Performance of TyrerCuzick Model for Breast Cancer Risk Assessment among Pakistan’s Females
Journal of Advances in Medicine and Medical Research,
Background: Breast cancer incidence is highest in Pakistan among Asian countries. The known risk factors are family history, hormonal exposure, benign proliferative diseases, and high mammographic density which are included in the TyrerCuzick model. The model needs validation studies to implement in prediction, screening, and prevention strategies among different populations. This study aims to validate the TyrerCuzick model for Pakistan's females.
Methods and Materials: A total of 317 biopsy-proven breast cancer patients from the breast surgery clinic at Liaquat National Hospital were included. The 10 years risk score is calculated by applying the TyrerCuzick model software. Subcategories of low risk <2%, moderate risk 2-7%, and high risk >8% were identified. Further risk group stratification is done to find the association with individual factors i.e., age group, menopausal status, family history, and mammographic density.
Results: The mean TyrerCuzick score was low to moderate i.e. 2.23±1.66. The score was distributed as low risk 174(54.9%), moderate risk 137(43.2%), and high risk 6(1.9%). Low risk was observed among 116(81.7%) of less than 50 years old, 105(78.9%) premenopausal, 113(59.8%) with no family history, and 120 patients (59.7%) with low mammographic density. Most of the moderate risk was found in 113(64.6%) of more than 50 years old, 109(60.2%) with postmenopausal, 24(61.5%) with family history, 58(50%) with high mammographic density respectively.
Conclusion: The TyrerCuzick model can predict risk for developing breast cancer among Pakistan’s femalesclose to accurate among older age, postmenopausal, family history of breast cancer, and high mammographic density.
- TyrerCuzick model
- mammographic density.
How to Cite
The Global Cancer Observatory; 2020.
Zaheer S, Shah N, Maqbool SA, Soomro NM. Estimates of past and future time trends in age-specific breast cancer incidence among women in Karachi, Pakistan: 2004–2025. BMC Public Health. 2019;19(1):1-9.
Shieh Y, Eklund M, Madlensky L, Sawyer SD, Thompson CK, Stover Fiscalini A, et al. Breast cancer screening in the precision medicine era: risk-based screening in a population-based trial. J Natl Cancer Inst. 2017;109(5):djw290.
Cuzick J, editor A breast cancer prediction model incorporating familial and personal risk factors. Hereditary Cancer Clin Pract. Springer; 2012.
Louro J, Posso M, Boon MH, Román M, Domingo L, Castells X, et al. A systematic review and quality assessment of individualized breast cancer risk prediction models. Br J Cancer. 2019;121(1):76-85.
Terry MB, Liao Y, Whittemore AS, Leoce N, Buchsbaum R, Zeinomar N, et al. 10-year performance of four models of breast cancer risk: a validation study. Lancet Oncol. 2019;20(4):504-17.
Himes DO, Root AE, Gammon A, Luthy KE. Breast cancer risk assessment: calculating lifetime risk using the Tyrer-Cuzick model. J Nurse Pract. 2016;12(9):581-92.
Brentnall AR, Cohn WF, Knaus WA, Yaffe MJ, Cuzick J, Harvey JA. A case-control study to add volumetric or clinical mammographic density into the Tyrer-Cuzick breast cancer risk model. J breast imaging. 2019;1(2):99-106.
Amir E, Evans D, Shenton A, Lalloo F, Moran A, Boggis C, et al. Evaluation of breast cancer risk assessment packages in the family history evaluation and screening program. J. Med. Genet. 2003;40(11): 807-14.
Quante AS, Whittemore AS, Shriver T, Strauch K, Terry MB. Breast cancer risk assessment across the risk continuum: genetic and nongenetic risk factors contributing to differential model performance.Breast Cancer Res. 2012;14(6):R144.
Khan R, Soomro R, Soomro SA, Mehmood Z. Gail Model Score in Women With Breast Cancer. J. Surg. Pak.. 2018;23:4.
Gail MH. Choosing breast cancer risk models: importance of independent validation. Oxford Uni Press; 2020.
Evans DG, Brentnall AR, Harvie M, Astley S, Harkness EF, Stavrinos P, et al. Breast cancer risk in a screening cohort of Asian and white British/Irish women from Manchester UK. BMC Public Health. 2018; 18(1):1-7.
Choudhury PP, Brook MN, Hurson AN, Lee A, Mulder CV, Coulson P, et al. Comparative validation of the BOADICEA and Tyrer-Cuzick breast cancer risk models incorporating classical risk factors and polygenic risk in a population-based prospective cohort of women of European ancestry. Breast Cancer Res. 2021; 23(1):1-5.
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