Variability Among Breast Cancer Risk Classification Models When Applied at the Level of the Individual Woman

Pregnancy-Related Factors and Breast Cancer Risk for Women Across a Range of Familial Risk

Importance

Few studies have investigated whether the associations between pregnancy-related factors and breast cancer (BC) risk differ by underlying BC susceptibility. Evidence regarding variation in BC risk is critical to understanding BC causes and for developing effective risk-based screening guidelines.

Objective

To examine the association between pregnancy-related factors and BC risk, including modification by a of BC where scores are based on age and BC family history.

Design, Setting, and Participants

This cohort study included participants from the prospective Family Study Cohort (ProF-SC), which includes the 6 sites of the Breast Cancer Family Registry (US, Canada, and Australia) and the Kathleen Cuningham Foundation Consortium (Australia). Analyses were performed in a cohort of women enrolled from 1992 to 2011 without any personal history of BC who were followed up through 2017 with a median (range) follow-up of 10 (1-23) years. Data were analyzed from March 1992 to March 2017.

Exposures

Parity, number of full-term pregnancies (FTP), age at first FTP, years since last FTP, and breastfeeding.

Main Outcomes and Measures

BC diagnoses were obtained through self-report or report by a first-degree relative and confirmed through pathology and data linkages. Cox proportional hazards regression models estimated hazard ratios (HR) and 95% CIs for each exposure, examining modification by PARS of BC. Differences were assessed by estrogen receptor (ER) subtype.

Results

The study included 17 274 women (mean [SD] age, 46.7 [15.1] years; 791 African American or Black participants [4.6%], 1399 Hispanic or Latinx participants [8.2%], and 13 790 White participants [80.7%]) with 943 prospectively ascertained BC cases. Compared with nulliparous women, BC risk was higher after a recent pregnancy for those women with higher PARS (last FTP 0-5 years HR for interaction, 1.53; 95% CI, 1.13-2.07; P for interaction < .001). Associations between other exposures were limited to ER-negative disease. ER-negative BC was positively associated with increasing PARS and increasing years since last FTP (P for interaction < .001) with higher risk for recent pregnancy vs nulliparous women (last FTP 0-5 years HR for interaction, 1.54; 95% CI, 1.03-2.31). ER-negative BC was positively associated with increasing PARS and being aged 20 years or older vs less than 20 years at first FTP (P for interaction = .002) and inversely associated with multiparity vs nulliparity (P for interaction = .01).

Conclusions and Relevance

In this cohort study of women with no prior BC diagnoses, associations between pregnancy-related factors and BC risk were modified by PARS, with greater associations observed for ER-negative BC.

Breast Density: Where Are We Now?

Breast density refers to the amount of fibroglandular tissue relative to fat on mammography and is determined either qualitatively through visual assessment or quantitatively. It is a heritable and dynamic trait associated with age, race/ethnicity, body mass index, and hormonal factors. Increased breast density has important clinical implications including the potential to mask malignancy and as an independent risk factor for the development of breast cancer. Breast density has been incorporated into breast cancer risk models. Given the impact of dense breasts on the interpretation of mammography, supplemental screening may be indicated.

Artificial Intelligence-Driven Mammography-Based Future Breast Cancer Risk Prediction: A Systematic Review

PURPOSE

To summarize the literature regarding the performance of mammography-image based artificial intelligence (AI) algorithms, with and without additional clinical data, for future breast cancer risk prediction.

MATERIALS AND METHODS

A systematic literature review was performed using six databases (medRixiv, bioRxiv, Embase, Engineer Village, IEEE Xplore, and PubMed) from 2012 through September 30, 2022. Studies were included if they used real-world screening mammography examinations to validate AI algorithms for future risk prediction based on images alone or in combination with clinical risk factors. The quality of studies was assessed, and predictive accuracy was recorded as the area under the receiver operating characteristic curve (AUC).

RESULTS

Sixteen studies met inclusion and exclusion criteria, of which 14 studies provided AUC values. The median AUC performance of AI image-only models was 0.72 (range 0.62-0.90) compared with 0.61 for breast density or clinical risk factor-based tools (range 0.54-0.69). Of the seven studies that compared AI image-only performance directly to combined image + clinical risk factor performance, six demonstrated no significant improvement, and one study demonstrated increased improvement.

CONCLUSIONS

Early efforts for predicting future breast cancer risk based on mammography images alone demonstrate comparable or better accuracy to traditional risk tools with little or no improvement when adding clinical risk factor data. Transitioning from clinical risk factor-based to AI image-based risk models may lead to more accurate, personalized risk-based screening approaches.

Implementing the National Dense Breast Reporting Standard, Expanding Supplemental Screening Using Current Guidelines, and the Proposed Find It Early Act

Thirty-eight states and the District of Columbia (DC) have dense breast notification laws that mandate varying levels of patient notification about breast density after a mammogram, and these cover over 90% of American women. On March 10, 2023, the Food and Drug Administration issued a final rule amending regulations under the Mammography Quality Standards Act for a national dense breast reporting standard for both patient results letters and mammogram reports. Effective September 10, 2024, letters will be required to tell a woman her breasts are "dense" or "not dense," that dense tissue makes it harder to find cancers on a mammogram, and that it increases the risk of developing cancer. Women with dense breasts will also be told that other imaging tests in addition to a mammogram may help find cancers. The specific density category can be added (eg, if mandated by a state "inform" law). Reports to providers must include the Breast Imaging Reporting and Data System density category. Implementing appropriate supplemental screening should be based on patient risk for missed breast cancer on mammography; such assessment should include consideration of breast density and other risk factors. This article discusses strategies for implementation. Currently 21 states and DC have varying insurance laws for supplemental breast imaging; in addition, Oklahoma requires coverage for diagnostic breast imaging. A federal insurance bill, the Find It Early Act, has been introduced that would ensure no-cost screening and diagnostic imaging for women with dense breasts or at increased risk and close loopholes in state laws.