Yun Jae Kwang, Lee Geun Dong, Kim Hyeong Ryul, Kim Dong Kwan, Zo Jae Il, Shim Young Mog, Kang Chang Hyun, Kim Young Tae, Paik Hyo Chae, Chung Kyoung Young
Department of Thoracic and Cardiovascular Surgery, Asan Medical Center, Ulsan University College of Medicine, Seoul, Republic of Korea.
Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
J Surg Oncol. 2019 Jun;119(8):1161-1169. doi: 10.1002/jso.25462. Epub 2019 Mar 28.
This study aimed to compare the predictive ability between the Masaoka-Koga (M-K) staging system and the 8th TNM staging system for the recurrence of thymic epithelial tumors (TETs). In addition, a nomogram was developed on the basis of the proposed TNM classification to predict individual recurrence rate.
A retrospective study was performed on 445 patients who underwent complete resection (R0) of TETs between January 2000 and February 2013. Concordance index (C-index) was used as a statistical indicator to quantify the prediction power of the prediction models.
In multivariate analysis, tumor stage and WHO classification were independent recurrence factors in a predictive model on the basis of M-K and TNM stage. The TNM model showed higher C-index than the M-K model (0.837 vs 0.817). The nomogram, on the basis of the TNM model, revealed a highly predictive performance, with a bootstrap-corrected C-index of 0.85 (95% CI, 0.76 to 0.93).
A predictive model based on the 8th TNM stage was slightly better than that based on M-K stage with respect to recurrence after R0 of TETs. The proposed nomogram could be applied to estimate the individual recurrence rate and make decisions for proper surveillance.
本研究旨在比较Masaoka-Koga(M-K)分期系统和第8版TNM分期系统对胸腺上皮肿瘤(TETs)复发的预测能力。此外,在提出的TNM分类基础上开发了一种列线图,以预测个体复发率。
对2000年1月至2013年2月期间接受TETs完整切除(R0)的445例患者进行回顾性研究。一致性指数(C指数)用作统计指标,以量化预测模型的预测能力。
在多变量分析中,肿瘤分期和世界卫生组织分类是基于M-K和TNM分期的预测模型中的独立复发因素。TNM模型的C指数高于M-K模型(0.837对0.817)。基于TNM模型的列线图显示出高度的预测性能,自展校正后的C指数为0.85(95%CI,0.76至0.93)。
对于TETs的R0术后复发,基于第8版TNM分期的预测模型略优于基于M-K分期的预测模型。所提出的列线图可用于估计个体复发率并为适当的监测做出决策。