IDDF2018-ABS-0197 Comparison of three lymph node staging schemes for predicting survival in patients with colorectal cancer: a large population database and chinese cohort validation

Background Several node staging schemes have been proposed for colorectal cancer. The optimal system remains controversial. This study aims to compare three node staging schemes in predicting survival outcome in patients with colorectal cancer. Methods Patients with colorectal cancer were identified from the Surveillance, Epidemiology, and End Results (SEER) database, and a Chinese patient cohort was used for independent validation. The prognostic performance of three node staging schemes was compared, involving a number-based scheme (pN), ratio-based scheme (rN) and log odds of positive lymph nodes scheme (LODDS). Prediction performance were assessed for overall performance using R2, discriminatory capacity using Harrell’s C statistic, Royston’s D statistic. After LODDS and rN were classified into three groups using the x-tile analysis, prediction capacity of these two classification was also compared with 7th N stage. Results There were 240 898 patients in the SEER database and 1316 in the Chinese cohort. LODDS scheme showed limited advantage in overall performance R2 (LODDS vs. rN vs. pN: 19.4% vs. 17.6% vs. 9.4%) and predictive accuracy with Harrell’s C statistic (LODDS vs. rN vs. pN: 0.727 vs. 0.719 vs. 0.712), Royston’s D statistic (LODDS vs. rN vs. pN: 4.01 vs. 4.13 vs. 3.83) than either pN or rN, for patients with colorectal cancer in SEER database. Results were validated in Chinese cohort, also showing that LODDS scheme still showed a limited advantage in predicting survival outcomes in patients with colorectal cancer (See table 1). Using x-tile method, patients were classified into three groups of LODDS as LODDS1. Conclusions Both LODDS, rN, and pN schemes had the similar discriminatory capacity and predictive accuracy. The LODDS, rN classification could also serve as an important reference for the tumour node metastasis (TNM) node classification.