Journal of surgical oncology · 2019
A nomogram for predicting recurrence after complete resection for thymic epithelial tumors based on the TNM classification: A multi-institutional retrospective analysis.
Сравнительное исследованиеСтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод пока не подготовлен
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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, other members of the Korean Association for Research on the Thymus (KART).
Аннотация
OBJECTIVES: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.