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构建包含 2004 年 WHO/ISUP 分级的列线图预测模型用于预测非肌层浸润性膀胱尿路上皮肿瘤患者的预后。

Constructing prognostic model incorporating the 2004 WHO/ISUP classification for patients with non-muscle-invasive urothelial tumours of the urinary bladder.

机构信息

Department of Pathology, Taipei Veterans General Hospital & National Yang-Ming University, Taipei, Taiwan.

出版信息

J Clin Pathol. 2010 Oct;63(10):910-5. doi: 10.1136/jcp.2010.079764.

Abstract

AIM

To construct a prognostic model for recurrence-free survival (RFS), progression-free survival (PFS) and cancer-specific survival (CSS) for patients who have undergone transurethral resection of non-muscle-invasive (pTa/pT1) urinary bladder urothelial tumours.

METHODS

1366 patients who had undergone transurethral resection of primary non-muscle-invasive urothelial tumours (pTa, 891 patients; pT1, 475 patients) confined to the bladder were retrospectively studied. Tumours were classified according to the 2004 WHO/International Society of Urologic Pathology grading system. Kaplan-Meier and stepwise Cox regression models were applied, and 200 bootstrap resamples were used to generate survival estimates and 95% CIs. A nomogram was developed that incorporated significant variables predicting survival.

RESULTS

RFS, PFS and CSS probabilities for non-muscle-invasive bladder urothelial tumours were calculated. Incorporating salient prognostic factors (tumour grade, pT stage, patient age, status of intravesical instillation), the model satisfactorily predicted PFS (concordance index=0.79) and CSS (concordance index=0.87).

CONCLUSIONS

Robust nomograms were created to predict PFS and CSS. These data provide an overall perspective of disease outcomes which may aid in developing individualised follow-up programmes.

摘要

目的

为接受经尿道非肌肉浸润性(pTa/pT1)膀胱尿路上皮肿瘤切除术的患者构建无复发生存(RFS)、无进展生存(PFS)和癌症特异性生存(CSS)的预后模型。

方法

回顾性研究了 1366 例接受经尿道切除原发性非肌肉浸润性尿路上皮肿瘤(pTa,891 例;pT1,475 例)的患者。肿瘤根据 2004 年世界卫生组织/国际泌尿病理学会分级系统进行分类。应用 Kaplan-Meier 和逐步 Cox 回归模型,并使用 200 次 bootstrap 重采样生成生存估计值和 95%CI。开发了一个包含预测生存的显著变量的列线图。

结果

计算了非肌肉浸润性膀胱尿路上皮肿瘤的 RFS、PFS 和 CSS 概率。纳入显著的预后因素(肿瘤分级、pT 分期、患者年龄、膀胱内灌注状态)后,该模型可满意地预测 PFS(一致性指数=0.79)和 CSS(一致性指数=0.87)。

结论

创建了稳健的列线图来预测 PFS 和 CSS。这些数据提供了疾病结局的整体视角,可能有助于制定个体化的随访计划。

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