Unlocking the complete blood count as a risk stratification tool for breast cancer using machine learning
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1355Keywords:
Blood Cell Count, Machine Learning, Breast CancerAbstract
Objective: To evaluate the efficacy of machine learning (ML) in using complete blood count (CBC) for breast cancer risk assessment. Method: This retrospective study analyzed CBCs from 396,848 women aged 40 to 70. A total of 2861 cases were identified (1882 confirmed by biopsy and 979 by imaging), while 393,987 were controls (BI-RADS 1 or 2). Data were divided into modeling (training and validation) and testing sets based on diagnostic certainty. Results: The ridge regression model, incorporating the neutrophil-to-lymphocyte ratio, red blood cells, and age, achieved an AUC of 0.64. The study population was stratified into four risk groups: high, moderate, medium, and low, with relative ratios of 1.99, 1.32, 1.02, and 0.42, respectively. Conclusion: This ML model provides a cost-effective tool for personalized breast cancer screening, potentially improving early detection in resource-limited settings.
Downloads
References
Coleman C. Early Detection and Screening for Breast Cancer. Semin Oncol Nurs. 2017 May;33(2):141–55.
Araujo DC, Rocha BA, Gomes KB, da Silva DN, Ribeiro VM, Kohara MA, et al. Unlocking the complete blood count as a risk stratification tool for breast cancer using machine learning: a large scale retrospective study. Sci Rep. 2024 May 12;14(1):1–10.
Zhang K, Bangma CH, Venderbos LDF, Roobol MJ. Individual and Population-Based Screening. Management of Prostate Cancer. 2017;43–55.
Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Hered Cancer Clin Pract. 2012;10(Suppl 2):A29.
Yala A, Mikhael PG, Strand F, Lin G, Satuluru S, Kim T, et al. Multi-Institutional Validation of a Mammography-Based Breast Cancer Risk Model. J Clin Oncol [Internet]. 2022 Jun 1 [cited 2024 May 28];40(16). Available from: https://pubmed.ncbi.nlm.nih.gov/34767469/
Danesh H, Ziamajidi N, Mesbah-Namin SA, Nafisi N, Abbasalipourkabir R. Association between Oxidative Stress Parameters and Hematological Indices in Breast Cancer Patients. Int J Breast Cancer [Internet]. 2022 Oct 3 [cited 2024 May 29];2022. Available from: https://pubmed.ncbi.nlm.nih.gov/36225290/
Amin MB, Greene FL, Edge SB, Compton CC, Gershenwald JE, Brookland RK, et al. The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more “personalized” approach to cancer staging. CA Cancer J Clin. 2017 Mar;67(2):93–9.
Hoerl AE, Kennard RW. Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics [Internet]. 1970 Feb 1 [cited 2024 May 29]; Available from: https://www.tandfonline.com/doi/abs/10.1080/00401706.1970.10488634
Ke G, Meng Q, Finely T, Wang T, Chen W, Ma W, et al. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In: Advances in Neural Information Processing Systems 30 (NIP 2017) [Internet]. 2017 [cited 2024 May 30]. Available from: https://www.microsoft.com/en-us/research/publication/lightgbm-a-highly-efficient-gradient-boosting-decision-tree/
Zuin G, Araujo D, Ribeiro V, Seiler MG, Prieto WH, Pintão MC, et al. Prediction of SARS-CoV-2-positivity from million-scale complete blood counts using machine learning. Communications Medicine. 2022 Jun 15;2(1):1–12.
Amador T, Saturnino S, Veloso A, Ziviani N. Early identification of ICU patients at risk of complications: Regularization based on robustness and stability of explanations. Artif Intell Med [Internet]. 2022 Jun [cited 2024 May 30];128. Available from: https://pubmed.ncbi.nlm.nih.gov/35534141/
Michaels E, Worthington RO, Rusiecki J. Breast Cancer: Risk Assessment, Screening, and Primary Prevention. Med Clin North Am [Internet]. 2023 Mar [cited 2024 May 30];107(2). Available from: https://pubmed.ncbi.nlm.nih.gov/36759097/
Ethier JL, Desautels D, Templeton A, Shah PS, Amir E. Prognostic role of neutrophil-to-lymphocyte ratio in breast cancer: a systematic review and meta-analysis. Breast Cancer Res [Internet]. 2017 [cited 2024 May 30];19. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5217326/
De Larco JE, Wuertz BR, Furcht LT. The potential role of neutrophils in promoting the metastatic phenotype of tumors releasing interleukin-8. Clin Cancer Res [Internet]. 2004 Aug 1 [cited 2024 May 30];10(15). Available from: https://pubmed.ncbi.nlm.nih.gov/15297389/
Katano M, Torisu M. Neutrophil-mediated tumor cell destruction in cancer ascites. Cancer [Internet]. 1982 Jul 1 [cited 2024 May 30];50(1). Available from: https://pubmed.ncbi.nlm.nih.gov/7083126/
Gago-Dominguez M, Matabuena M, Redondo CM, Patel SP, Carracedo A, Ponte SM, et al. Neutrophil to lymphocyte ratio and breast cancer risk: analysis by subtype and potential interactions. Sci Rep [Internet]. 2020 Aug 6 [cited 2024 May 30];10(1). Available from: https://pubmed.ncbi.nlm.nih.gov/32764699/
Mantovani A, Allavena P, Sica A, Balkwill F. Cancer-related inflammation. Nature [Internet]. 2008 Jul 24 [cited 2024 May 30];454(7203). Available from: https://pubmed.ncbi.nlm.nih.gov/18650914/
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
