Comprehensive analysis of prognostic value of lymph node classifications in esophageal squamous cell carcinoma: a
Junmiao Wen1, Jiayan Chen1, Donglai Chen2
1Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
Background:
We aim to assess the prognostic ability of three common lymph node-based staging algorithms, namely, the number of positive lymph nodes (pN), the lymph node ratio (LNR), and log odds of positive lymph nodes (LODDS) in patients with esophageal squamous cell carcinoma (ESCC).
Methods:
A total of 3902 ESCC patients treated at 10 Chinese institutions between 2003 and 2013 were included, along with 2465 patients from the Surveillance, Epidemiology, and End Results (SEER) database. The prognostic ability of the aforementioned algorithms was evaluated using time-dependent receiver operating characteristic (tdROC) curves, R 2, Harrell's concordance index (C-index), and the likelihood ratio chi-square score. The primary outcomes included cancer-specific survival (CSS), overall survival (OS), and CSS with a competing risk of death by non-ESCC causes.
Results:
LODDS had better prognostic performance than pN or LNR in both continuous and stratified patterns. In the multicenter cohort, the multivariate analysis showed that the model based on LODDS classification was superior to the others in predictive accuracy and discriminatory capacity. Two nomograms integrating LODDS classification and other clinicopathological risk factors associated with OS as well as cancer-specific mortality were constructed and validated in the SEER database. Finally, a novel TNLODDS classification which incorporates the LODDS classification was built and categorized patients in to three new stages.
Conclusion:
Among the three lymph node-based staging algorithms, LODDS demonstrated the highest discriminative capacity and prognostic accuracy for ESCC patients. The nomograms and novel TNLODDS classification based on LODDS classification could serve as precise evaluation tools to assist clinicians in estimating the survival time of individual patients and improving clinical outcomes postoperatively in the future.
