Suppr超能文献

膀胱尿路上皮癌淋巴结转移预测模型的外部验证

External Validation of Models for Prediction of Lymph Node Metastasis in Urothelial Carcinoma of the Bladder.

作者信息

Ku Ja Hyeon, Kim Myong, Byun Seok-Soo, Jeong Hyeon, Kwak Cheol, Kim Hyeon Hoe, Lee Sang Eun

机构信息

Department of Urology, Seoul National University Hospital, Seoul, Korea.

Department of Urology, Seoul National University Bundang Hospital, Seongnam, Korea.

出版信息

PLoS One. 2015 Oct 1;10(10):e0120552. doi: 10.1371/journal.pone.0120552. eCollection 2015.

Abstract

PURPOSE

To externally validate models to predict LN metastsis; Karakiewicz nomogram, clinical nodal staging score (cNSS), and pathologic nodal staging score (pNSS) using a different cohort.

MATERIALS AND METHODS

Clinicopathologic data from 500 patients who underwent radical cystectomy and pelvic lymphadenectomy were analyzed. The overall predictive values of models were compared with the criteria of overall performance, discrimination, calibration, and clinical usefulness.

RESULTS

Presence of pN+ stages was recorded in 117 patients (23.4%). Agreement between clinical and pathologic stage was noted in 174 (34.8%). Based on Nagelkerke's peudo-R2 and brier score, pNSS demonstrated best overall performance. Area under the receiver operating characteristics curve, showed that pNSS had the best discriminatory ability. In all models, calibration was on average correct (calibration-in-the-large coefficient = zero). On decision curve analysis, pNSS performed better than other models across a wide range of threshold probabilities.

CONCLUSIONS

When compared to pNSS, current precystectomy models such as the Karakiewicz nomogram and cNSS cannot predict the probability of LN metastases accurately. The findings suggest that the application of pNSS to Asian patients is feasible.

摘要

目的

使用不同队列对外验证预测淋巴结转移的模型,即卡拉基维茨列线图、临床淋巴结分期评分(cNSS)和病理淋巴结分期评分(pNSS)。

材料与方法

分析了500例行根治性膀胱切除术和盆腔淋巴结清扫术患者的临床病理数据。将模型的总体预测值与总体性能、区分度、校准度和临床实用性标准进行比较。

结果

117例患者(23.4%)记录有病理淋巴结阳性(pN+)分期。174例(34.8%)患者临床分期与病理分期一致。基于纳格尔克伪R²和布里尔评分,pNSS表现出最佳的总体性能。受试者操作特征曲线下面积显示,pNSS具有最佳的区分能力。在所有模型中,校准平均正确(总体校准系数=零)。决策曲线分析表明,在广泛的阈值概率范围内,pNSS的表现优于其他模型。

结论

与pNSS相比,当前的术前模型,如卡拉基维茨列线图和cNSS,不能准确预测淋巴结转移的概率。研究结果表明,pNSS在亚洲患者中的应用是可行的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad2c/4591286/4c1b99962740/pone.0120552.g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验