模型在中国人群中的应用
目的应用组织芯片,探讨联合临床信息和分子标志物的预后模型在中国人群中的适用性及准确性。
方法检索相关文献,获得现有2种应用分子标志物的无转移肾癌预后模型。应用我院病例库中有明确随访记录的无转移肾癌患者的组织蜡块482例,构建组织芯片,芯片上同时加入10例正常肾组织作为对照。选择两种可应用于无转移肾癌的预后模型,其中一种可同时应用于转移和无转移肾癌,包括的分子标志物为CA9,Vimentin,P53,Gelsolin;另外一种只应用于无转移肾癌,包括的分子标志物为P53,Ki-67,VEGF-D,VEGFR-1。应用免疫组化SABC法检测模型中相关分子标志物的表达。根据总生存时间(OS)、肿瘤特异性生存时间(CSS)以及无瘤生存时间(RFS),应用Kaplan-Meier法比较同一模型内不同风险组间生存时间,应用Harrell一致性指数(Harrell's c-index)比较不同模型的预测准确性。采用SPSS13.0及S-Plus统计软件,P＜0.05为差异有显著性意义。
结果(1)至随访结束,482例患者中,131例死于肿瘤特异性原因,36例死于非肿瘤特异性原因,128例出现肿瘤复发,平均随访时间62个月。(2)肾癌组织中CA9,Vimentin,P53,Ki-67,Gelsolin,VEGFR-1及VEGF-D蛋白表达总阳性率分别为85.3%(411/482),65.1%(314/482),31.7%(153/482),61.6%(297/482),41-3%(199/482),70.1%(338/482),25.9%(125/482)。(3)为方便分组比较,将患者按诺摩图计算的生存率分为5组(＜0.6,0.6-0.7,0.7-0.8,0.8-0.9,0.9—1.0),比较不同组间各种生存时间的差别,组间均有统计学意义(P＜0.05),显示2种模型均能对患者进行有效的风险分组。(4)只针对无转移肾癌的预后模型其一致性指数为0.812(OS),0.833(CSS)及0.872(RFS),为现有模型中最高。针对所有肾癌的预后模型其一致性指数为0.742(OS),0.750(CSS)及0.761(RFS)。
结论在第一部分研究中,诺摩图法表现出较高的应用价值。而结合分子标志物的诺摩图模型,使预测准确性进一步提高。其中,针对无转移肾癌的预后模型在本研究样本中显示出较高的适用性,而另外一个模型由于其建立是同时针对转移与无转移的肾癌人群,因此在无转移样本中准确性受到一定影响。此外,包括不同分子标志物的模型其准确性存在差别,提示不同人群中的分子标志物可能存在差异,也从侧面反映了肿瘤的生长异质性。由于目前所有预后模型的统计数据均来自高加索人群的资料,因此,要建立中国人群的肾癌预后模型,其临床指标和分子标志物均应重新挑选验证。
第三部分基于中国人群的肾癌预后模型的建立与验证
目的建立基于中国人群肾癌资料的预后模型,并对其准确性进行验证。
方法检索肾癌预后相关的中英文文献,寻找现有预后模型以外其他的对肾癌预后有影响的临床指标与分子标志物。利用第二部分中建立的组织芯片,对找到的新分子标志物进行免疫组化检测。对所有临床指标和分子标志物染色结果按不同随访终点进行多因素Cox比例风险模型回归分析,筛选对预后有影响的指标。根据Cox回归分析结果,利用诺摩图法分别建立针对总生存时间,肿瘤特异性生存时间及肿瘤复发时间的肾癌预后模型。新模型建立后应用Kaplan-Meier法与Harrell一致性指数对模型的准确性进行检测。Cox回归分析及Kaplan-Meier法应用SPSS13.0软件完成,诺摩图的建立及一致性指数的计算应用统计软件S-plus及其扩展包Design and Hmisc libraries完成,所有统计计算中P＜0.05认为差异有显著性意义。
结果(1)通过检索文献,发现多种肾癌预后相关的分子生物学指标,除模型中的以外,还包括p27,PTEN,SKP-2,COX-2,HIF-1α,VEGF,Cyclin D1,CXCR3,EpCAM,Survivin。(2)多因素Cox回归分析结果显示,VEGF(P=0.027),p27(P=0.016),p53(P=0.006),T分期(P＜0.001),肿瘤大小(P＜0.001)以及ECOG-PS(P=0.001)与患者预后显著相关。(3)根据Cox回归分析结果建立预测无转移肾癌患者总生存时间、肿瘤特异性生存时间以及复发时间的预后模型,该组模型不但可将肾癌患者准确分组,并且获得较高的一致性指数,根据不同随访终点分别为0.832(OS),0.883(CSS)及0.901(RFS),并且其准确性显著高于既往预后模型。
结论利用诺摩图法建立的联合临床指标和分子标志物的肾癌预后模型,包括了肿瘤体积,T分期,ECOG评分以及分子标志物P27,P53,VEGF。该模型的准确性不但优于单用临床指标的模型,并且高于其他应用分子标志物的预后模型。该模型的建立,对于中国肾癌患者的预后判断将会提供一定帮助。另外,该模型在其他样本中的适用性还有待于进一步验证
在本研究中,我们利用我院肾癌患者资料首次在高加索以外人群中对现有无转移肾癌预后模型进行了验证,同时,在广泛检索文献的基础上,对未纳入模型,但可能影响肾癌预后的指标进行了验证,并在此基础上首次建立了基于中国人群的肾癌预后模型。由于我们在建立模型时考虑了不同的随访终点,因此针对总生存时间、肿瘤特异性生存时间及复发时间建立了一组预后模型,根据不同的模型,可对患者的总生成时间、肿瘤特异性生存时间及复发时间进行有效预测。后期的统计学检验证明该组模型具有较高的准确性。一个有效的肾癌预后模型的建立,将会极大的帮助临床医生向患者提供咨询,进而制定有效的诊疗计划和随访策略,同时此类模型也将成为临床试验设计的有效辅助工具。
Renal cell carcinoma(RCC) is the most common malignancy in adult kidney,represent over 85%of renal cell carcinomas,and accounts for 3%of all human malignancy. Furthermore,approximately 25%of patients will present with metastatic disease.Currently, there are several factors can be used as predictors of survival for patients with RCC, including TNM stage,Fuhrman grade,histologic type,tumor size,tumor necrosis,Eastern Cooperative Oncology Group(ECOG) performance status(PS) and some molecular markers. However,single variable often provide low accuracy in predicting prognoses of RCC,and new prognostic models have been recently proposed by some researchers.These models are able to classify patients in groups with different prognoses or calculate with a punctual precision the survival probabilities of each single patient.Furthermore,they are helpful tools in the planning of postoperative follow-up and the design and interpretation of the results of RCTs.
The prediction system of RCC is mathematical models developed based on the statistical method.The models were projected with the objective to calculate the survival probability containing all available clinical and pathological information.With a higher accuracy and easier manipuility,their use in clinical practice and research trials is widespread. According to their structure,the models are classified into formula,algorithm and nomogram. More recently,with the development of gene arrays and proteomics,the model including both the clinical variables and molecular markers was projected,which might provide more accurate prognostic information.Till now,13 prognostic models were proposed in the literature and 9 models were proposed for nonmetastatic renal cell carcinoma.However,these models have varying degrees of accuracy in different patient samples.Furthermore,the research of developing and validating the models were entirely based on Caucasian population,no application of the models in Asian population has been published in the literature to date.
The aim of this study was to better define the general applicability of the currently used prognostic models for nonmetastatic RCC in Chinese population based on 15-year experience in a large single center in China.And we also evaluated molecular prognostic markers selected based on a review of the scientific literature by a tissue microarray.Finally,a nomogram including molecular and clinical predictors has been developed based on the Chinese population.The accuracy of the models was also compared with currently used models for predicting survival.
Part 1.Validation of the current clinical prognostic models for Nonmetastatic Renal Cell Carcinoma after Nephrectomy in Chinese population
Objective:To explore the general applicability of the current clinical prognostic models for nonmetastatic renal cell carcinoma in Chinese population.
Methods:Clinical and pathological variables of 653 nonmetastatic renal cell carcinoma patients in our hospital from 1993 to 2004 were retrospectively reviewed.7 models were used to predict the prognosis,including the Yaycioglu model,the Cindolo model,the UISS model, the SSIGN model,the Kattan nomogram,Sorbellini nomogram and Karakiewicz nomogram. 3 different endpoints were used for validation,including overall survival(OS), cancer-specific survival(CSS),and recurrence-free survival(RFS).Survival was estimated by the Kaplan-Meier method.Discriminating ability was assessed by the Harrell's c-index. All statistical tests were two-sided,with significance defined as P＜0.05.Analyses were performed using SPSS version 13.0 and S-Plus 6 software packages with the Design and Hmisc libraries.
Results:(1) At last follow-up,159 patients had died of any causes,123 patients died of cancer progression,and disease recurrence occurred in 156 patients.Overall median follow-up is 65 months.(2) The discriminating ability of all models was confirmed in the Chinese population.Different groups in the same model had significant differences in survival analysis.(3) The Kattan nomogram was the most accurate,with the highest C-indexes of 0.752,0.793 and 0.841 for OS,CSS,and RFS,respectively.Sorbellini nomogram and Karakiewicz nomogram also presented high accuracy,though a little lower than Kattan nomogram,with no significant difference.SSIGN model is the most accurate model in algorithm models,with the C-indexes of 0.712,0.751 and 0.777 for OS,CSS,and RFS,respectively.And the Cindolo model performed as well as the SSIGN model,though only including clinical presentation and size of tumor.In all models,Yaycioglu model showed lowest accuracy,with the C-indexes of 0.616,0.649 and 0.661 for OS,CSS,and RFS, respectively.
Conclusions:Mathematical models have a prognostic accuracy higher than the one of the single clinical and/or pathological variables.The results showed that nomograms discriminate better than other models,regardless of endpoints.The Kattan model was found to be the most accurate.This study defines a better applicability of the nomograms for Chinese patients with nonmetastatic RCC treated with nephrectomy.Though with a lower accuracy,algorithms could be a useful tool for patient counseling.Therefore,models should be chosen according to different environments and purposes.
Part 2.Validation of the prognostic models including molecular markers for Nonmetastatic Renal Cell Carcinoma after Nephrectomy in Chinese population
Objective:To explore the applicability of the models including molecular markers for nonmetastatic renal cell carcinoma in Chinese population,based on a tissue microarray.
Methods:A custom tissue array was constructed from 482 RCC patients who underwent nephrectomy.Clinical and pathological variables of all patients were retrospectively reviewed. Immunohistochemistry was performed for protein markers in the 2 prognostic models both developed by Kim et al,including CA9,Vimentin,P53,ki-67,Gelsolin,VEGFR-1 and VEGF-D.3 different endpoints were used for validation,including overall survival(OS), cancer-specific survival(CSS),and recurrence-free survival(RFS).Survival was estimated by the Kaplan- Meier method.Discriminating ability was assessed by the Harrell's c-index. All statistical tests were two-sided,with significance defined as P＜0.05.Analyses were performed using SPSS version 13.0 and S-Plus 6 software packages with the Design and Hmisc libraries.
Results:(1) At last follow-up,167 patients had died of any causes,131 patients died of cancer progression,and disease recurrence occurred in 128 patients.Overall median follow-up is 65 months.(2) The expression of CA9,Vimentin,P53,Ki-67,Gelsolin, VEGFR-1 and VEGF-D were identified in 85.3%(411/482),65.1%(314/482),31.7% (153/482),61.6%(297/482),41.3%(199/482),70.1%(338/482),25.9%(125/482) of renal cell carcinomas,respectively.(3) For descriptive purposes only,individual probability values from the nomogram were arbitrarily categorized in 5 classes(＜0.6,0.6-0.7,0.7-0.8,0.8-0.9, and 0.9-1.0).Different groups in the same model had significant differences in survival analysis.The discriminating ability of all models was confirmed in the Chinese population.(4) The C-indexes of the model developed for nonmetastatic renal cell carcinoma only was 0.812, 0.833 and 0.872 for OS,CSS,and RFS,respectively,which was highest in all models.While the number of the model developed for both the metastatic and nonmetastatic renal cell carcinoma was 0.778,0.782 and 0.799 for OS,CSS,and RFS,respectively.
Conclusions:In patients with RCC,a prognostic model for survival that includes molecular and clinical predictors is significantly more accurate than a standard clinical model. The model developed for nonmetastatic renal cell carcinoma showed well applicability in our samples.But the other one did not perform well in the nonmetastatic RCCs because it was developed based on both the metastatic and nonmetastatic population.Furthermore,the different accuracy of the 2 models implied that prognostic implication of tumor markers might differ in various populations,due to the heterogeneity tumorigeness.Therefore,if we want to develop the Chinese version of prognostic model,clinical and molecular markers should be re-examination.
Part 3.Development and validation of a new prognostic models for renal cell carcinoma based on the Chinese population
Objective:To propose a prognostic model for renal cell carcinoma that includes molecular and clinical predictors in Chinese population.
Methods:A systematic literature review was performed to search for molecular markers influence prognosis in nonmetastatic RCC.Immunohistochemical analysis was done on the tissue microarray of all searched RCC related markers.Associations between predictors and survival time were evaluated with Cox models.According to the result of Cox model, prognostic models were developed for OS,CSS,and RFS,respectively,using markers and clinical predictors significantly affect prognosis.And the prognostic accuracy was assessed by the Harrell's c-index.
Results:(1) From the initial search,we selected 10 new makers which might influence the prognosis of RCC,including Ki67,p27,SKP-2,COX-2,HIF-1α,VEGF,Cyclin D1, CXCR3,EpCAM,Survivino(2) On multivariate Cox regression analysis that included all markers and clinical variables,VEGF(P=0.027),p27(P=0.016),p53(P=0.006),T category (P＜0.001),tumor size(P＜0.001) and ECOG-PS(P=0.001) were significant independent predictors of disease specific survival and they were used to construct a combined molecular and clinical prognostic model.(3) The constructed nomogram combined the clinical and molecular factors and approached the concordance index of 0.852,0.883 and 0.901 for OS, CSS and RFS,respectively.The C-index of the model was significantly higher than that of previous models.
Conclusions:A nomogram consisting of 6 predictors(VEGF,p27,p53,T category, tumor size and ECOG-PS) was constructed based on the Chinese population.To our knowledge,this is the first prognostic model in Chinese RCC patients.The prognostic ability of the nomogram may be superior to clinical factors alone and even previous models. However,independent extemal validation of the nomogram is required.
In our research,we validated the prognostic models for RCC based on the population other than Caucasian for the first time.Furthermore,we also evaluated individual candidate molecular markers for prognostic information.Based on the above research,we finally proposed the first prognostic model in Chinese population.By using the model,we could precisely predict the prognosis of RCC patients.With validation in independent patient samples,the model will be a useful tool for patient counseling,clinical trial design and patient follow-up planning.
引文
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33. Klatte T, Seligson DB, Leppert JT, et al. The chemokine receptor CXCR3 is an independent prognostic factor in patients with localized clear cell renal cell carcinoma. J Urol 2008. 179(1): 61-6.
34. Struckmann K, Mertz K, Steu S, et al. pVHL co-ordinately regulates CXCR4/CXCL12 and MMP2/MMP9 expression in human clear-cell renal cell carcinoma. J Pathol 2008. 214(4): 464-71.
35. Hemmerlein B, Johanns U, Halbfass J, et al. The balance between MMP-2/-9 and TIMP-1/-2 is shifted towards MMP in renal cell carcinomas and can be further disturbed by hydrogen peroxide. Int J Oncol 2004. 24(5): 1069-76.
36. Rioux-Leclercq N, Turlin B, Bansard J, et al. Value of immunohistochemical Ki-67 and p53 determinations as predictive factors of outcome in renal cell carcinoma. Urology 2000. 55(4): 501-5.
37. Heuschmid M, Hofmockel G, Dexler B, Dammrich J, Bassukas ID. Different antigen unmasking techniques lead to significant differences in immunohistochemical staining of Ki-67 (Mib-1) in renal cell carcinomas. Oncol Rep 2002. 9(1): 19-22.
38. Visapaa H, Bui M, Huang Y, et al. Correlation of Ki-67 and gelsolin expression to clinical outcome in renal clear cell carcinoma. Urology 2003. 61(4): 845-50.
39. Went P, Dirnhofer S, Salvisberg T, et al. Expression of epithelial cell adhesion molecule (EpCam) in renal epithelial tumors. Am J Surg Pathol 2005. 29(1): 83-8.
40. Miyata Y, Koga S, Kanda S, Nishikido M, Hayashi T, Kanetake H. Expression of cyclooxygenase-2 in renal cell carcinoma: correlation with tumor cell proliferation, apoptosis, angiogenesis, expression of matrix metalloproteinase-2, and survival. Clin Cancer Res 2003. 9(5): 1741-9.
41. Khoury JD, Abrahams NA, Levin HS, MacLennan GT. The utility of epithelial membrane antigen and vimentin in the diagnosis of chromophobe renal cell carcinoma. Ann Diagn Pathol 2002. 6(3): 154-8.
42. Parker AS, Kosari F, Lohse CM, et al. High expression levels of survivin protein independently predict a poor outcome for patients who undergo surgery for clear cell renal cell carcinoma. Cancer 2006. 107(1): 37-45.
43. Yaycioglu O, Roberts WW, Chan T, Epstein JI, Marshall FF, Kavoussi LR. Prognostic assessment of nonmetastatic renal cell carcinoma: a clinically based model. Urology 2001.58(2): 141-5.
44. Cindolo L, de la Taille A, Messina G, et al. A preoperative clinical prognostic model for non-metastatic renal cell carcinoma. BJU Int 2003. 92(9): 901-5.
45. Zisman A, Pantuck AJ, Dorey F, et al. Improved prognostication of renal cell carcinoma using an integrated staging system. J Clin Oncol 2001. 19(6): 1649-57.
46. Frank I, Blute ML, Cheville JC, Lohse CM, Weaver AL, Zincke H. An outcome prediction model for patients with clear cell renal cell carcinoma treated with radical nephrectomy based on tumor stage, size, grade and necrosis: the SSIGN score. J Urol 2002. 168(6): 2395-400.
47. Kattan MW, Reuter V, Motzer RJ, Katz J, Russo P. A postoperative prognostic nomogram for renal cell carcinoma. J Urol 2001. 166(1): 63-7.
48. Karakiewicz PI, Briganti A, Chun FK, et al. Multi-institutional validation of a new renal cancer-specific survival nomogram. J Clin Oncol 2007. 25(11): 1316-22.
49. Kim HL, Seligson D, Liu X, et al. Using protein expressions to predict survival in clear cell renal carcinoma. Clin Cancer Res 2004. 10(16): 5464-71.
50. Kim HL, Seligson D, Liu X, et al. Using tumor markers to predict the survival of patients with metastatic renal cell carcinoma. J Urol 2005. 173(5): 1496-501.
51. Klatte T, Seligson DB, Larochelle J, et al. Molecular Signatures of Localized Clear Cell Renal Cell Carcinoma to Predict Disease-Free Survival after Nephrectomy. Cancer Epidemiol Biomarkers Prev 2009 .
52. Kovacs G, Akhtar M, Beckwith BJ, et al. The Heidelberg classification of renal cell tumours. J Pathol 1997. 183(2): 131-3.
53. Union Internationale Contre le Cancer (UICC) and the American Joint Committee on Cancer (AJCC). Workshop on Diagnosis and Prognosis of Renal Cell Carcinoma. Rochester, Minnesota, March 21-22, 1997. Cancer 1997. 80(5): 973-1000.
54. Montironi R, Santinelli A, Pomante R, et al. Morphometric index of adult renal cell carcinoma. Comparison with the Fuhrman grading system. Virchows Arch 2000. 437(1): 82-9.
55. Karakiewicz PI, Trinh QD, de la Taille A, et al. ECOG performance status 0 or 1 and symptom classification do not improve the ability to predict renal cell carcinoma-specific survival. Eur J Cancer 2007.43(6): 1023-9.
56. Pencina MJ, D'Agostino RB. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med 2004. 23(13): 2109-23.
57. Cindolo L, Patard JJ, Chiodini P, et al. Comparison of predictive accuracy of four prognostic models for nonmetastatic renal cell carcinoma after nephrectomy: a multicenter European study. Cancer 2005. 104(7): 1362-71.
58. Zigeuner R, Hutterer G, Chromecki T, et al. External Validation of the Mayo Clinic Stage, Size, Grade, and Necrosis (SSIGN) Score for Clear-Cell Renal Cell Carcinoma in a Single European Centre Applying Routine Pathology. Eur Urol 2008 .
59. Thompson RH, Leibovich BC, Lohse CM, et al. Dynamic outcome prediction in patients with clear cell renal cell carcinoma treated with radical nephrectomy: the D-SSIGN score. J Urol 2007. 177(2): 477-80.
60. Ficarra V, Novara G, Galfano A, et al. The 'Stage, Size, Grade and Necrosis' score is more accurate than the University of California Los Angeles Integrated Staging System for predicting cancer-specific survival in patients with clear cell renal cell carcinoma. BJU Int 2009. 103(2): 165-70.
61. Cindolo L, Chiodini P, Gallo C, et al. Validation by calibration of the UCLA integrated staging system prognostic model for nonmetastatic renal cell carcinoma after nephrectomy. Cancer 2008. 113(1): 65-71.
62. Hupertan V, Roupret M, Poisson JF, et al. Low predictive accuracy of the Kattan postoperative nomogram for renal cell carcinoma recurrence in a population of French patients. Cancer 2006. 107(11): 2604-8.
63. Leibovich BC, Blute ML, Cheville JC, et al. Prediction of progression after radical nephrectomy for patients with clear cell renal cell carcinoma: a stratification tool for prospective clinical trials. Cancer 2003. 97(7): 1663-71.
64. Higgins JP, Shinghal R, Gill H, et al. Gene expression patterns in renal cell carcinoma assessed by complementary DNA microarray. Am J Pathol 2003.162(3): 925-32.
65. Evans SM, Judy KD, Dunphy I, et al. Hypoxia is important in the biology and aggression of human glial brain tumors. Clin Cancer Res 2004. 10(24): 8177-84.
66. Schaid DJ, Rowland CM. Robust transmission regression models for linkage and association. Genet Epidemiol 2000. 19 Suppl 1: S78-84.
67. Galfano A, Novara G, Iafrate M, et al. Mathematical models for prognostic prediction in patients with renal cell carcinoma. Urol Int 2008. 80(2): 113-23.
68. Benigni R. A bootstrap analysis of four in vitro short-term test performances. Mutat Res 1989. 216(2): 127-35.
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23. Kim HL, Seligson D, Liu X, et al. Using tumor markers to predict the survival of patients with metastatic renal cell carcinoma. J Urol 2005.173(5): 1496-501.
24. Dirim A, Haberal AN, Goren MR, et al. VEGF, COX-2, and PCNA expression in renal cell carcinoma subtypes and their prognostic value. Int Urol Nephrol 2008. 40(4): 861-8.
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27. Hedberg Y, Ljungberg B, Roos G, Landberg G. Expression of cyclin D1, D3, E, and p27 in human renal cell carcinoma analysed by tissue microarray. Br J Cancer 2003. 88(9): 1417-23.
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29. Liu Z, Fu Q, Lv J, Wang F, Ding K. Prognostic implication of p27Kip1, Skp2 and Cks1 expression in renal cell carcinoma: a tissue microarray study. J Exp Clin Cancer Res 2008.27:51.
30. Shvarts O, Seligson D, Lam J, et al. p53 is an independent predictor of tumor recurrence and progression after nephrectomy in patients with localized renal cell carcinoma. J Urol 2005. 173(3): 725-8.
31. Cho DS, Joo HJ, Oh DK, et al. Cyclooxygenase-2 and p53 expression as prognostic indicators in conventional renal cell carcinoma. Yonsei Med J 2005. 46(1): 133-40.
32. Shin LJ, Seok KH, Bok KY, Cheol LM, Soo PC. Expression of PTEN in renal cell carcinoma and its relation to tumor behavior and growth. J Surg Oncol 2003. 84(3): 166-72.
33. Klatte T, Seligson DB, Leppert JT, et al. The chemokine receptor CXCR3 is an independent prognostic factor in patients with localized clear cell renal cell carcinoma. J Urol 2008. 179(1): 61-6.
34. Struckmann K, Mertz K, Steu S, et al. pVHL co-ordinately regulates CXCR4/CXCL12 and MMP2/MMP9 expression in human clear-cell renal cell carcinoma. J Pathol 2008. 214(4): 464-71.
35. Hemmerlein B, Johanns U, Halbfass J, et al. The balance between MMP-2/-9 and TIMP-1/-2 is shifted towards MMP in renal cell carcinomas and can be further disturbed by hydrogen peroxide. Int J Oncol 2004. 24(5): 1069-76.
36. Rioux-Leclercq N, Turlin B, Bansard J, et al. Value of immunohistochemical Ki-67 and p53 determinations as predictive factors of outcome in renal cell carcinoma. Urology 2000. 55(4): 501-5.
37. Heuschmid M, Hofmockel G, Dexler B, Dammrich J, Bassukas ID. Different antigen unmasking techniques lead to significant differences in immunohistochemical staining of Ki-67 (Mib-1) in renal cell carcinomas. Oncol Rep 2002. 9(1): 19-22.
38. Visapaa H, Bui M, Huang Y, et al. Correlation of Ki-67 and gelsolin expression to clinical outcome in renal clear cell carcinoma. Urology 2003. 61(4): 845-50.
39. Went P, Dirnhofer S, Salvisberg T, et al. Expression of epithelial cell adhesion molecule (EpCam) in renal epithelial tumors. Am J Surg Pathol 2005. 29(1): 83-8.
40. Miyata Y, Koga S, Kanda S, Nishikido M, Hayashi T, Kanetake H. Expression of cyclooxygenase-2 in renal cell carcinoma: correlation with tumor cell proliferation, apoptosis, angiogenesis, expression of matrix metalloproteinase-2, and survival. Clin Cancer Res 2003. 9(5): 1741-9.
41. Khoury JD, Abrahams NA, Levin HS, MacLennan GT. The utility of epithelial membrane antigen and vimentin in the diagnosis of chromophobe renal cell carcinoma. Ann Diagn Pathol 2002. 6(3): 154-8.
42. Parker AS, Kosari F, Lohse CM, et al. High expression levels of survivin protein independently predict a poor outcome for patients who undergo surgery for clear cell renal cell carcinoma. Cancer 2006. 107(1): 37-45.
43. Yaycioglu O, Roberts WW, Chan T, Epstein JI, Marshall FF, Kavoussi LR. Prognostic assessment of nonmetastatic renal cell carcinoma: a clinically based model. Urology 2001.58(2): 141-5.
44. Cindolo L, de la Taille A, Messina G, et al. A preoperative clinical prognostic model for non-metastatic renal cell carcinoma. BJU Int 2003. 92(9): 901-5.
45. Zisman A, Pantuck AJ, Dorey F, et al. Improved prognostication of renal cell carcinoma using an integrated staging system. J Clin Oncol 2001. 19(6): 1649-57.
46. Frank I, Blute ML, Cheville JC, Lohse CM, Weaver AL, Zincke H. An outcome prediction model for patients with clear cell renal cell carcinoma treated with radical nephrectomy based on tumor stage, size, grade and necrosis: the SSIGN score. J Urol 2002. 168(6): 2395-400.
47. Kattan MW, Reuter V, Motzer RJ, Katz J, Russo P. A postoperative prognostic nomogram for renal cell carcinoma. J Urol 2001. 166(1): 63-7.
48. Karakiewicz PI, Briganti A, Chun FK, et al. Multi-institutional validation of a new renal cancer-specific survival nomogram. J Clin Oncol 2007. 25(11): 1316-22.
49. Kim HL, Seligson D, Liu X, et al. Using protein expressions to predict survival in clear cell renal carcinoma. Clin Cancer Res 2004. 10(16): 5464-71.
50. Kim HL, Seligson D, Liu X, et al. Using tumor markers to predict the survival of patients with metastatic renal cell carcinoma. J Urol 2005. 173(5): 1496-501.
51. Klatte T, Seligson DB, Larochelle J, et al. Molecular Signatures of Localized Clear Cell Renal Cell Carcinoma to Predict Disease-Free Survival after Nephrectomy. Cancer Epidemiol Biomarkers Prev 2009 .
52. Kovacs G, Akhtar M, Beckwith BJ, et al. The Heidelberg classification of renal cell tumours. J Pathol 1997. 183(2): 131-3.
53. Union Internationale Contre le Cancer (UICC) and the American Joint Committee on Cancer (AJCC). Workshop on Diagnosis and Prognosis of Renal Cell Carcinoma. Rochester, Minnesota, March 21-22, 1997. Cancer 1997. 80(5): 973-1000.
54. Montironi R, Santinelli A, Pomante R, et al. Morphometric index of adult renal cell carcinoma. Comparison with the Fuhrman grading system. Virchows Arch 2000. 437(1): 82-9.
55. Karakiewicz PI, Trinh QD, de la Taille A, et al. ECOG performance status 0 or 1 and symptom classification do not improve the ability to predict renal cell carcinoma-specific survival. Eur J Cancer 2007.43(6): 1023-9.
56. Pencina MJ, D'Agostino RB. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med 2004. 23(13): 2109-23.
57. Cindolo L, Patard JJ, Chiodini P, et al. Comparison of predictive accuracy of four prognostic models for nonmetastatic renal cell carcinoma after nephrectomy: a multicenter European study. Cancer 2005. 104(7): 1362-71.
58. Zigeuner R, Hutterer G, Chromecki T, et al. External Validation of the Mayo Clinic Stage, Size, Grade, and Necrosis (SSIGN) Score for Clear-Cell Renal Cell Carcinoma in a Single European Centre Applying Routine Pathology. Eur Urol 2008 .
59. Thompson RH, Leibovich BC, Lohse CM, et al. Dynamic outcome prediction in patients with clear cell renal cell carcinoma treated with radical nephrectomy: the D-SSIGN score. J Urol 2007. 177(2): 477-80.
60. Ficarra V, Novara G, Galfano A, et al. The 'Stage, Size, Grade and Necrosis' score is more accurate than the University of California Los Angeles Integrated Staging System for predicting cancer-specific survival in patients with clear cell renal cell carcinoma. BJU Int 2009. 103(2): 165-70.
61. Cindolo L, Chiodini P, Gallo C, et al. Validation by calibration of the UCLA integrated staging system prognostic model for nonmetastatic renal cell carcinoma after nephrectomy. Cancer 2008. 113(1): 65-71.
62. Hupertan V, Roupret M, Poisson JF, et al. Low predictive accuracy of the Kattan postoperative nomogram for renal cell carcinoma recurrence in a population of French patients. Cancer 2006. 107(11): 2604-8.
63. Leibovich BC, Blute ML, Cheville JC, et al. Prediction of progression after radical nephrectomy for patients with clear cell renal cell carcinoma: a stratification tool for prospective clinical trials. Cancer 2003. 97(7): 1663-71.
64. Higgins JP, Shinghal R, Gill H, et al. Gene expression patterns in renal cell carcinoma assessed by complementary DNA microarray. Am J Pathol 2003.162(3): 925-32.
65. Evans SM, Judy KD, Dunphy I, et al. Hypoxia is important in the biology and aggression of human glial brain tumors. Clin Cancer Res 2004. 10(24): 8177-84.
66. Schaid DJ, Rowland CM. Robust transmission regression models for linkage and association. Genet Epidemiol 2000. 19 Suppl 1: S78-84.
67. Galfano A, Novara G, Iafrate M, et al. Mathematical models for prognostic prediction in patients with renal cell carcinoma. Urol Int 2008. 80(2): 113-23.
68. Benigni R. A bootstrap analysis of four in vitro short-term test performances. Mutat Res 1989. 216(2): 127-35.