Predicting the onset of breast cancer using mammogram imaging data with irregular boundary

Modeling correlated pairs of mammogram images

Mammography remains the primary screening strategy for breast cancer, which continues to be the most prevalent cancer diagnosis among women globally. Because screening mammograms capture both the left and right breast, there is a nonnegligible correlation between the pair of images. Previous studies have explored the concept of averaging between the pair of images after proper image registration; however, no comparison has been made in directly utilizing the paired images. In this paper, we extend the bivariate functional principal component analysis over triangulations to jointly characterize the pair of imaging data bounded in an irregular domain and then nest the extracted features within the survival model to predict the onset of breast cancer. The method is applied to our motivating data from the Joanne Knight Breast Health Cohort at Siteman Cancer Center. Our findings indicate that there was no statistically significant difference in model discrimination performance between averaging the pair of images and jointly modeling the two images. Although the breast cancer study did not reveal any significant difference, it is worth noting that the methods proposed here can be readily extended to other studies involving paired or multivariate imaging data.

Causal mediation analysis using high-dimensional image mediator bounded in irregular domain with an application to breast cancer

Mammography is the primary breast cancer screening strategy. Recent methods have been developed using the mammogram image to improve breast cancer risk prediction. However, it is unclear on the extent to which the effect of risk factors on breast cancer risk is mediated through tissue features summarized in mammogram images and the extent to which it is through other pathways. While mediation analysis has been conducted using mammographic density (a summary measure within the image), the mammogram image is not necessarily well described by a single summary measure and, in addition, such a measure provides no spatial information about the relationship between the exposure risk factor and the risk of breast cancer. Thus, to better understand the role of the mammogram images that provide spatial information about the state of the breast tissue that is causally predictive of the future occurrence of breast cancer, we propose a novel method of causal mediation analysis using mammogram image mediator while accommodating the irregular shape of the breast. We apply the proposed method to data from the Joanne Knight Breast Health Cohort and leverage new insights on the decomposition of the total association between risk factor and breast cancer risk that was mediated by the texture of the underlying breast tissue summarized in the mammogram image.

Repeated measures of mammographic density and texture to evaluate prediction and risk of breast cancer: a systematic review of the methods used in the literature

PURPOSE

It may be important for women to have mammograms at different points in time to track changes in breast density, as fluctuations in breast density can affect breast cancer risk. This systematic review aimed to assess methods used to relate repeated mammographic images to breast cancer risk.

METHODS

The databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov were searched through October 2021. Eligibility criteria included published articles in English describing the relationship of change in mammographic features with risk of breast cancer. Risk of bias was assessed using the Quality in Prognostic Studies tool.

RESULTS

Twenty articles were included. The Breast Imaging Reporting and Data System and Cumulus were most commonly used for classifying mammographic density and automated assessment was used on more recent digital mammograms. Time between mammograms varied from 1 year to a median of 4.1, and only nine of the studies used more than two mammograms. Several studies showed that adding change of density or mammographic features improved model performance. Variation in risk of bias of studies was highest in prognostic factor measurement and study confounding.

CONCLUSION

This review provided an updated overview and revealed research gaps in assessment of the use of texture features, risk prediction, and AUC. We provide recommendations for future studies using repeated measure methods for mammogram images to improve risk classification and risk prediction for women to tailor screening and prevention strategies to level of risk.

Epidemiology and Risk Factors for Breast Cancer: 21st Century Advances, Gaps to Address through Interdisciplinary Science

Research methods to study risk factors and prevention of breast cancer have evolved rapidly. We focus on advances from epidemiologic studies reported over the past two decades addressing scientific discoveries, as well as their clinical and public health translation for breast cancer risk reduction. In addition to reviewing methodology advances such as widespread assessment of mammographic density and Mendelian randomization, we summarize the recent evidence with a focus on the timing of exposure and windows of susceptibility. We summarize the implications of the new evidence for application in risk stratification models and clinical translation to focus prevention-maximizing benefits and minimizing harm. We conclude our review identifying research gaps. These include: pathways for the inverse association of vegetable intake and estrogen receptor (ER)-ve tumors, prepubertal and adolescent diet and risk, early life adiposity reducing lifelong risk, and gaps from changes in habits (e.g., vaping, binge drinking), and environmental exposures.

Association and Prediction Utilizing Craniocaudal and Mediolateral Oblique View Digital Mammography and Long-Term Breast Cancer Risk

Mammographic percentage of volumetric density is an important risk factor for breast cancer. Epidemiology studies historically used film images often limited to craniocaudal (CC) views to estimate area-based breast density. More recent studies using digital mammography images typically use the averaged density between craniocaudal (CC) and mediolateral oblique (MLO) view mammography for 5- and 10-year risk prediction. The performance in using either and both mammogram views has not been well-investigated. We use 3,804 full-field digital mammograms from the Joanne Knight Breast Health Cohort (294 incident cases and 657 controls), to quantity the association between volumetric percentage of density extracted from either and both mammography views and to assess the 5 and 10-year breast cancer risk prediction performance. Our results show that the association between percent volumetric density from CC, MLO, and the average between the two, retain essentially the same association with breast cancer risk. The 5- and 10-year risk prediction also shows similar prediction accuracy. Thus, one view is sufficient to assess association and predict future risk of breast cancer over a 5 or 10-year interval.

PREVENTION RELEVANCE

Expanding use of digital mammography and repeated screening provides opportunities for risk assessment. To use these images for risk estimates and guide risk management in real time requires efficient processing. Evaluating the contribution of different views to prediction performance can guide future applications for risk management in routine care.

Identifying regions of interest in mammogram images

Screening mammography is the primary preventive strategy for early detection of breast cancer and an essential input to breast cancer risk prediction and application of prevention/risk management guidelines. Identifying regions of interest within mammogram images that are associated with 5- or 10-year breast cancer risk is therefore clinically meaningful. The problem is complicated by the irregular boundary issue posed by the semi-circular domain of the breast area within mammograms. Accommodating the irregular domain is especially crucial when identifying regions of interest, as the true signal comes only from the semi-circular domain of the breast region, and noise elsewhere. We address these challenges by introducing a proportional hazards model with imaging predictors characterized by bivariate splines over triangulation. The model sparsity is enforced with the group lasso penalty function. We apply the proposed method to the motivating Joanne Knight Breast Health Cohort to illustrate important risk patterns and show that the proposed method is able to achieve higher discriminatory performance.

Strategies to Achieve Breast Health Equity in the St. Louis Region and Beyond over 15+ Years

Community-based participatory strategies are a promising approach to addressing disparities in community health outcomes. This paper details the efforts of Siteman Cancer Center to achieve breast health equity over the past 15+ years. We begin by describing the activities and successes arising from our breast health community partnerships including identifying priorities, developing recommendations, and implementing patient navigation services to advance breast health. This system-wide coordinated navigation approach that includes primary and specialty care providers helped to increase potential impact on reducing breast health disparities by expediting care, increasing care efficiency, and standardizing referral procedures across systems for all women including those who are uninsured and underinsured. We also discuss a mobile mammography unit that has been deployed to serve women living in both urban and rural regions. The van reached a particularly vulnerable population that was mostly poor, uninsured, and with limited educational backgrounds regardless of their zip code of service. This work shows that collaborations between academic and community partners have resulted in decreased late stage at diagnosis and improved access to mammography. Furthermore, we offer lessons learned and recommendations that may be applicable to other communities.

Joanne Knight Breast Health Cohort at Siteman Cancer Center

PURPOSE

The Joanne Knight Breast Health Cohort was established to link breast cancer risk factors, mammographic breast density, benign breast biopsies and associated tissue markers, and blood markers in a diverse population of women undergoing routine mammographic screening to study risk factors and validate models for breast cancer risk prediction.

METHODS

Women were recruited from November 2008 to April 2012 through the mammography service at the Joanne Knight Breast Health Center at Washington University in St. Louis, Missouri. Baseline questionnaire risk factors, blood, and screening mammograms were collected from 12,153 women. Of these, 1,672 were excluded for prior history of any cancer (except non-melanoma skin) or diagnosis of breast cancer within 6 months of blood draw/registration for the study, for a total of 10,481 women. Follow-up is through linking to electronic health records, tumor registry, and death register. Routine screening mammograms are collected every 1-2 years and incident benign breast biopsies and cancers are identified through record linkage to pathology and tumor registries. Formal fixed tissue samples are retrieved and stored for analysis. County-level measures of structural inequality were derived from publicly available resources.

RESULTS

Cohort Composition: median age at entry was 54.8 years and 26.7% are African American. Through 2020, 74% of participants have had a medical center visit within the past year and 80% within the past 2 years representing an average of 9.7 person-years of follow-up from date of blood draw per participant. 9,997 women are continuing in follow-up. Data collected at baseline include breast cancer risk factors, plasma and white blood cells, and mammograms prior to baseline, at baseline, and during follow-up.

CONCLUSION

This cohort assembled and followed in a routine mammography screening and care setting that serves a diverse population of women in the St. Louis region now provides opportunities to integrate study of questionnaire measures, plasma and DNA markers, benign and malignant tissue markers, and repeated breast image features into prospective evaluation for breast cancer etiology and outcomes.