Clinicopathological Determinants of Overall Survival in Breast Cancer: A Retrospective Cohort Analysis: Clinicopathological Determinants of Overall Survival in Breast Cancer

Warda Munir; Wajeeha Waqar; Maham Saleem; Memoona Anees

doi:10.54393/pbmj.v9i6.1393

Authors

Warda Munir Department of Public Health, University of the Punjab, Lahore, Pakistan
Wajeeha Waqar Department of Nutrition, Abbot Nutrition International, Lahore, Pakistan
Maham Saleem Department of Human Nutrition and Food Technology, Superior University, Lahore, Pakistan
Memoona Anees Department of Family Medicine, Madera Family Medicine Group, California, United States of America

DOI:

https://doi.org/10.54393/pbmj.v9i6.1393

Keywords:

Breast Neoplasms, Lymph Node Ratio, Prognosis, Survival Analysis, Risk Assessment

Abstract

Breast cancer is the most common cause of cancer mortality in women, and the survival rate depends on a multitude of demographic and tumor-related factors. Personalizing surveillance intensity and the choice of treatment depends on accurate risk stratification. Objectives: To determine the main patient and tumor parameters associated with overall survival (OS) in breast cancer patients by univariate and multivariable Cox proportional hazards analyses. Methods: This is a retrospective cohort study of 1,000 breast cancer patients in the SEER database. Univariate and multivariable Cox regression analyses were used to identify independent predictors of survival. Three predictive models were built and validated on a held-out test set (30% of data): Cox regression, Random Survival Forest (RSF) and XGBoost. Clinicopathological parameters affecting overall survival (OS) were identified, and two machine learning algorithms were developed to create a practical predictive risk model for 5-year mortality. Results: The independent prognostic parameters for survival were lymph node ratio (HR 2.27 per 0.1 increase), advanced stage (Stage IIIC vs. IIA: HR 3.35), tumor size (HR 1.79 per log unit), older age (HR 1.42 per decade), ER negative status (HR 1.67), and high histological grade (HR 1.46). Marital status (and race) was not significant after adjustment. The RSF model showed the best performance (C index = 0.758, AUC ROC = 0.78, calibration slope = 0.98). Conclusions: The most important independent factors in the survival of BC are LNR, stage, tumor size, age, and ER status, whereas demographic factors play a minimal role after these factors have been controlled for.

References

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians. 2021 May; 71(3): 209-49. doi: 10.3322/caac.21660.

Early BC. Relevance of Breast Cancer Hormone Receptors and Other Factors to the Efficacy of Adjuvant Tamoxifen: Patient-Level Meta-Analysis of Randomized Trials. Lancet-London. 2011 Jan 1; 378(9793): 771-84. doi: 10.1016/S0140-6736(11)60993-8.

Byrd DR, Brookland RK, Washington MK, Gershenwald JE, Compton CC, Hess KR et al. AJCC Cancer Staging Manual. New York: Springer. 2017 Jan.

Howlader N, Noone AM, Krapcho M, Miller D, Brest A, Yu M et al. Seer Cancer Statistics Review, 1975-2018. Bethesda, MD: National Cancer Institute, 2021 [Internet]. 2021 Apr.

Shetty AB, Prasad A, Shetty AB. Breast Cancer Subtypes, Tumor Size, and Lymph Node Metastasis: A Retrospective Analysis of 442 Patients. International Journal of Life Sciences, Biotechnology and Pharma Research. 2024 Oct; 13 (10).

Woodward WA, Barlow WE, Jagsi R, Buchholz TA, Shak S, Baehner F et al. Association Between 21-Gene Assay Recurrence Score and Locoregional Recurrence Rates in Patients with Node-Positive Breast Cancer. Journal of the American Medical Association Oncology. 2020 Apr; 6(4): 505-11. doi: 10.1001/jamaoncol.2019.5559.

Acheampong T, Kehm RD, Terry MB, Argov EL, Tehranifar P. Incidence Trends of Breast Cancer Molecular Subtypes by Age and Race/Ethnicity in the US from 2010 to 2016. Journal of the American Medical Association Network Open. 2020 Aug; 3(8): e2013226. doi: 10.1001/jamanetworkopen.2020.13226.

Giaquinto AN, Sung H, Miller KD, Kramer JL, Newman LA, Minihan A et al. Breast Cancer Statistics, 2022. CA: A Cancer Journal for Clinicians. 2022 Nov; 72(6): 524-41. doi: 10.3322/caac.21754.

Giordano SH, Franzoi MA, Temin S, Anders CK, Chandarlapaty S, Crews JR et al. Systemic Therapy for Advanced Human Epidermal Growth Factor Receptor 2–Positive Breast Cancer: ASCO Guideline Update. Journal of Clinical Oncology. 2022 Aug; 40(23): 2612-35. doi: 10.1200/JCO.22.00519.

Lu D, Yan Y, Jiang M, Sun S, Jiang H, Lu Y et al. Predictive Value of Radiomics-Based Machine Learning for the Disease-Free Survival in Breast Cancer: A Systematic Review and Meta-Analysis. Frontiers in Oncology. 2023 Aug; 13: 1173090. doi: 10.3389/fonc.2023.1173090.

Huang Y, Bazzazzadehgan S, Li J, Arabshomali A, Li M, Bhattacharya K et al. Comparison of Machine Learning Methods Versus Traditional Cox Regression for Survival Prediction in Cancer Using Real-World Data: A Systematic Literature Review and Meta-Analysis. BioMed Central Medical Research Methodology. 2025 Oct; 25(1): 243. doi: 10.1186/s12874-025-02694-z.

Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N et al. Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures. Epidemiology. 2010 Jan; 21(1): 128-38. doi: 10.1097/EDE.0b013e3181c30fb2.

Peduzzi P, Concato J, Feinstein AR, Holford TR. Importance of Events Per Independent Variable in Proportional Hazards Regression Analysis II. Accuracy and Precision of Regression Estimates. Journal of Clinical Epidemiology. 1995 Dec; 48(12): 1503-10. doi: 10.1016/0895-4356(95)00048-8.

Park KH, Loibl S, Sohn J, Park YH, Jiang Z, Tadjoedin H et al. Pan-Asian adapted ESMO Clinical Practice Guidelines for the Diagnosis, Treatment and Follow-Up of Patients with Early Breast Cancer. European Society for Medical Oncology Open. 2024 May; 9(5): 102974. doi: 10.1016/j.esmoop.2024.102974.

Das SM, Choudhury KB, Mondal S, Das R. Research Article Prognostic Significance of Lymph Node Ratio in Breast Cancer–A Single Institutional Retrospective Study. Biomedicine. 2024; 44(4): 406-14. doi: 10.51248/v44i4.103.

Liu J, Li Y, Zhang W, Yang C, Yang C, Chen L et al. The Prognostic Role of Lymph Node Ratio in Breast Cancer Patients Received Neoadjuvant Chemotherapy: A Dose-Response Meta-Analysis. Frontiers in Surgery. 2022 Oct; 9: 971030. doi: 10.3389/fsurg.2022.971030.

Hassing CM, Mejdahl MK, Lænkholm AV, Kroman N, Knoop AS, Tvedskov TH. Adjuvant Chemotherapy in Patients with ER-Negative/HER2-Negative, T1abn0 Breast Cancer: A Nationwide Study. Breast Cancer Research and Treatment. 2023 Feb; 198(1): 103-12. doi: 10.1007/s10549-022-06839-2.

Shen A, Wei X, Zhu F, Sun M, Ke S, Qiang W et al. Risk Prediction Models for Breast Cancer-Related Lymphedema: A Systematic Review and Meta-Analysis. European Journal of Oncology Nursing. 2023 Jun; 64: 102326. doi: 10.1016/j.ejon.2023.102326.

Giaquinto AN, Sung H, Newman LA, Freedman RA, Smith RA, Star J et al. Breast Cancer Statistics 2024. CA: A Cancer Journal for Clinicians. 2024 Nov; 74(6): 477-95. doi: 10.3322/caac.21863.

Li LW, Liu X, Shen ML, Zhao MJ, Liu H. Development and Validation of a Random Survival Forest Model for Predicting Long-Term Survival of Early-Stage Young Breast Cancer Patients Based on the SEER Database and an External Validation Cohort. American Journal of Cancer Research. 2024 Apr; 14(4): 1609. doi: 10.62347/OJTY4008.

Ren J, Li Y, Zhou J, Yang T, Jing J, Xiao Q et al. Developing Machine Learning Models for Personalized Treatment Strategies in Early Breast Cancer Patients Undergoing Neoadjuvant Systemic Therapy Based on SEER Database. Scientific Reports. 2024 Sep; 14(1): 22055. doi: 10.1038/s41598-024-72385-0.