Negative lymph nodes number and lymph node ratio as independent prognostic factors for triple-negative breast cancer outcome - Hospital based cohort study

Background: Triple negative breast cancer (TNBC) is an aggressive subtype, 
though; many researchers are deeply investigating it. One of the important 
prognostic factors is lymph nodal status. Old studies have shown the impact of 
number of positive lymph node (PLN) on disease survival. But, lymph node 
ratio (LNR) and the number of negative lymph nodes (NLNs) were newer 
factors to be included in trials. Our aim to evaluate them as independent 
prognostication tool on DFS & overall survival (OS) in TNBC patients. 
Material and Methods: There were 92 female patients with TNBC, aged 18 
and above, underwent surgery with axillary dissection from 2014 to 2019, then 
was followed up on until December 2021. LNR is calculated by dividing the 
number of PLN by the total number of lymph nodes resected. For descriptive 
statistics, SPSS was utilized. To guarantee that the discretized variables can 
predict survival, the Weka software was used to discretize cut off values for 
LNR, NLNs, and a multinomial logistic regression.
Results: The mean age of our 92 patients was 49.45± SD 11.75. The median 
number of total LN was 12.3 ±SD 3.689, while, the median number of the PLNs 
was 5±SD 4.751. A significantly positive correlation found between DFS & 
NLNs (P = 0.001) while it was significantly negative between DFS & LNR 
(P=0.002). By Weka tool, the best LNR cut-off value was 0.19, while, NLNs 
was 9. Though, Patients having LNR ≤ 0.19 and or NLNs > 9 had longer DFS 
(>24m) 52% & 58% respectively compared to those with >0.19 or ≤ 9. For 
instance, A multinomial regression test confirmed that the odds of a patients 
with NLNs ≤ 9 to have lower DFS (≤12 m) instead of >24 m by 22 times with 
(P= 0.004). Regarding OS, NLNs & LNR significantly affecting it (p = 0.006, 
0.032) respectively. 
Conclusion: It was clear that LNR and NLNs had a significant effect on 
survival, which, can be used as a part of prognostic tools. Further multi-centric 
studies needed to verify the standardization of those cut-off values.