The Value of Sentinel Lymph Node Mapping for the Staging of Node-Negative Colon Cancer: Propensity Score and Mediation Analyses

ObjectivesMediation analysis to assess the protective impact of sentinel lymph node (SLN) mapping on prognosis and survival of patients with colon cancer through a more precise evaluation of the lymph node (LN) status. BackgroundUp to 20% of patients with node-negative colon cancer develop disease recurrence. Conventional histopathological LN examination may be limited in describing the real metastatic burden of LN. MethodsData of 312 patients with stage I & II colon cancer was collected prospectively. Patients were either staged using intraoperative SLN mapping with multilevel sectioning and immunohistochemical staining of the SLN or conventional techniques. The value of the SLN mapping for the detection of truly node-negative patients was assessed using Cox regression and mediation analysis. ResultsSLN mapping was performed in 143 patients. Disease recurrence was observed in 13 (9.1%) patients staged with SLN mapping and in 27 (16%) staged conventionally. Five-year overall survival (OS) rate was 82.7% (95% confidence interval [CI], 76.5-89.4%) with SLN mapping compared with 65.8% (95% CI, 58.8-73.7%). Five-year cancer-specific survival (CSS) was 95.1% (95% CI, 91.3-99.0%) with SLN mapping compared with 92.5% (95% CI, 88.0-97.2%). Node-negative staging with SLN mapping was associated with significantly better OS (hazard ratio [HR], 0.64; 95% CI, 0.56-0.72; P < 0.001) and CSS (HR, 0.49; 95% CI, 0.39-0.61; P < 0.001) in multivariate analysis. Mediation analysis confirmed a direct protective effect of SLN mapping on OS (HR, 0.78; 95% CI, 0.52-0.96; P < 0.01) and disease-free survival (DFS) (HR, 0.75; 95% CI, 0.48-0.89; P < 0.01). ConclusionsStaging performed by SLN mapping with multilevel sectioning provides more accurate results than conventional staging. The observed clinically relevant and statistically significant benefit in OS and DFS is explained by a more accurate detection of positive LN by SLN mapping.