1. How are the models used?
1. To identify women who may benefit from risk-reducing medications
The Gail model is used to determine risk for purposes of advising on use of medications to reduce risk. In the National Surgical Adjuvant Breast and Bowel Project (NSABP) P1  study, women at increased risk for breast cancer were defined as follows: 1) age 35 to 59 years with at least a 1.66% five-year risk for developing breast cancer by the Gail model; or 2) personal history of lobular carcinoma in situ (LCIS); or 3) age over 60 years. 13,388 such women were randomized to receive tamoxifen or placebo daily for five years. Tamoxifen reduced the risk of invasive breast cancer by 49% and reduced the risk of noninvasive cancer by 50%.
The reduced risk of breast cancer was only seen for estrogen-receptor expressing tumors. There was a 2.5-fold increase in risk of endometrial cancer in women taking tamoxifen and a decrease in hip and spine fracture risk. Blood clots causing stroke and deep vein thrombosis are increased in women taking tamoxifen [2, 3].
2. To identify women who may carry a pathogenic mutation in BRCA1 or BRCA2
Some models (e.g. Tyrer-Cuzick [IBIS], Penn II, BOADICEA, BRCAPRO) will also calculate the probability of a BRCA1/2 mutation; however, most testing guidelines are now criterion based (e.g. NCCN) as opposed to probability based. In practical terms, clinical decision-making around genetic testing is rarely based on a priori probabilities.
3. To identify women who meet criteria for high-risk screening MRI
Current American Cancer Society guidelines  recommend annual screening MRI, in addition to mammography, beginning by age 25 to 30 in women who have a lifetime risk (LTR) of breast cancer of 20 to 25% or more. Any of the models used to predict risk of a pathogenic mutation (Tyrer-Cuzick [IBIS], Penn II, BOADICEA, BRCAPRO), or the Claus model, but NOT the Gail model, can be used to estimate lifetime risk for purposes of screening MRI guidelines.
National Comprehensive Cancer Network (NCCN) guidelines also recommend annual screening MRI beginning by age 25, with the addition of mammography beginning at age 30, in women who are known to carry pathogenic mutations in BRCA1 or BRCA2 (unless the woman has had bilateral mastectomy), and in women who are first-degree relatives of known mutation carriers but who are themselves untested (see table below) .
Women who are known to carry or are first-degree untested relatives of individuals with less common disease-causing mutations (such as those associated with Li-Fraumeni syndrome, Bannayan-Riley-Ruvalcaba syndrome, hereditary diffuse gastric cancer, Peutz-Jeghers syndrome, Cowden syndrome, Neurofibromatosis type 1, or Fanconi anemia) are also recommended for annual screening MRI beginning between ages 20-35, depending on the mutation (see table below). Women with known pathogenic mutations in ATM, CHEK2, or NBN should consider annual MRI starting at age 40 or 5-10 years before the earliest known breast cancer in the family (whichever comes first).
Finally, women with prior chest radiation therapy (such as for Hodgkin disease) between ages 10 and 30 are at high risk for developing breast cancer [4, 6, 7], similar in risk to BRCA1 or BRCA2 carriers, and are also recommended for annual screening MRI starting at age 25 or 8 years after the chest radiation therapy, whichever is later.
Table: NCCN Breast Cancer Screening Guidelines in Women Who Carry or Are First-Degree Untested Relatives of Individuals with Pathogenic Mutations Known to Increase Breast Cancer Risk 
|Gene||Associated Hereditary Cancer Syndromes||NCCN Breast Cancer Screening Guidelines|
|Starting age for MRI
|Starting age for
|BRCA1||BRCA-related breast and/or
ovarian cancer syndrome
|BRCA2||BRCA-related breast and/or
ovarian cancer syndrome
|CDH1||Hereditary diffuse gastric cancer||30||30|
|NF1||Neurofibromatosis type 1||30a,b||30a|
hamartoma tumor syndrome,
aScreening recommendations only apply to individuals with a clinical diagnosis of Neurofibromatosis type 1 (NF1).
bThere are currently no data to suggest an increased breast cancer risk after age 50 years in women with NF1; therefore, MRI screening may discontinue at 50 years of age in this group. In addition, the presence of breast neurofibromas may lead to false-positive MRI results; however, more data on sensitivity and specificity of MRI in women with NF1 is needed.
cStart at stated age or 5-10 years before the earliest known breast cancer in the family (whichever comes first).
1. Fisher B, Costantino JP, Wickerham DL, et al. Tamoxifen for prevention of breast cancer: Report of the National Surgical Adjuvant Breast and Bowel Project P-1 Study. J Natl Cancer Inst 1998; 90:1371-1388
2. Hernandez RK, Sorensen HT, Pedersen L, Jacobsen J, Lash TL. Tamoxifen treatment and risk of deep venous thrombosis and pulmonary embolism: A Danish population-based cohort study. Cancer 2009; 115:4442-4449
3. Fisher B, Costantino JP, Wickerham DL, et al. Tamoxifen for the prevention of breast cancer: Current status of the National Surgical Adjuvant Breast and Bowel Project P-1 study. J Natl Cancer Inst 2005; 97:1652-1662>
4. Saslow D, Boetes C, Burke W, et al. American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J Clin 2007; 57:75-89
5. National Comprehensive Cancer Network. Genetic/familial high-risk assessment: Breast, ovarian, and pancreatic, Version 1.2020. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines). https://www.nccn.org/professionals/physician_gls/pdf/genetics_bop.pdf Published December 4, 2019. Accessed April 25, 2020.
6. Oeffinger KC, Ford JS, Moskowitz CS, et al. Breast cancer surveillance practices among women previously treated with chest radiation for a childhood cancer. JAMA 2009; 301:404-414
7. Monticciolo DL, Newell MS, Moy L, Niell B, Monsees B, Sickles EA. Breast cancer screening in women at higher-than-average risk: Recommendations from the ACR. J Am Coll Radiol 2018; 15:408-414
2. Risk model explanations
The Risk Models Table that follows features details and live links to several commonly utilized breast cancer risk assessment models.
Models that do include breast density in risk calculations:
- Tyrer-Cuzick Model (IBIS) version 8 update was based in part on input from Dr. Jennifer Harvey and Dr. Martin Yaffe and includes breast density. In this model, breast density is one of the top five factors determining breast cancer risk. This model is the most comprehensive and tends to be the most accurate at predicting risk at the population level.
- Breast Cancer Surveillance Consortium (BCSC) model  is a modification of the Gail model and was developed and validated in a large, ethnically diverse, prospective cohort of women undergoing screening mammography. It includes the risk factors with the greatest population attributable risks for breast cancer, including age, breast density, family history, history of a breast biopsy, and a polygenic risk score (PRS) based on common genetic variations .
- Artificial Intelligence (AI) is being used to identify textural and other findings beyond breast density on mammograms that predict increased risk; such information is complementary to the Tyrer-Cuzick model (v.8) .
- A recent study of a mammography-based deep learning model developed at Massachusetts General Hospital called Mirai more accurately identified high-risk patients than the Tyrer-Cuzick v8 risk model and prior deep learning models. Mirai identified 41.5% of women developing breast cancer in the next five years as high-risk, compared to only 22.9% with the Tyrer-Cuzick model v8. Mirai performed similarly across all density categories . Addition of family history or mutation status did not further improve on performance of AI alone.
- In a study from the Karolinska Institutet , use of AI to identify mammographic microcalcifications and masses, even if not the site of actual malignancy, and differences between right and left breasts, successfully predicted women who would develop interval or advanced cancer in the two years after a normal mammogram and improved short-term (2-to-3-year) risk assessment over Tyrer-Cuzick (v.7) or Gail models . This model proved more accurate than traditional risk models and can augment genetic/family history to help identify women who should and, importantly, who should not, have supplemental screening after 2D mammography.
Models that do not include breast density in risk calculations:
1. Tice JA, Cummings SR, Smith-Bindman R, Ichikawa L, Barlow WE, Kerlikowske K. Using clinical factors and mammographic breast density to estimate breast cancer risk: Development and validation of a new predictive model. Ann Intern Med 2008; 148:337-347
2. Vachon CM, Pankratz VS, Scott CG, et al. The contributions of breast density and common genetic variation to breast cancer risk. J Natl Cancer Inst 2015; 107
3. Yala A, Lehman C, Schuster T, Portnoi T, Barzilay R. A deep learning mammography-based model for improved breast cancer risk prediction. Radiology 2019:182716
4. Yala A, Mikhael PG, Strand F, et al. Toward robust mammography-based models for breast cancer risk. Sci Transl Med. 2021;13(578):eaba4373. doi: 10.1126/scitranslmed.aba4373
5. Eriksson M, Czene K, Strand F, Zackrisson S, Lindholm P, Lång K, Förnvik D, Sartor H, Mavaddat N, Easton D, Hall P. Identification of Women at High Risk of Breast Cancer Who Need Supplemental Screening. Radiology. 2020 Sep 8:201620. doi: 10.1148/radiol.2020201620. Epub ahead of print. PMID: 32897160.
6. Eriksson M, Czene K, Pawitan Y, Leifland K, Darabi H, Hall P. A clinical model for identifying the short-term risk of breast cancer. Breast Cancer Res 2017; 19:29
7. Gail MH, Brinton LA, Byar DP, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 1989; 81:1879-1886
8. Claus EB, Risch N, Thompson WD. Autosomal dominant inheritance of early-onset breast cancer: Implications for risk prediction. Cancer 1994; 73:643-651
3. Diagnostic considerations
Risk models and diagnostic considerations:
- Any particular risk model will not include all known risk factors
- Estimated risk can vary substantially between models
- Age: As a woman gets older, her 5- and 10-year risk of developing breast cancer increase but her lifetime risk decreases
- Risks change every year and should be periodically reassessed. For example, risk changes with age (see above), family history may change if family members have been diagnosed with breast or ovarian cancer, or a breast biopsy may have been performed.
Risk model limitations:
- Adoption (or otherwise unknown family history)
- Small family size
- All models underestimate rates of breast cancer. At best they identify about 70% of women who will develop cancer at the population level.
- All models are low in accuracy at the individual level (“discrimination”)
- Risk models have been largely developed and validated in populations of white European ancestry and may not perform equally in other ethnic populations
Polygenic risk score for improvement in model performance:
Single nucleotide polymorphisms (SNPs, informally, “snips”) are variations that occur normally throughout the DNA. Individually, their impact on breast cancer risk is small; however, when combined to create a “polygenic risk score” (PRS) or “SNP score”, some of these variations increase the risk of breast cancer. Including PRS with classical risk factors used in current risk models may improve accuracy of risk assessment [1-4]. Measurement of PRS is not yet widely available.
Artificial Intelligence (AI)-based risk assessment:
Mammography-based features beyond breast density identified from deep learning models [5, 6] and iCAD software [7, 8] have been used in combination with traditional risk factors to improve risk prediction. More recently a model was developed and validated in a U.S. multiethnic cohort to estimate risks using tomosynthesis imaging features and age to help identify women who would be diagnosed with an interval cancer or a cancer at the next screen after negative or benign tomosynthesis screen . These approaches can help better identify women who should and, importantly, who should not, have supplemental screening after 2D mammography or tomosynthesis. AI can also help triage mammograms more likely to be normal, allowing radiologists to focus greater attention on those more likely to have cancer.
AI is also being used with breast ultrasound. AI-based decision support performs similar to breast imaging specialists  and has been shown to improve accuracy of sonographic breast lesion assessment among nonspecialists . AI can reduce false positives on ultrasound .
Indications for genetic testing include :
- Male breast cancer or family history of male breast cancer: 6% of men with breast cancer are found to have a pathogenic mutation in BRCA2
- Personal history of breast cancer and age ≤ 50 at diagnosis
- Personal history of breast cancer with diagnosis at any age and any of the following:
- Triple-negative breast cancer
- Multiple primary breast cancers
- Lobular breast cancer with personal or family history of diffuse gastric cancer
- Ashkenazi Jewish ancestry
- ≥ 1 close blood relativea with any of the following: breast cancer at age ≤ 50, male breast cancer, ovarian cancer, pancreatic cancer or metastatic or high-risk prostate cancer
- ≥ 3 total diagnoses of breast cancer in patient or close blood relatives
- ≥ 2 close blood relatives with either breast or prostate cancer (any grade)
- First- or second-degree relative meeting any of the criteria listed aboveb
- ≥ 5% risk of BRCA1/2 pathogenic variant based on prior probability models (e.g., Tyrer-Cuzick, BRCAPRO, CanRisk)
- Testing may be considered with Ashkenazi Jewish ancestry absent additional risk factors
a Close blood relatives include first-, second-, and third-degree relatives on the same side of the family.
– First-degree relatives: parents, siblings, and children;
– Second-degree relatives: grandparents, aunts, uncles, nieces, nephews, grandchildren, and half-siblings;
– Third-degree relatives: great-grandparents, great-aunts, great-uncles, great-grandchildren, first cousins, and half aunts and uncles.
b If the affected relative has pancreatic cancer or prostate cancer, only first-degree relatives should be offered testing unless indicated based on additional family history.
Consider tamoxifen or raloxifene in post-menopausal women with at least one of the following:
- At least 1.67% 5-year risk by Gail model
- Personal history of lobular carcinoma in situ
- Age at least 60 years
For women with disease-causing BRCA mutation(s), discuss options:
- Bilateral prophylactic mastectomy; if not, then annual screening MRI is recommended.
- Risk-reducing salpingo-oophorectomy if at least 10-year life expectancy
Risk model indications for increased surveillance:
- Annual MRI screening is recommended in “high-risk” women (to include mammography after age 30):
- Lifetime risk (LTR) estimated at 20% or more by models that predict mutation carrier status (e.g., Tyrer-Cuzick (IBIS), Penn II, BOADICEA, BRACAPRO, or Claus model – but NOT the Gail Model)
- Disease-causing mutation(s), such as TP53, BRCA1/2, STK11, CDH1, NF1, PALB2, PTEN, ATM, CHEK2, BARD1) or first-degree untested relative of disease-causing mutation carrier but untested. Starting age for annual screening MRI ranges from 20-40 years depending on the mutation.
- Prior chest radiation therapy (e.g., for Hodgkin’s disease) before age 30. Begin annual screening MRI at age 25 or 8 years after chest radiation therapy, whichever is later.
- Personal history of breast cancer and dense breasts or diagnosis by age 50.
- Consider annual MRI in addition to annual mammography or tomosynthesis in the following subgroups of women:
- Personal history of breast cancer without dense breasts
- History of LCIS or atypia (ADH, ALH, atypical papilloma)
- Dense breasts, especially if extremely dense breasts or other risk factors (personal history of breast cancer, prior atypical biopsy, intermediate family history). The European Society of Breast Imaging (EUSOBI) recommends offering screening MRI to women aged 50-70 with extremely dense breasts every 2-4 years. Some U.S. states require insurance to cover screening MRI for women with extremely dense breasts or other risk factors. See coverage in your state at https://densebreast-info.org/legislative-information/state-legislation-map/.
- Continue annual MRI screening (and mammography) to age 70 (unless bilateral mastectomy) if at least 10-year life expectancy, patient continues to meet high-risk guidelines, and can tolerate MRI (no kidney failure, pacemaker, some other metallic implants, severe claustrophobia).
In women recommended for MRI but unable to access or tolerate it, consider contrast-enhanced mammography or supplemental ultrasound screening .
1. Shieh Y, Hu D, Ma L, et al. Breast cancer risk prediction using a clinical risk model and polygenic risk score. Breast Cancer Res Treat 2016;159(3):513–525.
2. Vachon CM, Pankratz VS, Scott CG, et al. The contributions of breast density and common genetic variation to breast cancer risk. J Natl Cancer Inst 2015;107(5). doi: 10.1093/jnci/dju397.
3. Zhang X, Rice M, Tworoger SS, et al. Addition of a polygenic risk score, mammographic density, and endogenous hormones to existing breast cancer risk prediction models: a nested case- control study. PLoS Med 2018;15(9):e1002644.
4. Yang Y, Tao R, Shu X, et al. Incorporating polygenic risk scores and nongenetic risk factors for breast cancer risk prediction among Asian women. JAMA Netw Open 2022;5(3):e2149030. doi: 10.1001/jamanetworkopen.2021.49030.
5. Yala A, Mikhael PG, Strand F, Lin G, Smith K, Wan YL, et al. Toward robust mammography-based models for breast cancer risk. Sci Transl Med. 2021;13(578):eaba4373. doi: 10.1126/scitranslmed.aba4373.
6. Eriksson M, Conant EF, Kontos D, Hall P. Risk Assessment in Population-Based Breast Cancer Screening. J Clin Oncol 2022;40:2279-2280
7. Eriksson M, Czene K, Strand F, Zackrisson S, Lindholm P, Lång K, et al. Identification of Women at High Risk of Breast Cancer Who Need Supplemental Screening. Radiology. 2020;297(2):327-333. doi: 10.1148/radiol.2020201620. Epub 2020 Sep 8.
8. Eriksson M, Czene K, Pawitan Y, Leifland K, Darabi H, Hall P. A clinical model for identifying the short-term risk of breast cancer. Breast Cancer Res. 2017;19(1):29. doi: 10.1186/s13058-017-0820-y.
9. Eriksson M, Destounis S, Czene K, et al. A risk model for digital breast tomosynthesis to predict breast cancer and guide clinical care. Sci Transl Med 2022; 14:eabn3971
10. Berg, WA, Gur D, Bandos AI, et al. Impact of Original and Artificially Improved Artificial Intelligence–based Computer-aided Diagnosis on Breast US Interpretation, Journal of Breast Imaging 2021;3(3):301–311. doi: 10.1093/jbi/wbab013
11. Mango VL, Sun M, Wynn RT, Ha R. Should We Ignore, Follow, or Biopsy? Impact of Artificial Intelligence Decision Support on Breast Ultrasound Lesion Assessment. AJR Am J Roentgenol. 2020;214(6):1445-1452. doi: 10.2214/AJR.19.21872. Epub 2020 Apr 22. Erratum in: AJR Am J Roentgenol. 2020;215(1):262.
12. Shen Y, Shamout FE, Oliver JR, et al. Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams. Nat Commun 2021;12(1):5645. doi: 10.1038/s41467-021-26023-2.
13. National Comprehensive Cancer Network. Genetic/familial high-risk assessment: Breast, ovarian, and pancreatic (Version 3.2023). NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines). Available at: https://www.nccn.org/professionals/physician_gls/pdf/genetics_bop.pdf. Accessed February 22, 2023.
14. National Comprehensive Cancer Network. Breast Cancer Screening and Diagnosis (Version 1.2022). Available at: https://www.nccn.org/professionals/physician_gls/pdf/breast-screening.pdf. Accessed December 19, 2022.
4. Risk Models Table (includes live links)
|MODEL/LINK||PROVIDES||OUTPUT||INCLUDES||WHEN TO USE|
|Models that DO include breast density as a risk factor|
|Personal risk and risk of mutation carrier||10-year* and lifetime risk (LTR) of developing breast cancer||Current age, age at menarche, height, weight, parity, age of first childbirth, age at menopause, HRT use, pathology results from prior benign or atypical breast biopsies, ovarian cancer, breast density (BI-RADS®, Volpara density, or Visual Analog Scale), Ashkenazi descent, age at diagnosis of first- and second-degree female relatives with breast or ovarian cancer and male relatives with breast cancer||MRI screening (20% lifetime risk threshold); historical use for risk assessment for genetic testing (10% risk for pathogenic mutations as threshold)|
|Breast Cancer Surveillance Consortium (BCSC)||Personal risk||5-year and 10-year risk of developing invasive breast cancer||Current age, race/ethnicity, BI-RADS® breast density, first-degree relative, pathology results from prior benign or atypical breast biopsies||Risk assessment for use of medications for prevention (tamoxifen, raloxifene, aromatase inhibitors)|
|Models that DO NOT include breast density as a risk factor|
|Gail||Personal risk||5-year and LTR of developing breast cancer||Current age, age at menarche, age at first live birth childbirth, number of first-degree relatives (mother, sisters, daughters) with breast cancer, prior benign biopsies, prior atypical biopsy and race/ethnicity.
DOES NOT INCLUDE: Age of diagnosis of relatives; not to be used to assess “high-risk” criteria for MRI screening
|When considering tamoxifen or other risk-reducing medications (>1.7% 5-year risk)
NOT to be used for risk assessment for purposes of screening MRI nor for genetic testing
|Penn II||Personal risk and risk of mutation carrier||LTR risk of developing breast cancer||Ashkenazi descent, number of women in family diagnosed with both breast and ovarian cancer, number of women in family diagnosed with ovarian or fallopian cancer in absence of breast cancer, number of breast cancer cases in family diagnosed < age 50, age of youngest breast cancer case in family; number of people in family with: presence of mother-daughter diagnosed with bilateral breast cancer, male breast cancer diagnosis, presence of pancreatic cancer or prostate cancer||MRI screening (20% lifetime risk threshold); historical use for risk assessment for genetic testing (10% risk for pathogenic mutations as threshold)|
|Claus||Personal risk||LTR of developing breast cancer||Occurrence(s) of breast cancer in first-degree and second-degree female relative(s) by decade age of diagnosis||MRI screening (20% lifetime risk threshold)
*10-year risk by Tyrer-Cuzick model can be divided by two to estimate 5-year risk (personal communication, Jack Cuzick, PhD, 6/10/20)