## 1 Introduction
The BRCAPRO calculates the breast cancer risk primarily by assessing the probability of carrying major genes based on personal and family histories of breast, ovarian, or related cancers associated with these genes [23]. It incorporates pedigree information, including age at onset, and the outcomes of genetic testing for BRCA1 and BRCA2 in both women and their relatives. However, these models are less suitable for assessing the breast cancer risk in the general population, as they do not account for other unknown genetic factors. The Tyrer-Cuzick model [24], instead, combines two widely used sub-models for assessing the breast cancer risk. It integrates a genetic segregation model for familial risk with a proportional hazards regression model for other risk factors. The risk factors in the model can be broadly categorized into five main groups: i. family history and highly penetrant dominant genetic mutations; ii. factors related to estrogen exposure, such as age at first childbirth, age at menopause, menarche (onset of periods), use of hormone replacement therapy, height, and weight; iii. specific types of prior benign breast disease; iv. breast imaging features observed on mammograms, notably the amount of dense tissue; and v. common but individually less impactful genetic differences, specifically single nucleotide polymorphisms (SNPs). These SNPs, comprising several hundred relatively common genetic variants, each with a modest impact on disease, collectively contribute significantly to the overall risk assessment through a \"polygenic risk score\" [25]. Additionally, there are other potential risk factors that are challenging to quantify but may enhance the model performance. Other models, including the Breast Cancer Surveillance Consortium (BCSC) [20; 26] and Rosner-Collotz models [27; 28], rely solely on a regression function. The Rosner-Collotz model includes terms for the same broad risk factors as the Tyrer-Cuzick model, along with additional factors such as alcohol consumption, adolescent body somatotype, and hormone levels. However, these models differ in the assumptions regarding the risks and prevalence of risk factors, as well as the utilization of interaction terms [22]. Despite the widespread use of these risk models, each of them presents significant limitations that can affect their predictive accuracy and generalizability. The Gail model, for example, exhibits low AUC values in non-white populations, particularly among African American and Asian women, as it was initially developed using data from predominantly white women [12; 29]. Furthermore, it fails to incorporate crucial high-risk factors such as genetic mutations, including BRCA1/2, or a more extensive family history, and it overlooks important risk modifiers like breast density and lifestyle factors. Similarly, the BOADICEA, while more inclusive of a detailed family history and genetic factors, suffers from its complexity, making it less accessible for widespread clinical use. Moreover, its reliance on data from European populations limits its applicability to other ethnic groups [30; 31]. The BRCAPRO model, while focusing on BRCA1/2 mutations, does not account for other significant risk factors and heavily depends on a detailed and accurate family history, which may not always be available or reliable [32; 33]. The Tyrer-Cuzick model (IBIS), on the other hand, is known to overestimate the risk in certain populations, such as women with benign breast disease or those using hormone replacement therapy, and requires complex, detailed data inputs [22; 24]. Additionally, the Breast Cancer Surveillance Consortium (BCSC) model is limited by its reliance on U.S. mammography screening data, making it less relevant for international populations, while its focus on breast density often overlooks other genetic or non-genetic risk factors [34; 35]. Lastly, the Rosner-Collotz model, though comprehensive, is data-intensive and challenging for routine clinical use, while it underestimates the genetic risk by prioritizing modifiable risk factors [36; 37].