Article Type
Article
Changed
Fri, 05/13/2022 - 15:57
Author(s)
Robin Seitzman, PhD, MPH
Wendie Berg, MD, PhD

Text copyright DenseBreast-info.org.

 

 

Answer

B.  The Gail risk model1-3 is used to predict 5-year and lifetime risks of developing invasive breast cancer, and to identify women who may benefit from risk-reducing medications such as tamoxifen. The Gail model should not be used to determine risk for purposes of screening magnetic resonance imaging (MRI)4 (or genetic testing).

Breast cancer risk models are used to stratify patients into risk categories to facilitate personalized screening and surveillance plans for clinical management. Several breast cancer risk assessment tools have been developed that include different combinations of known risk factors and are used for the following purposes: 

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 risk-reducing medications. Any woman with a 5-year risk ≥1.67% by the Gail model may be considered for treatment with tamoxifen (pre or postmenopausal), raloxifene (postmenopausal), or aromatase inhibitors (postmenopausal).5  

In the National Surgical Adjuvant Breast and Bowel Project (NSABP) P1 study,6 women at increased risk for breast cancer were defined as follows: 

  • age 35 to 59 years with at least a 1.66% 5-year risk for developing breast cancer by the Gail model
  • personal history of lobular carcinoma in situ (LCIS)
  • age over 60 years.

More than 13,000 such women were randomly assigned to receive tamoxifen or placebo daily for 5 years. Tamoxifen reduced the risk of invasive breast cancer by 49% and reduced the risk of noninvasive cancer by 50% compared with placebo. 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.7,8

2. To identify women who may carry a pathogenic mutation in BRCA1 or BRCA2. Some models (eg, Tyrer-Cuzick [IBIS],9 Penn II,10 BOADICEA,11 and BRCAPRO12) estimate the probability of a BRCA1/2 mutation; however, most testing guidelines are now criterion based (eg, National Comprehensive Cancer Network [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 (ACS) guidelines4 recommend annual screening MRI, in addition to mammography, beginning by age 25 to 30 in women who have a lifetime risk of breast cancer ≥20%. Any of the models used to predict risk of a pathogenic mutation (Tyrer-Cuzick [IBIS], Penn II, BOADICEA, BRCAPRO),or the Claus model,13 but not the Gail model, can be used to estimate lifetime risk for purposes of screening MRI guidelines. The ACS and NCCN guidelines specifically recommend against using the Gail model to determine risk for purposes of MRI screening or risk of pathogenic mutation, as it does not include detailed family history such as age at diagnosis or second-degree relatives. 

ACS and 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.14 

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.14 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,4,15,16 with risk similar in magnitude to pathogenic BRCA1 or BRCA2  carriers. These women are also recommended for annual screening MRI starting at age 25 or 8 years after the chest radiation therapy, whichever is later.

Currently the Tyrer-Cuzick Model (IBIS) version 817 and the Breast Cancer Surveillance Consortium (BCSC) models18 include breast density in risk calculations; the Gail, Penn II, and Claus models do not include breast density. 

Adding polygenic risk scores based on single nucleotide polymorphisms to traditional comprehensive risk models such as the Tyrer-Cuzick model has been shown to improve model performance.19 In addition, artificial intelligence is being used to identify textural and other findings beyond breast density on mammograms that predict increased risk. Such information, which is complementary to the Tyrer-Cuzick model (v.8),20 has more accurately identified high-risk patients than the Tyrer-Cuzick v8 risk model and prior deep learning models.21 

In a study from the Karolinska Institute, a model that included computer-aided detection of microcalcifications and masses in addition to other traditional risk factors (including breast density) successfully identified women who would develop interval or advanced cancer in the 2 years after a normal mammogram and improved short-term (2-to-3-year) risk assessment over TyrerCuzick (v.7) or Gail models.22 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. Risk models that include detailed family history should be used rather than the Gail model to identify women who meet high risk criteria for MRI screening. Research also supports the benefits of MRI in women with dense breasts who are not otherwise considered “high risk,” and while not widely available, lower cost, abbreviated MRI protocols have been validated for all women with dense breasts.23 For more details on risk models, including a risk models table with live links to commonly used breast cancer risk assessment tools, visit https://densebreast-info .org/for-providers/risk-model-tutorial/. ●

RESOURCES

 

For more information, visit medically sourced DenseBreastinfo.org. Comprehensive resources include a free CME opportunity, Dense Breasts and Supplemental Screening.
References
  1. The Breast Cancer Risk Assessment Tool. https://bcrisktool .cancer.gov/calculator.html. Accessed March 15, 2022.
  2. 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.
  3. Gail MH, Costantino JP, Pee D, et al. Projecting individualized absolute invasive breast cancer risk in African American women. J Natl Cancer Inst. 2007;99:1782-1792.
  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. Network NCC. Breast Cancer Risk Reduction (Version 1.2022). https://www.nccn.org/professionals/physician_gls /pdf/breast_risk.pdf. Published 2022. Accessed February 8, 2022.
  6. 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.
  7. 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.
  8. Hernandez RK, Sorensen HT, Pedersen L, et al. Tamoxifen 
    treatment and risk of deep venous thrombosis and pulmonary embolism: a Danish population-based cohort study. Cancer. 2009;115:4442-4449.
  9. Tyrer J, Duffy SW, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med. 2004;23:1111-1130.
  10. Panchal SM, Ennis M, Canon S, et al. Selecting a BRCA risk assessment model for use in a familial cancer clinic. BMC Med Genet. 2008;9:116.
  11. Antoniou AC, Pharoah PP, Smith P, et al. The BOADICEA model of genetic susceptibility to breast and ovarian cancer. Br J Cancer. 2004;91:1580-1590.
  12. Berry DA, Iversen ES, Jr., Gudbjartsson DF, et al. BRCAPRO validation, sensitivity of genetic testing of BRCA1/BRCA2, and prevalence of other breast cancer susceptibility genes. 
     J Clin Oncol. 2002;20:2701-2712.
  13. Claus EB, Risch N, Thompson WD. Autosomal dominant inheritance of early-onset breast cancer. Implications for risk prediction. Cancer. 1994;73:643-651.
  14. Network NCC. Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic (Version 1.2022). https:// www.nccn.org/professionals/physician_gls/pdf/genetics _bop.pdf. Accessed February 9, 2022.
  15. Monticciolo DL, Newell MS, Moy L, et al. Breast cancer screening in women at higher-than-average risk: recommendations from the ACR. J Am Coll Radiol. 2018;15(3 Pt A):408-414.
  16. 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.
  17. Brentnall AR, Cuzick J, Buist DSM, et al. Long-term accuracy of breast cancer risk assessment combining classic risk factors and breast density. JAMA Oncol. 2018;4:e180174.
  18. Tice JA, Cummings SR, Smith-Bindman R, et al. 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.
  19. Brentnall AR, van Veen EM, Harkness EF, et al. A case-control evaluation of 143 single nucleotide polymorphisms for breast cancer risk stratification with classical factors and mammographic density. Int J Cancer. 2020;146:2122-2129.
  20. Yala A, Lehman C, Schuster T, et al. A deep learning mammography-based model for improved breast cancer risk prediction. Radiology. 2019;292:60-66.
  21. Yala A, Mikhael PG, Strand F, et al. Toward robust mammography-based models for breast cancer risk. Sci Transl Med. 2021;13.
  22. Eriksson M, Czene K, Pawitan Y, et al. A clinical model for identifying the short-term risk of breast cancer. Breast Cancer Res. 2017;19:29.
  23. Comstock CE, Gatsonis C, Newstead GM, et al. Comparison of abbreviated breast MRI vs digital breast tomosynthesis for breast cancer detection among women with dense breasts undergoing screening. JAMA. 2020;323:746-756.
Article PDF
obgm0340419_wyd_dbi.pdf
Author and Disclosure Information

Dr. Seitzman is Director of Education and Epidemiology Research, DenseBreast-info.org.

 

Dr. Berg is Professor of Radiology, University of Pittsburgh School of Medicine, Magee-Womens Hospital of the University of Pittsburgh Medical Center. She is also Chief Scientific Advisor, DenseBreast-info.org.

The authors report that DenseBreast-info, Inc. is a 501(c)(3) nonprofit organization which receives unrestricted educational grants from GE Healthcare, Bayer, Volpara Health, Hologic, Siemens Healthineers, Beekley Medical, iCAD, and CMR Naviscan. Dr. Berg also reports being principal investigator for research from Koios Medical, Inc. for which her department receives a grant.
Issue
OBG Management - 34(4)
Publications
MDedge ObGyn
OBG Management
Page Number
19-22
Sections
GYN Image Quiz
Author(s)
Robin Seitzman, PhD, MPH
Wendie Berg, MD, PhD
Author(s)
Robin Seitzman, PhD, MPH
Wendie Berg, MD, PhD
Author and Disclosure Information

Dr. Seitzman is Director of Education and Epidemiology Research, DenseBreast-info.org.

 

Dr. Berg is Professor of Radiology, University of Pittsburgh School of Medicine, Magee-Womens Hospital of the University of Pittsburgh Medical Center. She is also Chief Scientific Advisor, DenseBreast-info.org.

The authors report that DenseBreast-info, Inc. is a 501(c)(3) nonprofit organization which receives unrestricted educational grants from GE Healthcare, Bayer, Volpara Health, Hologic, Siemens Healthineers, Beekley Medical, iCAD, and CMR Naviscan. Dr. Berg also reports being principal investigator for research from Koios Medical, Inc. for which her department receives a grant.
Author and Disclosure Information

Dr. Seitzman is Director of Education and Epidemiology Research, DenseBreast-info.org.

 

Dr. Berg is Professor of Radiology, University of Pittsburgh School of Medicine, Magee-Womens Hospital of the University of Pittsburgh Medical Center. She is also Chief Scientific Advisor, DenseBreast-info.org.

The authors report that DenseBreast-info, Inc. is a 501(c)(3) nonprofit organization which receives unrestricted educational grants from GE Healthcare, Bayer, Volpara Health, Hologic, Siemens Healthineers, Beekley Medical, iCAD, and CMR Naviscan. Dr. Berg also reports being principal investigator for research from Koios Medical, Inc. for which her department receives a grant.
Article PDF
obgm0340419_wyd_dbi.pdf
Article PDF
obgm0340419_wyd_dbi.pdf

Text copyright DenseBreast-info.org.

 

 

Answer

B.  The Gail risk model1-3 is used to predict 5-year and lifetime risks of developing invasive breast cancer, and to identify women who may benefit from risk-reducing medications such as tamoxifen. The Gail model should not be used to determine risk for purposes of screening magnetic resonance imaging (MRI)4 (or genetic testing).

Breast cancer risk models are used to stratify patients into risk categories to facilitate personalized screening and surveillance plans for clinical management. Several breast cancer risk assessment tools have been developed that include different combinations of known risk factors and are used for the following purposes: 

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 risk-reducing medications. Any woman with a 5-year risk ≥1.67% by the Gail model may be considered for treatment with tamoxifen (pre or postmenopausal), raloxifene (postmenopausal), or aromatase inhibitors (postmenopausal).5  

In the National Surgical Adjuvant Breast and Bowel Project (NSABP) P1 study,6 women at increased risk for breast cancer were defined as follows: 

  • age 35 to 59 years with at least a 1.66% 5-year risk for developing breast cancer by the Gail model
  • personal history of lobular carcinoma in situ (LCIS)
  • age over 60 years.

More than 13,000 such women were randomly assigned to receive tamoxifen or placebo daily for 5 years. Tamoxifen reduced the risk of invasive breast cancer by 49% and reduced the risk of noninvasive cancer by 50% compared with placebo. 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.7,8

2. To identify women who may carry a pathogenic mutation in BRCA1 or BRCA2. Some models (eg, Tyrer-Cuzick [IBIS],9 Penn II,10 BOADICEA,11 and BRCAPRO12) estimate the probability of a BRCA1/2 mutation; however, most testing guidelines are now criterion based (eg, National Comprehensive Cancer Network [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 (ACS) guidelines4 recommend annual screening MRI, in addition to mammography, beginning by age 25 to 30 in women who have a lifetime risk of breast cancer ≥20%. Any of the models used to predict risk of a pathogenic mutation (Tyrer-Cuzick [IBIS], Penn II, BOADICEA, BRCAPRO),or the Claus model,13 but not the Gail model, can be used to estimate lifetime risk for purposes of screening MRI guidelines. The ACS and NCCN guidelines specifically recommend against using the Gail model to determine risk for purposes of MRI screening or risk of pathogenic mutation, as it does not include detailed family history such as age at diagnosis or second-degree relatives. 

ACS and 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.14 

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.14 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,4,15,16 with risk similar in magnitude to pathogenic BRCA1 or BRCA2  carriers. These women are also recommended for annual screening MRI starting at age 25 or 8 years after the chest radiation therapy, whichever is later.

Currently the Tyrer-Cuzick Model (IBIS) version 817 and the Breast Cancer Surveillance Consortium (BCSC) models18 include breast density in risk calculations; the Gail, Penn II, and Claus models do not include breast density. 

Adding polygenic risk scores based on single nucleotide polymorphisms to traditional comprehensive risk models such as the Tyrer-Cuzick model has been shown to improve model performance.19 In addition, artificial intelligence is being used to identify textural and other findings beyond breast density on mammograms that predict increased risk. Such information, which is complementary to the Tyrer-Cuzick model (v.8),20 has more accurately identified high-risk patients than the Tyrer-Cuzick v8 risk model and prior deep learning models.21 

In a study from the Karolinska Institute, a model that included computer-aided detection of microcalcifications and masses in addition to other traditional risk factors (including breast density) successfully identified women who would develop interval or advanced cancer in the 2 years after a normal mammogram and improved short-term (2-to-3-year) risk assessment over TyrerCuzick (v.7) or Gail models.22 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. Risk models that include detailed family history should be used rather than the Gail model to identify women who meet high risk criteria for MRI screening. Research also supports the benefits of MRI in women with dense breasts who are not otherwise considered “high risk,” and while not widely available, lower cost, abbreviated MRI protocols have been validated for all women with dense breasts.23 For more details on risk models, including a risk models table with live links to commonly used breast cancer risk assessment tools, visit https://densebreast-info .org/for-providers/risk-model-tutorial/. ●

RESOURCES

 

For more information, visit medically sourced DenseBreastinfo.org. Comprehensive resources include a free CME opportunity, Dense Breasts and Supplemental Screening.

Text copyright DenseBreast-info.org.

 

 

Answer

B.  The Gail risk model1-3 is used to predict 5-year and lifetime risks of developing invasive breast cancer, and to identify women who may benefit from risk-reducing medications such as tamoxifen. The Gail model should not be used to determine risk for purposes of screening magnetic resonance imaging (MRI)4 (or genetic testing).

Breast cancer risk models are used to stratify patients into risk categories to facilitate personalized screening and surveillance plans for clinical management. Several breast cancer risk assessment tools have been developed that include different combinations of known risk factors and are used for the following purposes: 

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 risk-reducing medications. Any woman with a 5-year risk ≥1.67% by the Gail model may be considered for treatment with tamoxifen (pre or postmenopausal), raloxifene (postmenopausal), or aromatase inhibitors (postmenopausal).5  

In the National Surgical Adjuvant Breast and Bowel Project (NSABP) P1 study,6 women at increased risk for breast cancer were defined as follows: 

  • age 35 to 59 years with at least a 1.66% 5-year risk for developing breast cancer by the Gail model
  • personal history of lobular carcinoma in situ (LCIS)
  • age over 60 years.

More than 13,000 such women were randomly assigned to receive tamoxifen or placebo daily for 5 years. Tamoxifen reduced the risk of invasive breast cancer by 49% and reduced the risk of noninvasive cancer by 50% compared with placebo. 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.7,8

2. To identify women who may carry a pathogenic mutation in BRCA1 or BRCA2. Some models (eg, Tyrer-Cuzick [IBIS],9 Penn II,10 BOADICEA,11 and BRCAPRO12) estimate the probability of a BRCA1/2 mutation; however, most testing guidelines are now criterion based (eg, National Comprehensive Cancer Network [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 (ACS) guidelines4 recommend annual screening MRI, in addition to mammography, beginning by age 25 to 30 in women who have a lifetime risk of breast cancer ≥20%. Any of the models used to predict risk of a pathogenic mutation (Tyrer-Cuzick [IBIS], Penn II, BOADICEA, BRCAPRO),or the Claus model,13 but not the Gail model, can be used to estimate lifetime risk for purposes of screening MRI guidelines. The ACS and NCCN guidelines specifically recommend against using the Gail model to determine risk for purposes of MRI screening or risk of pathogenic mutation, as it does not include detailed family history such as age at diagnosis or second-degree relatives. 

ACS and 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.14 

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.14 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,4,15,16 with risk similar in magnitude to pathogenic BRCA1 or BRCA2  carriers. These women are also recommended for annual screening MRI starting at age 25 or 8 years after the chest radiation therapy, whichever is later.

Currently the Tyrer-Cuzick Model (IBIS) version 817 and the Breast Cancer Surveillance Consortium (BCSC) models18 include breast density in risk calculations; the Gail, Penn II, and Claus models do not include breast density. 

Adding polygenic risk scores based on single nucleotide polymorphisms to traditional comprehensive risk models such as the Tyrer-Cuzick model has been shown to improve model performance.19 In addition, artificial intelligence is being used to identify textural and other findings beyond breast density on mammograms that predict increased risk. Such information, which is complementary to the Tyrer-Cuzick model (v.8),20 has more accurately identified high-risk patients than the Tyrer-Cuzick v8 risk model and prior deep learning models.21 

In a study from the Karolinska Institute, a model that included computer-aided detection of microcalcifications and masses in addition to other traditional risk factors (including breast density) successfully identified women who would develop interval or advanced cancer in the 2 years after a normal mammogram and improved short-term (2-to-3-year) risk assessment over TyrerCuzick (v.7) or Gail models.22 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. Risk models that include detailed family history should be used rather than the Gail model to identify women who meet high risk criteria for MRI screening. Research also supports the benefits of MRI in women with dense breasts who are not otherwise considered “high risk,” and while not widely available, lower cost, abbreviated MRI protocols have been validated for all women with dense breasts.23 For more details on risk models, including a risk models table with live links to commonly used breast cancer risk assessment tools, visit https://densebreast-info .org/for-providers/risk-model-tutorial/. ●

RESOURCES

 

For more information, visit medically sourced DenseBreastinfo.org. Comprehensive resources include a free CME opportunity, Dense Breasts and Supplemental Screening.
References
  1. The Breast Cancer Risk Assessment Tool. https://bcrisktool .cancer.gov/calculator.html. Accessed March 15, 2022.
  2. 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.
  3. Gail MH, Costantino JP, Pee D, et al. Projecting individualized absolute invasive breast cancer risk in African American women. J Natl Cancer Inst. 2007;99:1782-1792.
  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. Network NCC. Breast Cancer Risk Reduction (Version 1.2022). https://www.nccn.org/professionals/physician_gls /pdf/breast_risk.pdf. Published 2022. Accessed February 8, 2022.
  6. 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.
  7. 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.
  8. Hernandez RK, Sorensen HT, Pedersen L, et al. Tamoxifen 
    treatment and risk of deep venous thrombosis and pulmonary embolism: a Danish population-based cohort study. Cancer. 2009;115:4442-4449.
  9. Tyrer J, Duffy SW, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med. 2004;23:1111-1130.
  10. Panchal SM, Ennis M, Canon S, et al. Selecting a BRCA risk assessment model for use in a familial cancer clinic. BMC Med Genet. 2008;9:116.
  11. Antoniou AC, Pharoah PP, Smith P, et al. The BOADICEA model of genetic susceptibility to breast and ovarian cancer. Br J Cancer. 2004;91:1580-1590.
  12. Berry DA, Iversen ES, Jr., Gudbjartsson DF, et al. BRCAPRO validation, sensitivity of genetic testing of BRCA1/BRCA2, and prevalence of other breast cancer susceptibility genes. 
     J Clin Oncol. 2002;20:2701-2712.
  13. Claus EB, Risch N, Thompson WD. Autosomal dominant inheritance of early-onset breast cancer. Implications for risk prediction. Cancer. 1994;73:643-651.
  14. Network NCC. Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic (Version 1.2022). https:// www.nccn.org/professionals/physician_gls/pdf/genetics _bop.pdf. Accessed February 9, 2022.
  15. Monticciolo DL, Newell MS, Moy L, et al. Breast cancer screening in women at higher-than-average risk: recommendations from the ACR. J Am Coll Radiol. 2018;15(3 Pt A):408-414.
  16. 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.
  17. Brentnall AR, Cuzick J, Buist DSM, et al. Long-term accuracy of breast cancer risk assessment combining classic risk factors and breast density. JAMA Oncol. 2018;4:e180174.
  18. Tice JA, Cummings SR, Smith-Bindman R, et al. 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.
  19. Brentnall AR, van Veen EM, Harkness EF, et al. A case-control evaluation of 143 single nucleotide polymorphisms for breast cancer risk stratification with classical factors and mammographic density. Int J Cancer. 2020;146:2122-2129.
  20. Yala A, Lehman C, Schuster T, et al. A deep learning mammography-based model for improved breast cancer risk prediction. Radiology. 2019;292:60-66.
  21. Yala A, Mikhael PG, Strand F, et al. Toward robust mammography-based models for breast cancer risk. Sci Transl Med. 2021;13.
  22. Eriksson M, Czene K, Pawitan Y, et al. A clinical model for identifying the short-term risk of breast cancer. Breast Cancer Res. 2017;19:29.
  23. Comstock CE, Gatsonis C, Newstead GM, et al. Comparison of abbreviated breast MRI vs digital breast tomosynthesis for breast cancer detection among women with dense breasts undergoing screening. JAMA. 2020;323:746-756.
References
  1. The Breast Cancer Risk Assessment Tool. https://bcrisktool .cancer.gov/calculator.html. Accessed March 15, 2022.
  2. 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.
  3. Gail MH, Costantino JP, Pee D, et al. Projecting individualized absolute invasive breast cancer risk in African American women. J Natl Cancer Inst. 2007;99:1782-1792.
  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. Network NCC. Breast Cancer Risk Reduction (Version 1.2022). https://www.nccn.org/professionals/physician_gls /pdf/breast_risk.pdf. Published 2022. Accessed February 8, 2022.
  6. 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.
  7. 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.
  8. Hernandez RK, Sorensen HT, Pedersen L, et al. Tamoxifen 
    treatment and risk of deep venous thrombosis and pulmonary embolism: a Danish population-based cohort study. Cancer. 2009;115:4442-4449.
  9. Tyrer J, Duffy SW, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med. 2004;23:1111-1130.
  10. Panchal SM, Ennis M, Canon S, et al. Selecting a BRCA risk assessment model for use in a familial cancer clinic. BMC Med Genet. 2008;9:116.
  11. Antoniou AC, Pharoah PP, Smith P, et al. The BOADICEA model of genetic susceptibility to breast and ovarian cancer. Br J Cancer. 2004;91:1580-1590.
  12. Berry DA, Iversen ES, Jr., Gudbjartsson DF, et al. BRCAPRO validation, sensitivity of genetic testing of BRCA1/BRCA2, and prevalence of other breast cancer susceptibility genes. 
     J Clin Oncol. 2002;20:2701-2712.
  13. Claus EB, Risch N, Thompson WD. Autosomal dominant inheritance of early-onset breast cancer. Implications for risk prediction. Cancer. 1994;73:643-651.
  14. Network NCC. Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic (Version 1.2022). https:// www.nccn.org/professionals/physician_gls/pdf/genetics _bop.pdf. Accessed February 9, 2022.
  15. Monticciolo DL, Newell MS, Moy L, et al. Breast cancer screening in women at higher-than-average risk: recommendations from the ACR. J Am Coll Radiol. 2018;15(3 Pt A):408-414.
  16. 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.
  17. Brentnall AR, Cuzick J, Buist DSM, et al. Long-term accuracy of breast cancer risk assessment combining classic risk factors and breast density. JAMA Oncol. 2018;4:e180174.
  18. Tice JA, Cummings SR, Smith-Bindman R, et al. 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.
  19. Brentnall AR, van Veen EM, Harkness EF, et al. A case-control evaluation of 143 single nucleotide polymorphisms for breast cancer risk stratification with classical factors and mammographic density. Int J Cancer. 2020;146:2122-2129.
  20. Yala A, Lehman C, Schuster T, et al. A deep learning mammography-based model for improved breast cancer risk prediction. Radiology. 2019;292:60-66.
  21. Yala A, Mikhael PG, Strand F, et al. Toward robust mammography-based models for breast cancer risk. Sci Transl Med. 2021;13.
  22. Eriksson M, Czene K, Pawitan Y, et al. A clinical model for identifying the short-term risk of breast cancer. Breast Cancer Res. 2017;19:29.
  23. Comstock CE, Gatsonis C, Newstead GM, et al. Comparison of abbreviated breast MRI vs digital breast tomosynthesis for breast cancer detection among women with dense breasts undergoing screening. JAMA. 2020;323:746-756.
Issue
OBG Management - 34(4)
Issue
OBG Management - 34(4)
Page Number
19-22
Page Number
19-22
Publications
MDedge ObGyn
OBG Management
Publications
MDedge ObGyn
OBG Management
Article Type
Article
Sections
GYN Image Quiz
Questionnaire Body

Quiz developed in collaboration with 

Disallow All Ads
Content Gating
No Gating (article Unlocked/Free)
Alternative CME
Disqus Comments
Default
Eyebrow Default
IN THE KNOW WITH DENSEBREAST-INFO
Gate On Date
Fri, 01/21/2022 - 13:30
Un-Gate On Date
Fri, 01/21/2022 - 13:30
Use ProPublica
CFC Schedule Remove Status
Fri, 01/21/2022 - 13:30
Hide sidebar & use full width
render the right sidebar.
Conference Recap Checkbox
Not Conference Recap
Clinical Edge
Display the Slideshow in this Article
Medscape Article
Display survey writer
Reuters content
Disable Inline Native ads
WebMD Article
Article PDF Media