Quantitative Nodal Burden and Mortality Across Solid Cancers. (21st March 2022)

• Record Type: Journal Article
• Authors: Nguyen, Anthony T; Luu, Michael; Nguyen, Vina P; Lu, Diana J; Shiao, Stephen L; Kamrava, Mitchell; Atkins, Katelyn M; Mita, Alain C; Scher, Kevin S; Spratt, Daniel E; Faries, Mark B; Daskivich, Timothy J; Lin, De-Chen; Chen, Michelle M; Clair, Jon Mallen-St; Sandler, Howard M; Ho, Allen S; Zumsteg, Zachary S
• Is Part Of: Journal of the National Cancer Institute. Volume 114:Number 7(2022)
• Page Start: 1003
• Page End: 1011
• Publication Date: 2022-03-21
• Subjects: Cancer -- Periodicals; Cancer -- Research -- Periodicals; 616.994
• DOI: 10.1093/jnci/djac059
• Languages: English
• ISSNs: 0027-8874

Abstract

Abstract: Background: Nodal staging systems vary substantially across solid tumors, implying heterogeneity in the behavior of nodal variables in various contexts. We hypothesized, in contradiction to this, that metastatic lymph node (LN) number is a universal and dominant predictor of outcome across solid tumors. Methods: We performed a retrospective cohort analysis of 1 304 498 patients in the National Cancer Database undergoing surgery between 2004 and 2015 across 16 solid cancer sites. Multivariable Cox regression analyses were constructed using restricted cubic splines to model the association between nodal number and mortality. Recursive partitioning analysis (RPA) was used to derive nodal classification systems for each solid cancer based on metastatic LN count. The reproducibility of these findings was assessed in 1 969 727 patients from the Surveillance, Epidemiology, and End Results registry. Two-sided tests were used for all statistical analyses. Results: Consistently across disease sites, mortality risk increased continuously with increasing number of metastatic LNs ( P < .001 for all spline segments). Each RPA-derived nodal classification system produced multiple prognostic groups spanning a wide spectrum of mortality risk ( P < .001). Multivariable models using these RPA-derived nodal classifications demonstrated improved concordance with mortality compared with models using American Joint Committee on Cancer staging in sites where nodal classification is not based on metastatic LN count. Each RPA-derived nodal classification system was reproducible in a large validation cohort for all-cause and cause-specific mortality ( P < .001). High quantitative nodal burden was the single strongest tumor-intrinsic variable associated with mortality in 12 of 16 disease sites. Conclusions: Quantitative metastatic LN burden is a fundamental driver of mortality across solid cancers and should serve as a foundation for pathologic nodal staging across solid tumors.