Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Aug 28;21(1):965.
doi: 10.1186/s12885-021-08642-6.

Nomograms predict survival of patients with lymph node-positive, luminal a breast cancer

Yilun Li  1 , Li Ma  2
Affiliations

Nomograms predict survival of patients with lymph node-positive, luminal a breast cancer

Yilun Li et al. BMC Cancer. .

Abstract

Background: To develop nomograms for the prediction of the 1-, 3-, and 5-year overall survival (OS) and breast cancer-specific survival (BCSS) for patients with lymph node positive, luminal A breast cancer.

Methods: Thirty-nine thousand fifty-one patients from The Surveillance, Epidemiology, and End Results (SEER) database were included in our study and were set into a training group (n = 19,526) and a validation group (n = 19,525). Univariate analysis and Cox proportional hazards analysis were used to select variables and set up nomogram models on the basis of the training group. Kaplan-Meier curves and the log-rank test were adopted in the survival analysis and curves plotting. C-index, calibration plots and ROC curves were used to performed internal and external validation on the training group and validation group.

Results: Following independent factors were included in our nomograms: Age, marital status, grade, ethnic group, T stage, positive lymph nodes numbers, Metastasis, surgery, radiotherapy, chemotherapy. In both the training group and testing group, the calibration plots show that the actual and nomogram-predicted survival probabilities are consistent greatly. The C-index values of the nomograms in the training and validation cohorts were 0.782 and 0.806 for OS and 0.783 and 0.804 for BCSS, respectively. The ROC curves show that our nomograms have good discrimination.

Conclusions: The nomograms may assist clinicians predict the 1-, 3-, and 5-year OS and BCSS of patients with lymph node positive, luminal A breast cancer.

Keywords: Luminal A; Lymph node-positive; Nomograms; Prognosis.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Flow diagram showing steps involved in construction and validation of nomograms
Fig. 2
Fig. 2
Nomograms for predicting 1-, 3-, and 5-year OS (A) and BCSS (B) for patients with the indicated prognosis factors. Summing up points from all predictors could obtain total points. The predicted probabilities of OS and BCSS can be obtained by projecting the location of the total points to the bottom scales. NO. nodes: number of positive lymph nodes; OS, overall survival; BCSS, breast cancer-specific survival
Fig. 3
Fig. 3
Calibration plots for the 1-, 3-, and 5-year. (A, B, C) Internal calibration curves for OS; (D,E,F) external calibration curves for OS; (G, H, I) internal calibration curves for BCSS; (J, K, L) external calibration curves for BCSS. OS, overall survival; BCSS, breast cancer-specific survival
Fig. 4
Fig. 4
ROC curves for the 1-, 3-, and 5-year. (A) Internal calibration plots for OS; (B) external calibration curves for OS;(C) internal calibration plots for BCSS; (D) external calibration plots for BCSS. OS, overall survival; BCSS, breast cancer-specific survival
Fig. 5
Fig. 5
Kaplan-Meier curves of OS and BCSS for each predictor. (A, B) age; (C, D) race; (E, F) marital status; (G, H) T stage; (I, J) number of positive lymph nodes; (K, L) Bone metastasis; (M, N) liver metastasis; (O, P) brain metastasis; (Q, R) tumor grade; (S, T) Radiotherapy; (U, V) Chemotherapy; (W, X) Surgery
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA: a cancer journal for clinicians. 2019;69(1):7–34. doi: 10.3322/caac.21551. - DOI - PubMed
    1. Kosvyra A, Maramis C, Chouvarda I. Developing an Integrated Genomic Profile for Cancer Patients with the Use of NGS Data. Emerg Sci J. 2019;3(3):157–167. doi: 10.28991/esj-2019-01178. - DOI
    1. Callagy G, Cattaneo E, Daigo Y, Happerfield L, Bobrow LG, Pharoah PD, Caldas C. Molecular classification of breast carcinomas using tissue microarrays. Diagnostic Molecular Pathology. 2003;12(1):27–34. doi: 10.1097/00019606-200303000-00004. - DOI - PubMed
    1. Goldhirsch A, Wood WC, Coates AS, Gelber RD, Thürlimann B, Senn HJ. Strategies for subtypes--dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncology. 2011;22(8):1736–1747. doi: 10.1093/annonc/mdr304. - DOI - PMC - PubMed
    1. Sørlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS et al: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proceedings of the National Academy of Sciences of the United States of America 2001, 98(19):10869–10874.10.1073/pnas.191367098 - PMC - PubMed

MeSH terms