Nomogram Model of LNR Predicts Survival in Premenopausal Patients with Node-positive Luminal Breast Cancer

Anticancer Res. 2017 Aug;37(8):4575-4586. doi: 10.21873/anticanres.11856.

Abstract

Aim: The aim of this study was to assess the prognostic value of lymph node ratio (LNR) in premenopausal patients with luminal breast carcinoma.

Materials and methods: A total of 885 female patients who presented with axillary lymph node-positive luminal breast cancer between 2000 and 2009 were investigated. Using X-tile, we classified patients into low-, intermediate- and high-risk groups based on LNR. The Kaplan-Meier method was used to determine cumulative survival curves. Cox proportional hazards analyses were used to identify the factors that contributed to disease-free (DFS) and overall (OS) survival.

Results: The median age of patients was 42 years (range=21-58 years). A training set of 295 patients and a validation set of 590 patients were used to determine the optimal LNR cut-off points (0.20 and 0.63). DFS was 87.7%, 77.4% and 53.9% (p<0.001) and OS was 91.5%, 76.7% and 50.9% (p<0.0001) for the low- (≤0.20), intermediate- (0.21-0.63) and high-risk (>0.63) groups, respectively. The 10-year DFS and OS rates were significantly longer in the low-risk group than in the high-risk group. Nomogram analysis demonstrated that LNR contributed more compared to nodal stage in predicting both DFS and OS.

Conclusion: We conclude that LNR strongly predicts prognosis in premenopausal patients with lymph node-positive luminal breast cancer.

Keywords: Lymph node ratio; breast cancer; luminal subtype; nomogram; prognosis.

MeSH terms

  • Adult
  • Biomarkers, Tumor
  • Breast Neoplasms / mortality*
  • Breast Neoplasms / pathology*
  • Breast Neoplasms / therapy
  • Combined Modality Therapy
  • Female
  • Follow-Up Studies
  • Humans
  • Kaplan-Meier Estimate
  • Lymph Nodes / pathology*
  • Lymphatic Metastasis
  • Middle Aged
  • Nomograms
  • Premenopause*
  • Prognosis
  • Retrospective Studies
  • Young Adult

Substances

  • Biomarkers, Tumor