Prospective breast cancer risk prediction model for women undergoing screening mammography

Use of an AI Score Combining Cancer Signs, Masking, and Risk to Select Patients for Supplemental Breast Cancer Screening

Background Mammographic density measurements are used to identify patients who should undergo supplemental imaging for breast cancer detection, but artificial intelligence (AI) image analysis may be more effective. Purpose To assess whether AISmartDensity-an AI-based score integrating cancer signs, masking, and risk-surpasses measurements of mammographic density in identifying patients for supplemental breast imaging after a negative screening mammogram. Materials and Methods This retrospective study included randomly selected individuals who underwent screening mammography at Karolinska University Hospital between January 2008 and December 2015. The models in AISmartDensity were trained and validated using nonoverlapping data. The ability of AISmartDensity to identify future cancer in patients with a negative screening mammogram was evaluated and compared with that of mammographic density models. Sensitivity and positive predictive value (PPV) were calculated for the top 8% of scores, mimicking the proportion of patients in the Breast Imaging Reporting and Data System "extremely dense" category. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and was compared using the DeLong test. Results The study population included 65 325 examinations (median patient age, 53 years [IQR, 47-62 years])-64 870 examinations in healthy patients and 455 examinations in patients with breast cancer diagnosed within 3 years of a negative screening mammogram. The AUC for detecting subsequent cancers was 0.72 and 0.61 (P < .001) for AISmartDensity and the best-performing density model (age-adjusted dense area), respectively. For examinations with scores in the top 8%, AISmartDensity identified 152 of 455 (33%) future cancers with a PPV of 2.91%, whereas the best-performing density model (age-adjusted dense area) identified 57 of 455 (13%) future cancers with a PPV of 1.09% (P < .001). AISmartDensity identified 32% (41 of 130) and 34% (111 of 325) of interval and next-round screen-detected cancers, whereas the best-performing density model (dense area) identified 16% (21 of 130) and 9% (30 of 325), respectively. Conclusion AISmartDensity, integrating cancer signs, masking, and risk, outperformed traditional density models in identifying patients for supplemental imaging after a negative screening mammogram. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kim and Chang in this issue.

Risk Analysis of Breast Cancer by Using Bilateral Mammographic Density Differences: A Case-Control Study

The identification of risk factors helps radiologists assess the risk of breast cancer. Quantitative factors such as age and mammographic density are established risk factors for breast cancer. Asymmetric breast findings are frequently encountered during diagnostic mammography. The asymmetric area may indicate a developing mass in the early stage, causing a difference in mammographic density between the left and right sides. Therefore, this paper aims to propose a quantitative parameter named bilateral mammographic density difference (BMDD) for the quantification of breast asymmetry and to verify BMDD as a risk factor for breast cancer. To quantitatively evaluate breast asymmetry, we developed a semi-automatic method to estimate mammographic densities and calculate BMDD as the absolute difference between the left and right mammographic densities. And then, a retrospective case-control study, covering the period from July 2006 to October 2014, was conducted to analyse breast cancer risk in association with BMDD. The study included 364 women diagnosed with breast cancer and 364 matched control patients. As a result, a significant difference in BMDD was found between cases and controls (P < 0.001) and the case-control study demonstrated that women with BMDD > 10% had a 2.4-fold higher risk of breast cancer (odds ratio, 2.4; 95% confidence interval, 1.3-4.5) than women with BMDD ≤ 10%. In addition, we also demonstrated the positive association between BMDD and breast cancer risk among the subgroups with different ages and the Breast Imaging Reporting and Data System (BI-RADS) mammographic density categories. This study demonstrated that BMDD could be a potential risk factor for breast cancer.

The relation between obesity and breast cancer risk in women by considering menstruation status and geographical variations: a systematic review and meta-analysis

Given the increase in the incidence of breast cancer during the past decades, several studies have investigated the effects of variables on breast cancer, especially obesity. This systematic review and meta-analysis aims to evaluate any effects of obesity on breast cancer risk in women, before and after menopause, and in different continents.All forms of relevant literature examining any association between obesity and breast cancer, including cohort, case-control, and cross-sectional studies, were identified in the PubMed, Scopus, EMBASE, and Web of Science databases from January 1, 1990 until January 13, 2023. Body mass index (BMI) > 30 was used to indicate obesity. Every type of breast cancer was examined as outcome factors. The quality of the papers was evaluated using the Newcastle-Ottawa scale checklist. The Egger and Begg test was used to evaluate publication bias. To assess any extra impact of each research on the final measurement, a sensitivity analysis was carried out.One hundred and two studies were included in this meta-analysis. Respectively, 48 and 67 studies reported associations between obesity and breast cancer in pre and post menopausal women. Combining all studies, the pooled OR of the association between obesity and breast cancer in pre-menopausal women was OR = 0.93 CI: (0.85-1.1), (I² = 65.4%), and for post-menopausal woman, OR = 1.26 CI: (1.19-1.34), (I² = 90.5%).Obesity has a protective role in breast cancer among pre-menopausal women, but this relationship is statistically significant only in European women. The chance of developing breast cancer increases in post-menopausal women who are obese. This relationship is significant among Asian, North American, African and European women.

Qualitative assessments of density and background parenchymal enhancement on contrast-enhanced spectral mammography associated with breast cancer risk in high-risk women

OBJECTIVE

To investigate the correlation between the risk of breast cancer for high-risk females and the density and background parenchymal enhancement (BPE) on contrast-enhanced spectral mammography (CESM).

METHODS

Females at high-risk, without breast cancer history and received CESM from July 2016 to December 2017 were retrospectively enrolled. The longest follow-up time was 4.5 years, and patients who developed breast cancer with maximized follow-up time were classified as cancer cohort, while females who did not develop breast cancer were categorized as control cohort. These two cohorts were one-to-one matched in age, family and/or genetic history of breast cancer, menopausal status and BRCA status. The density and BPE at CESM imaging were assessed. Conditional logistic regression was applied to evaluate the relationship between imaging features and breast cancer risk.

RESULTS

During the follow-up interval, 90 women at high-risk without history of breast cancer were newly diagnosed. Compared with minimal BPE, increasing BPE levels were associated with the risk of breast cancer among high-risk females in a time interval of 4.5 years (mild: odds ratio [OR]=3.2, p = 0.001; moderate: OR = 4.0, p = 0.002; marked: OR = 11.2, p < 0.001). In addition, females with mild, moderate or marked BPE were four times more likely to be diagnosed with breast cancer than females with minimal BPE in a time interval of 4.5 years (OR = 4.0, p < 0.001).

CONCLUSION

Qualitative CESM BPE assessment may be useful in the prediction of breast cancer risk among high-risk females.

ADVANCES IN KNOWLEDGE

• Qualitative CESM BPE assessment may be useful in the prediction of breast cancer risk among high-risk women during the follow-up period of 4.5 years.• The significance of breast density as an independent risk factor is not fully established for high-risk women during the follow-up period of 4.5 years.

Association of Longitudinal Mammographic Breast Density Changes with Subsequent Breast Cancer Risk

Background Although Breast Imaging Reporting and Data System (BI-RADS) density classification has been used to assess future breast cancer risk, its reliability and validity are still debated in literature. Purpose To determine the association between overall longitudinal changes in mammographic breast density and breast cancer risk stratified by menopausal status. Materials and Methods In a retrospective cohort study using the Korean National Health Insurance Service database, women aged at least 40 years without a history of cancer who underwent three consecutive biennial mammographic screenings in 2009-2014 were followed up through December 2020. Participants were divided according to baseline breast density: fatty (BI-RADS categories a, b) versus dense (BI-RADS categories c, d) and then into subgroups on the basis of changes from the first to second and from second to third screenings. Women without change in breast density were used as the reference group. Main outcomes were incident breast cancer events, both invasive breast cancer and ductal carcinoma in situ. Cox proportion hazard regression was used to calculate the hazard ratio (HR) with adjustment for other covariables. Results Among 2 253 963 women (mean age, 59 years ± 9) there were 22 439 detected breast cancers. Premenopausal women with fatty breasts at the first screening had a higher risk of breast cancer as density increased in the second and third screenings (fatty-to-dense HR, 1.45 [95% CI: 1.27, 1.65]; dense-to-fatty HR, 1.53 [95% CI: 1.34, 1.74]; dense-to-dense HR, 1.93 [95% CI: 1.75, 2.13]). In premenopausal women with dense breasts at baseline, those in whom density continuously decreased had a 0.62-fold lower risk (95% CI: 0.56, 0.69). Similar results were observed in postmenopausal women, remaining significant after adjustment for baseline breast density or changes in body mass index (fatty-to-dense HR, 1.50 [95% CI: 1.39, 1.62]; dense-to-fatty HR, 1.42 [95% CI: 1.31, 1.53]; dense-to-dense HR, 1.62 [95% CI: 1.51, 1.75]). Conclusion In both premenopausal and postmenopausal women undergoing three consecutive biennial mammographic screenings, a consecutive increase in breast density augmented the future breast cancer risk whereas a continuous decrease was associated with a lower risk. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Kataoka et al in this issue.

Artificial Intelligence in Breast X-Ray Imaging

This topical review is focused on the clinical breast x-ray imaging applications of the rapidly evolving field of artificial intelligence (AI). The range of AI applications is broad. AI can be used for breast cancer risk estimation that could allow for tailoring the screening interval and the protocol that are woman-specific and for triaging the screening exams. It also can serve as a tool to aid in the detection and diagnosis for improved sensitivity and specificity and as a tool to reduce radiologists' reading time. AI can also serve as a potential second 'reader' during screening interpretation. During the last decade, numerous studies have shown the potential of AI-assisted interpretation of mammography and to a lesser extent digital breast tomosynthesis; however, most of these studies are retrospective in nature. There is a need for prospective clinical studies to evaluate these technologies to better understand their real-world efficacy. Further, there are ethical, medicolegal, and liability concerns that need to be considered prior to the routine use of AI in the breast imaging clinic.

Modified Brier score for evaluating prediction accuracy for binary outcomes

The Brier score has been a popular measure of prediction accuracy for binary outcomes. However, it is not straightforward to interpret the Brier score for a prediction model since its value depends on the outcome prevalence. We decompose the Brier score into two components, the mean squares between the estimated and true underlying binary probabilities, and the variance of the binary outcome that is not reflective of the model performance. We then propose to modify the Brier score by removing the variance of the binary outcome, estimated via a general sliding window approach. We show that the new proposed measure is more sensitive for comparing different models through simulation. A standardized performance improvement measure is also proposed based on the new criterion to quantify the improvement of prediction performance. We apply the new measures to the data from the Breast Cancer Surveillance Consortium and compare the performance of predicting breast cancer risk using the models with and without its most important predictor.

Developing the breast cancer risk prediction system using hybrid machine learning algorithms

BACKGROUND

Breast cancer (BC) is the most common cause of cancer-related deaths in women globally. Currently, many machine learning (ML)-based predictive models have been established to assist clinicians in decision making for the prediction of BC. However, preventing risk factor formation even with having healthy lifestyle behaviors or preventing disease at early stages can significantly lead to optimal population-wide BC health. Thus, we aimed to develop a prediction model by using a genetic algorithm (GA) incorporating several ML algorithms for the prediction and early warning of BC.

MATERIAL AND METHODS

The data of 3168 healthy individuals and 1742 patient case records in the BC Registry Database in Ayatollah Taleghani hospital, Abadan, Iran were analyzed. First, a modified hybrid GA was used to perform feature selection and optimization of selected features. Then, with the use of selected features, several ML algorithms were trained to predict BC. Afterward, the performance of each model was measured in terms of accuracy, precision, sensitivity, specificity, and receiver operating characteristic (ROC) curve metrics. Finally, a clinical decision support system based on the best model was developed.

RESULTS

After performing feature selection, age, consumption of dairy products, BC family history, breast biopsy, chest X-ray, hormone therapy, alcohol consumption, being overweight, having children, and education statuses were selected as the most important features for prediction of BC. The experimental results showed that the decision tree yielded a superior performance than other ML models, with values of 99.3%, 99.5%, 98.26% for accuracy, specificity, and sensitivity, respectively.

CONCLUSION

The developed predictive system can accurately identify persons who are at elevated risk for BC and can be used as an essential clinical screening tool for the early prevention of BC and serve as an important tool for developing preventive health strategies.

Bilateral Analysis Boosts the Performance of Mammography-based Deep Learning Models in Breast Cancer Risk Prediction

Breast cancer is one of the leading causes of death among women. Early prediction of breast cancer can significantly improve the survival rates. Breast density was proven as a reliable risk factor. Deep learning models can learn subtle cues in the mammogram images. CNN models were recently shown to improve the risk discrimination in full-field mammograms. This study aims to improve risk prediction models using bilateral analysis. Bilateral analysis is the process of comparing two breasts to verify presence of anomalies. We developed a Siamese neural network to leverage the bilateral information and asymmetries between the two mammograms of the same patient. We tested our model on 271 patients and compared the results of our Siamese model against the traditional unilateral CNN model. Our results showed AUCs of 0.75 and 0.70 respectively (p = 0.0056). The Siamese model also exhibits higher sensitivity, specificity, precision, and false positive rate with values of 0.68, 0.69, 0.71, 0.31 respectively. While the CNN values were 0.61, 0.66, 0.67, 0.34 respectively. We merged both models by two techniques using pre-trained weights and weighted voting ensemble. The merging technique boosted the AUC to 0.78. The results suggest that bilateral analysis can significantly improve the risk discrimination.

Body mass index, breast density, and the risk of breast cancer development in relation to the menopausal status; results from a population-based screening program in a native African-Arab country

Background

Risk factors are traits or behaviors that have an influence on the development of breast cancer (BC). Awareness of the prevalent risk factors can guide in developing prevention interventions.

Purpose

To evaluate the correlation between the breast density, body mass index, and the risk of breast cancer development in relation to the menopausal status in a native African-Arab population.

Material and methods

The study included 30,443 screened females who were classified into cancer and non-cancer groups and each group was further sub-classified into pre- and postmenopausal groups. The breast density (BD) was reported and subjectively classified according to the 2013 ACR BI-RADS breast density classification. The weight and height were measured, and the body mass index (BMI) was calculated and classified according to the WHO BMI classification.

Results

A statistically significant difference was calculated between the mean BMI in the cancer and non-cancer groups (p: .027) as well as between the pre- and postmenopausal groups (p < .001). A positive statistically insignificant correlation was calculated between the breast density and the risk of breast cancer in the premenopausal group (OR: 1.062, p: .919) and a negative highly significant correlation was calculated in the postmenopausal group (OR: 0.234, p < .001).

Conclusion

BMI and BD are inversely associated with each other. The current studied population presented unique ethnic characteristics, where a decreased BD and an increased BMI were found to be independent risk factors for developing breast cancer.

The current status of risk-stratified breast screening

Apart from high-risk scenarios such as the presence of highly penetrant genetic mutations, breast screening typically comprises mammography or tomosynthesis strategies defined by age. However, age-based screening ignores the range of breast cancer risks that individual women may possess and is antithetical to the ambitions of personalised early detection. Whilst screening mammography reduces breast cancer mortality, this is at the risk of potentially significant harms including overdiagnosis with overtreatment, and psychological morbidity associated with false positives. In risk-stratified screening, individualised risk assessment may inform screening intensity/interval, starting age, imaging modality used, or even decisions not to screen. However, clear evidence for its benefits and harms needs to be established. In this scoping review, the authors summarise the established and emerging evidence regarding several critical dependencies for successful risk-stratified breast screening: risk prediction model performance, epidemiological studies, retrospective clinical evaluations, health economic evaluations and qualitative research on feasibility and acceptability. Family history, breast density or reproductive factors are not on their own suitable for precisely estimating risk and risk prediction models increasingly incorporate combinations of demographic, clinical, genetic and imaging-related parameters. Clinical evaluations of risk-stratified screening are currently limited. Epidemiological evidence is sparse, and randomised trials only began in recent years.

Application of Deep Learning to Construct Breast Cancer Diagnosis Model

(1) Background: According to Taiwan’s ministry of health statistics, the rate of breast cancer in women is increasing annually. Each year, more than 10,000 women suffer from breast cancer, and over 2000 die of the disease. The mortality rate is annually increasing, but if breast cancer tumors are detected earlier, and appropriate treatment is provided immediately, the survival rate of patients will increase enormously. (2) Methods: This research aimed to develop a stepwise breast cancer model architecture to improve diagnostic accuracy and reduce the misdiagnosis rate of breast cancer. In the first stage, a breast cancer risk factor dataset was utilized. After pre-processing, Artificial Neural Network (ANN) and the support vector machine (SVM) were applied to the dataset to classify breast cancer tumors and compare their performances. The ANN achieved 76.6% classification accuracy, and the SVM using radial functions achieved the best classification accuracy of 91.6%. Therefore, SVM was utilized in the determination of results concerning the relevant breast cancer risk factors. In the second stage, we trained AlexNet, ResNet101, and InceptionV3 networks using transfer learning. The networks were studied using Adaptive Moment Estimation (ADAM) and Stochastic Gradient Descent with Momentum (SGDM) based optimization algorithm to diagnose benign and malignant tumors, and the results were evaluated; (3) Results: According to the results, AlexNet obtained 81.16%, ResNet101 85.51%, and InceptionV3 achieved a remarkable accuracy of 91.3%. The results of the three models were utilized in establishing a voting combination, and the soft-voting method was applied to average the prediction result for which a test accuracy of 94.20% was obtained; (4) Conclusions: Despite the small number of images in this study, the accuracy is higher compared to other literature. The proposed method has demonstrated the need for an additional productive tool in clinical settings when radiologists are evaluating mammography images of patients.

Early Detection Initiative: A randomized controlled trial of algorithm-based screening in patients with new onset hyperglycemia and diabetes for early detection of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is the only leading cause of cancer death without an early detection strategy. In retrospective studies, 0.5-1% of subjects >50 years of age who newly develop biochemically-defined diabetes have been diagnosed with PDAC within 3 years of meeting new onset hyperglycemia and diabetes (NOD) criteria. The Enriching New-onset Diabetes for Pancreatic Cancer (ENDPAC) algorithm further risk stratifies NOD subjects based on age and changes in weight and diabetes parameters. We present the methodology for the Early Detection Initiative (EDI), a randomized controlled trial of algorithm-based screening in patients with NOD for early detection of PDAC. We hypothesize that study interventions (risk stratification with ENDPAC and imaging with Computerized Tomography (CT) scan) in NOD will identify earlier stage PDAC. EDI uses a modified Zelen's design with post-randomization consent. Eligible subjects will be identified through passive surveillance of electronic medical records and eligible study participants randomized 1:1 to the Intervention or Observation arm. The sample size is 12,500 subjects. The ENDPAC score will be calculated only in those randomized to the Intervention arm, with 50% (n = 3125) expected to have a high ENDPAC score. Consenting subjects in the high ENDPAC group will undergo CT imaging for PDAC detection and an estimate of potential harm. The effectiveness and efficacy evaluation will compare proportions of late stage PDAC between Intervention and Observation arm per randomization assignment or per protocol, respectively, with a planned interim analysis. The study is designed to improve the detection of sporadic PDAC when surgical intervention is possible.

Supplemental Cancer Screening for Women With Dense Breasts: Guidance for Health Care Professionals

Mammography is the standard for breast cancer screening. The sensitivity of mammography in identifying breast cancer, however, is reduced for women with dense breasts. Thirty-eight states have passed laws requiring that all women be notified of breast tissue density results in their mammogram report. The notification includes a statement that differs by state, encouraging women to discuss supplemental screening options with their health care professionals (HCPs). Several supplemental screening tests are available for women with dense breast tissue, but no established guidelines exist to direct HCPs in their recommendation of preferred supplemental screening test. Tailored screening, which takes into consideration the patient's mammographic breast density and lifetime breast cancer risk, can guide breast cancer screening strategies that are more comprehensive. This review describes the benefits and limitations of the various available supplemental screening tests to guide HCPs and patients in choosing the appropriate breast cancer screening.

Minimizing re-excision after breast conserving surgery - a review of radiofrequency spectroscopy for real-time, intraoperative margin assessment

INTRODUCTION

For early-stage breast cancer, breast-conserving surgery (BCS) plus radiation is standard-of-care. Nationwide, >20% of BCS patients require re-excision for positive margins, resulting in delayed adjuvant therapy, increased complications, emotional and financial stress for patients, and additional cost to the healthcare system. Although several methods may be employed to mitigate positive margins, no technique can fully address the need. MarginProbe® is an adjunctive tool for real-time intraoperative margin assessment and is shown to reduce positive margins by >50%.

AREAS COVERED

Discussion of the impact of re-excision following BCS, a review of currently available methods for intraoperative margin management, followed by a technology and literature review of the MarginProbe® Radiofrequency Spectroscopy System.

EXPERT OPINION

Re-excision significantly impacts patients, providers and payers. Limitations in the ability to assess margins at time of surgery warrant more advanced methods of residual disease detection. MarginProbe facilitates the most efficient pathway for breast cancer patients through the surgical phase of treatment. The device is well-suited for adoption as the healthcare focus shifts from volume to value and supports the three pillars of the US Department of Health and Human Services' 'Triple-Aim' strategy: improve population health, improve patient experience of care, and reduce per-capita costs.

Fibroglandular Tissue and Background Parenchymal Enhancement on Breast MR Imaging Correlates With Breast Cancer

Objectives

To evaluate the association of breast cancer with both the background parenchymal enhancement intensity and volume (BPE_I and BPE_V, respectively) and the amount of fibroglandular tissue (FGT) using an automatic quantitative assessment method in breast magnetic resonance imaging (MRI).

Materials and Methods

Among 17,274 women who underwent breast MRI, 132 normal women (control group), 132 women with benign breast lesions (benign group), and 132 women with breast cancer (cancer group) were randomly selected and matched by age and menopausal status. The area under the receiver operating characteristic curve (AUC) was compared in Cancer vs Control and Cancer vs Benign groups to assess the discriminative ability of BPE_I, BPE_V and FGT.

Results

Compared with the control groups, the cancer group showed a significant difference in BPE_V with a maximum AUC of 0.715 and 0.684 for patients in premenopausal and postmenopausal subgroup, respectively. And the cancer group showed a significant difference in BPE_V with a maximum AUC of 0.622 and 0.633 for patients in premenopausal and postmenopausal subgroup, respectively, when compared with the benign group. FGT showed no significant difference when breast cancer group was compared with normal control and benign lesion group, respectively. Compared with the control groups, BPE_I showed a slight difference in the cancer group. Compared with the benign group, no significant difference was seen in cancer group.

Conclusion

Increased BPE_V is correlated with a high risk of breast cancer While FGT is not.

Prevalence of Breast Cancer in Female Physicians Performing Procedures With Significant Fluoroscopy Exposure: Survey

OBJECTIVE

The aim of this study was to assess self-reported breast cancer prevalence potentially associated with occupational radiation exposure from fluoroscopy-guided procedures in female physicians using current standard protection measures.

METHODS

An institutional review board-approved survey was shared as a link to self-identified female physicians. We compared self-reported prevalence of breast cancer among women physicians with longer than 10 years of postfellowship practice in specialties with heavy fluoroscopy exposure versus specialties with low fluoroscopy exposure. We compared the distribution of breast cancer risk factors and personal radiation safety measures.

RESULTS

A total of 303 women physicians participated in the survey. There were 8 (16%) of 49 from the first study group and 8 (18%) of 44 from the second study group who self-reported a diagnosis of breast cancer. There were no differences in the distribution of breast cancer risk factors between the 2 groups or prevalence of breast cancer (P = 0.81).

CONCLUSIONS

Self-reported breast cancer prevalence is similar between women physicians who are practicing fluoroscopically heavy and light medical specialties.

Physicians' perceptions of breast density notification laws and appropriate patient follow-up

Breast density notification laws have been adopted in the absence of consistent guidelines for post-notification follow-up. This can lead to inconsistent and potentially deficient management of women's health due to inconsistent physician practices. We examined physicians' knowledge and practices regarding follow-up for patients who receive density notifications. Physicians who referred patients to a Michigan hospital network for screening mammograms were recruited to participate in survey study; 105 (29.8%) responded. The survey assessed physicians' demographics, knowledge, and awareness of breast density and breast cancer risk and of density notification laws, and perceptions of appropriate follow-up behaviors for their patients who received density notifications. Most physicians (75%) knew about the notification law, and they were generally comfortable responding to breast density questions and deciding on follow-up. Most indicated that additional breast imaging (68.0%), followed by assessing breast cancer risk (24.7%) were appropriate follow-up responses. Physicians who performed breast cancer risk assessments, and who were more comfortable with breast density questions and follow-up decision making, were more likely to propose additional imaging. Male physicians were less likely to propose assessing breast cancer risk, and less likely to propose clinical and/or breast self-examinations. Divergence between practice and guidelines when it comes to supplemental breast cancer screening, coupled with density notification language that promotes additional screening in the absence of consistent evidence, remains concerning. Improved understanding of how density notification recipients and their physicians make decisions about supplemental screening is warranted to ensure that breast cancer risk is properly considered.

Developing and validating an individualized breast cancer risk prediction model for women attending breast cancer screening

BACKGROUND

Several studies have proposed personalized strategies based on women's individual breast cancer risk to improve the effectiveness of breast cancer screening. We designed and internally validated an individualized risk prediction model for women eligible for mammography screening.

METHODS

Retrospective cohort study of 121,969 women aged 50 to 69 years, screened at the long-standing population-based screening program in Spain between 1995 and 2015 and followed up until 2017. We used partly conditional Cox proportional hazards regression to estimate the adjusted hazard ratios (aHR) and individual risks for age, family history of breast cancer, previous benign breast disease, and previous mammographic features. We internally validated our model with the expected-to-observed ratio and the area under the receiver operating characteristic curve.

RESULTS

During a mean follow-up of 7.5 years, 2,058 women were diagnosed with breast cancer. All three risk factors were strongly associated with breast cancer risk, with the highest risk being found among women with family history of breast cancer (aHR: 1.67), a proliferative benign breast disease (aHR: 3.02) and previous calcifications (aHR: 2.52). The model was well calibrated overall (expected-to-observed ratio ranging from 0.99 at 2 years to 1.02 at 20 years) but slightly overestimated the risk in women with proliferative benign breast disease. The area under the receiver operating characteristic curve ranged from 58.7% to 64.7%, depending of the time horizon selected.

CONCLUSIONS

We developed a risk prediction model to estimate the short- and long-term risk of breast cancer in women eligible for mammography screening using information routinely reported at screening participation. The model could help to guiding individualized screening strategies aimed at improving the risk-benefit balance of mammography screening programs.

Relation of Quantitative Histologic and Radiologic Breast Tissue Composition Metrics With Invasive Breast Cancer Risk

Background

Benign breast disease (BBD) is a strong breast cancer risk factor, but identifying patients that might develop invasive breast cancer remains a challenge.

Methods

By applying machine-learning to digitized hematoxylin and eosin-stained biopsies and computer-assisted thresholding to mammograms obtained circa BBD diagnosis, we generated quantitative tissue composition metrics and determined their association with future invasive breast cancer diagnosis. Archival breast biopsies and mammograms were obtained for women (18-86 years of age) in a case-control study, nested within a cohort of 15 395 BBD patients from Kaiser Permanente Northwest (1970-2012), followed through mid-2015. Patients who developed incident invasive breast cancer (ie, cases; n = 514) and those who did not (ie, controls; n = 514) were matched on BBD diagnosis age and plan membership duration. All statistical tests were 2-sided.

Results

Increasing epithelial area on the BBD biopsy was associated with increasing breast cancer risk (odds ratio [OR]_{Q4 vs Q1} = 1.85, 95% confidence interval [CI] = 1.13 to 3.04; P _trend = .02). Conversely, increasing stroma was associated with decreased risk in nonproliferative, but not proliferative, BBD (P _{heterogeneity} = .002). Increasing epithelium-to-stroma proportion (OR_{Q4 vs Q1} = 2.06, 95% CI =1.28 to 3.33; P _trend = .002) and percent mammographic density (MBD) (OR_{Q4 vs Q1} = 2.20, 95% CI = 1.20 to 4.03; P _trend = .01) were independently and strongly predictive of increased breast cancer risk. In combination, women with high epithelium-to-stroma proportion and high MBD had substantially higher risk than those with low epithelium-to-stroma proportion and low MBD (OR = 2.27, 95% CI = 1.27 to 4.06; P _trend = .005), particularly among women with nonproliferative (P _trend = .01) vs proliferative (P _trend = .33) BBD.

Conclusion

Among BBD patients, increasing epithelium-to-stroma proportion on BBD biopsies and percent MBD at BBD diagnosis were independently and jointly associated with increasing breast cancer risk. These findings were particularly striking for women with nonproliferative disease (comprising approximately 70% of all BBD patients), for whom relevant predictive biomarkers are lacking.

Background Parenchymal Enhancement at Contrast-Enhanced Spectral Mammography (CESM) as a Breast Cancer Risk Factor

RATIONALE AND OBJECTIVES

To assess the extent of background parenchymal enhancement (BPE) at contrast-enhanced spectral mammography (CESM), association between clinical factors and BPE, and between BPE extent and breast cancer.

MATERIALS AND METHODS

This study included 516 women who underwent CESM imaging for screening and diagnostic purposes between 2012 and 2015 in a single center. BPE at CESM images was retrospectively, independently and blindly graded by six experienced radiologists using the following scale: minimal, mild, moderate, or marked. Agreement between readers was estimated using Kendall's W coefficient of concordance. Associations between clinical factors and BPE, and between BPE and breast cancer were examined using generalized estimating equations. Association between BPE and breast cancer was assessed for the whole study group, and for the screening population separately.

RESULTS

Most women underwent CESM for breast cancer screening (424/516, 82.2%). Mean age was 53 years, the majority had dense breasts (50.4-94%, depending on the reviewer), and minimal to mild BPE (75.8-89.9%). A total of 53/516 women had breast cancer. Overall concordance (W) values between the readers were 0.611 for breast density and 0.789 on BPE. Increased breast density and younger age were positive predictors for increased BPE (odds ratio [OR] 4.07, 95% confidence interval [CI] 2.32-7.14, p < 0.001; OR 2.88, 95% CI 1.87-4.42, p < 0.001, respectively). Breast radiation therapy was a negative predictor for BPE (OR 0.13, 95% CI 0.06-0.31, p < 0.001). Women with increased BPE had increased odds for breast cancer (OR 2.24, 95% CI 1.23-4.09, p = 0.008). This result was consistent when screening cases were analyzed separately (OR 6.27, 95% CI 2.38-16.53, p < 0.001).

CONCLUSION

BPE at CESM was associated with breast density. Women with increased BPE had increased odds for breast cancer, independently of other potential risk factors.

Comparison of BSGI, MRI, mammography, and ultrasound for the diagnosis of breast lesions and their correlations with specific molecular subtypes in Chinese women

Background

Breast cancer is a leading cause of cancer in females, and is the second leading cancer-related cause of death in this group. Early diagnosis is essential to breast cancer to be effectively treated, and ultrasound, mammography, and magnetic resonance imaging (MRI) represent three key technologies that are utilized for the diagnosis of breast lesions. Breast-specific gamma imaging (BSGI) is an approach to molecular breast imaging that allows for high-resolution radio-imaging that is not adversely impacted by breast tissue density. This study was therefore designed to assess the relative diagnostic efficacy of BSGI, MRI, mammography, and ultrasound in different molecular subtypes of breast cancer among Chinese women.

Methods

Diagnostic findings from 390 patients that had undergone diagnosis and treatment in our breast surgery department were retrospectively reviewed. Patients had been diagnosed via BSGI, mammography, ultrasound, and MRI. The diagnostic efficacy of these different imaging modalities and their associated biological characteristics were compared in the present study.

Results

A total of 229 of these 390 patients (58.7%) were diagnosed with malignant breast cancer, with the remaining 161 (41.3%) cases having been found to be benign. BSGI, MRI, mammography, and ultrasound yielded respective sensitivity values of 91.7, 92.5, 77.3, and 82.1%, while the respective specificity values for these imaging modalities were 80.7, 69.7, 74.5, and 70.8%. For lesions > 1 cm, BSGI offered a sensitivity of 92.5%. For mammographic breast density A, B, C, and D, BSGI offered a sensitivity of 93.3, 94.0, 91.5, and 89.3%, respectively. BSGI also yielded a significantly higher lesion-to-normal lesion ratio (LNR) for malignant lesions relative to benign lesions (2.76 ± 1.32 vs 1.46 ± 0.49).

Conclusions

These findings confirm that BSGI is highly sensitive and is superior to mammography in the detection and diagnosis of ductal carcinomas in situ (DCIS). Such diagnostic efficacy can be further improved by using BSGI as an auxiliary modality to mammography and ultrasound, potentially improving the reliability of breast lesion diagnosis, thereby ensuring that patients receive rapid and effective treatment without the risk of misdiagnosis or unnecessary surgical treatment.

Robust Estimation of Breast Cancer Incidence Risk in Presence of Incomplete or Inaccurate Information

Purpose:

To evaluate the robustness of multiple machine learning classifiers for breast cancer risk estimation in the presence of incomplete or inaccurate information.

Data and methods:

Open data for this study was obtained from the BCSC Data Resource (http://breastscreening.cancer.gov/). We conducted two ablation-type experiments to compare the robustness of different classifiers where we randomly switched known information to missing with a missing probability of p_m in one experiment, and randomly corrupted the existing information with a probability of p_c in another experiment. We considered three prominent machine-learning classifiers such as Logistic regression (LR), Random Forests (RF) and a custom Neural Network (NN) architecture and compared their degradation of discrimination performance as a function of increasing probability of missing or inaccurate data.

Results:

LR, RF and custom NN resulted in an Area Under Curve (AUC) of 0.645, 0.643 and 0.649, respectively, on a test set with 500,000 total observations. When we manipulated the data by varying probabilities p_m and p_c from 0 to 1, NN resulted in better performance in terms of AUC compared to RF and LR as long as less than half the data was missing/inaccurate (that is, for values of p_m < 0.5 and p_c < 0.5). However, for missing (p_m) or corruption (p_c) probabilities above 0.5, LR gave similar performance as the custom NN. RF resulted in overall poorer performance when the data had additional missing or incorrect entries.

Conclusion:

In cases where the input information is missing or inaccurate, our experiments show that the proposed custom NN provides reliable risk estimates in medical datasets like BCSC. These results are particularly important in health care applications where not every attribute of the individual participant might be available.

Comparison of 99mTc-MIBI scintigraphy, ultrasound, and mammography for the diagnosis of BI-RADS 4 category lesions

Background

We sought to determine the diagnostic efficacy of Breast-specific gamma imaging (BSGI) in Chinese women with BI-RADS 4 category lesions and to compare this efficacy to that of ultrasound/mammography.

Methods

We retrospectively analyzed data from 177 women that had undergone BSGI of BI-RADS 4 category lesions originally detected via ultrasound and/or mammography.

Results

Of these 177 cases, 117 (66.1%) were malignant lesions and 60 (33.9%) were benign. The sensitivity, specificity, positive predictive values, and negative predictive values of BSGI were 94.9% (111/117), 78.3% (47/60), 89.5% (111/124), and 88.7% (47/53), respectively. The specificity and positive predictive values for mammography were 48.3% (29/60) and 77.5% (107/138), while for ultrasound they were 53.3% (32/60) and 79.6% (109/137), respectively. The sensitivity and specificity of BSGI for the detection of lesions ≤1 cm in size were 90.9% (10/11) and 88.0% (22/25), respectively, while for breast lesions >1 cm in size these values were 94.3% (100/106) and 71.4% (25/35), respectively. In addition, BSGI sensitivity and specificity values for dense breast tissue were 94.0% (79/84) and 78.0% (39/50), respectively, whereas for non-dense breast tissue these vales were 97.0% (32/33) and 80.0% (8/10), respectively. The sensitivity of BSGI for invasive ductal carcinomas (IDC) and ductal carcinomas in situ (DCIS) was 98.9% (95/96) and 75.0% (9/12), respectively. The tumor to normal tissue ratio of BSGI for malignant lesions was significantly higher than for benign lesions (2.18 ± 1.17 vs 1.66 ± 0.40, t = 7.56, P<0.05).

Conclusions

These results indicate that BSGI is highly sensitive for the detection of such lesions, achieving good positive/negative predictive values. This suggests that for IDC in particular, BSGI is superior to ultrasound and mammography for the diagnosis of BI-RADS 4 category lesions, although this was less apparent for the diagnosis of DCIS lesions. BSGI exhibited excellent performance in dense breast tissue and for the detection of lesions ≤1 cm in size.

Towards a more precise and individualized assessment of breast cancer risk

Many clinically based models are available for breast cancer risk assessment; however, these models are not particularly useful at the individual level, despite being designed with that intent. There is, therefore, a significant need for improved, precise individualized risk assessment. In this Research Perspective, we highlight commonly used clinical risk assessment models and recent scientific advances to individualize risk assessment using precision biomarkers. Genome-wide association studies have identified >100 single nucleotide polymorphisms (SNPs) associated with breast cancer risk, and polygenic risk scores (PRS) have been developed by several groups using this information. The ability of a PRS to improve risk assessment is promising; however, validation in both genetically and ethnically diverse populations is needed. Additionally, novel classes of biomarkers, such as microRNAs, may capture clinically relevant information based on epigenetic regulation of gene expression. Our group has recently identified a circulating-microRNA signature predictive of long-term breast cancer in a prospective cohort of high-risk women. While progress has been made, the importance of accurate risk assessment cannot be understated. Precision risk assessment will identify those women at greatest risk of developing breast cancer, thus avoiding overtreatment of women at average risk and identifying the most appropriate candidates for chemoprevention or surgical prevention.

Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review

According to the American Cancer Society's forecasts for 2019, there will be about 268,600 new cases in the United States with invasive breast cancer in women, about 62,930 new noninvasive cases, and about 41,760 death cases from breast cancer. As a result, there is a high demand for breast imaging specialists as indicated in a recent report for the Institute of Medicine and National Research Council. One way to meet this demand is through developing Computer-Aided Diagnosis (CAD) systems for breast cancer detection and diagnosis using mammograms. This study aims to review recent advancements and developments in CAD systems for breast cancer detection and diagnosis using mammograms and to give an overview of the methods used in its steps starting from preprocessing and enhancement step and ending in classification step. The current level of performance for the CAD systems is encouraging but not enough to make CAD systems standalone detection and diagnose clinical systems. Unless the performance of CAD systems enhanced dramatically from its current level by enhancing the existing methods, exploiting new promising methods in pattern recognition like data augmentation in deep learning and exploiting the advances in computational power of computers, CAD systems will continue to be a second opinion clinical procedure.

Design and clinical validation of a software program for automated measurement of mammographic breast density

Background

Mammographic breast density is an important predictor of breast cancer, but its measurement has limitations related to subjectivity of visual evaluation or to difficult access for automatic volumetric measurement methods. Herein, we describe the design and clinical validation of Aguida, a software program for automated quantification of breast density from flat mammography images.

Materials and methods

The software program was developed in MatLab. After image segmentation separating the background from the breast image, the operator positions a cursor defining a region of interest on the pectoralis major muscle from the mediolateral oblique view. Then, in the craniocaudal view, the threshold for separation of the dense tissue is based on the optical density of the pectoral muscle, and the proportion of dense tissue is calculated by the program. Mammograms obtained from 2 different occasions in 291 women were used for clinical evaluation.

Results

The intraclass correlation coefficient (ICC) between breast density measurements by the software and by a radiologist was 0.96, with a bias of only 0.67 percentage points and a 95% limit of agreement of 13.5 percentage points; the ICC was 0.94 in the interobserver reliability assessment by two radiologists with different experience; and the ICC was 0.98 in the intraobserver reliability assessment. The distribution among the density classes was close to the values obtained with the volumetric software.

Conclusions

Measurement of breast density with the Aguida program from flat mammography images showed high agreement with the visual determination by radiologists, and high inter- and intra-observer reliability.

Epidemiologic Risk Factors in a Comparison of a Barrett Esophagus Registry (BarrettNET) and a Case-Control Population in Germany

Endoscopic screening for Barrett's esophagus as the major precursor lesion for esophageal adenocarcinoma is mostly offered to patients with symptoms of gastroesophageal reflux disease (GERD). However, other epidemiologic risk factors might affect the development of Barrett's esophagus and esophageal adenocarcinoma. Therefore, efforts to improve the efficiency of screening to find the Barrett's esophagus population "at risk" compared with the normal population are needed. In a cross-sectional analysis, we compared 587 patients with Barrett's esophagus from the multicenter German BarrettNET registry to 1976 healthy subjects from the population-based German KORA cohort, with and without GERD symptoms. Data on demographic and lifestyle factors, including age, gender, smoking, alcohol consumption, body mass index, physical activity, and symptoms were collected in a standardized epidemiologic survey. Increased age, male gender, smoking, heavy alcohol consumption, low physical activity, low health status, and GERD symptoms were significantly associated with Barrett's esophagus. Surprisingly, among patients stratified for GERD symptoms, these associations did not change. Demographic, lifestyle, and clinical factors as well as GERD symptoms were associated with Barrett's esophagus development in Germany, suggesting that a combination of risk factors could be useful in developing individualized screening efforts for patients with Barrett's esophagus and GERD in Germany.

Transposon Insertion Mutagenesis in Mice for Modeling Human Cancers: Critical Insights Gained and New Opportunities

Transposon mutagenesis has been used to model many types of human cancer in mice, leading to the discovery of novel cancer genes and insights into the mechanism of tumorigenesis. For this review, we identified over twenty types of human cancer that have been modeled in the mouse using Sleeping Beauty and piggyBac transposon insertion mutagenesis. We examine several specific biological insights that have been gained and describe opportunities for continued research. Specifically, we review studies with a focus on understanding metastasis, therapy resistance, and tumor cell of origin. Additionally, we propose further uses of transposon-based models to identify rarely mutated driver genes across many cancers, understand additional mechanisms of drug resistance and metastasis, and define personalized therapies for cancer patients with obesity as a comorbidity.

Exploring Explanatory Models of Risk in Breast Cancer Risk Counseling Discussions: NSABP/NRG Oncology Decision-Making Project 1

BACKGROUND

Explanatory models represent patient understanding of etiology, pathophysiology, illness, symptoms, and treatments, but little attention has been paid to how they are used by patients "at risk" for future disease.

OBJECTIVE

The aims of this study were to elucidate what constitutes an explanatory model of risk and to describe explanatory models of risk related to developing breast cancer.

METHODS

Thirty qualitative interviews with women identified as at an increased risk for breast cancer were conducted. Interviews were coded to identify domains of explanatory models of risk using a priori codes derived from the explanatory model of illness framework. Within each domain, a grounded thematic analysis described participants' explanatory models related to breast cancer risk.

RESULTS

The domains of treatment and etiology remained similar in a risk context compared with illness, whereas course of illness, symptoms, and pathophysiology differed. We identified a new, integrative concept relative to other domains within explanatory models of risk: social comparisons, which was dominant in risk perhaps due to the lack of physical experiences associated with being "at risk."

CONCLUSIONS

Developing inclusive understandings of risk and its treatment is key to developing a framework for the care of high-risk patients that is both evidence based and sensitive to patient preferences.

IMPLICATIONS FOR PRACTICE

The concept of "social comparisons" can assist healthcare providers in understanding women's decision making under conditions of risk. Ensuring that healthcare providers understand patient perceptions of risk is important because it relates to patient decision making, particularly due to an increasing focus on risk assessment in cancer.

A way forward for cancer prevention therapy: personalized risk assessment

Cancer is characterized by genetic and molecular aberrations whose number and complexity increase dramatically as cells progress along the spectrum of carcinogenesis. The pharmacologic application of agents in the context of a lower burden of dysregulated cellular processes constitutes an efficient strategy to enhance therapeutic efficacy, and underlies the rationale for using cancer prevention agents in high-risk populations. A longstanding barrier to implementing this strategy is that the risk in the general population is low for any given cancer, many people would have to be treated in order to benefit a few. Therefore, identifying and treating high-risk individuals will improve the risk: benefit ratio. Currently, risk is defined by considering a relatively low number of factors. A strategy that considers multiple factors has the ability to define a much-higher-risk cohort than the general population. This article will review the rationale for evaluating multiple risk factors so as to identify individuals at highest risk. It will use breast and lung cancer as examples, will describe currently available risk assessment tools, and will discuss ongoing efforts to expand the impact of this approach. The high potential of this strategy to provide a way forward for developing cancer prevention therapy will be highlighted.

Editor's Choice: Deliberative and non-deliberative effects of descriptive and injunctive norms on cancer screening behaviors among African Americans

Objective: Two longitudinal studies examined whether effects of subjective norms on secondary cancer prevention behaviors were stronger and more likely to non-deliberative (i.e., partially independent of behavioral intentions) for African Americans (AAs) compared to European Americans (EAs), and whether the effects were moderated by racial identity. Design: Study 1 examined between-race differences in predictors of physician communication following receipt of notifications about breast density. Study 2 examined predictors of prostate cancer screening among AA men who had not been previously screened.Main Outcome Measures: Participants' injunctive and descriptive normative perceptions; racial identity (Study 2); self-reported physician communication (Study 1) and PSA testing (Study 2) behaviors at follow up. Results: In Study 1, subjective norms were significantly associated with behaviors for AAs, but not for EAs. Moreover, there were significant non-deliberative effects of norms for AAs. In Study 2, there was further evidence of non-deliberative effects of subjective norms for AAs. Non-deliberative effects of descriptive norms were stronger for AAs who more strongly identified with their racial group. Conclusion: Subjective norms, effects of which are non-deliberative and heightened by racial identity, may be a uniquely robust predictor of secondary cancer prevention behaviors for AAs. Implications for targeted screening interventions are discussed.

Medication Use to Reduce Risk of Breast Cancer: US Preventive Services Task Force Recommendation Statement

IMPORTANCE

Breast cancer is the most common nonskin cancer among women in the United States and the second leading cause of cancer death. The median age at diagnosis is 62 years, and an estimated 1 in 8 women will develop breast cancer at some point in their lifetime. African American women are more likely to die of breast cancer compared with women of other races.

OBJECTIVE

To update the 2013 US Preventive Services Task Force (USPSTF) recommendation on medications for risk reduction of primary breast cancer.

EVIDENCE REVIEW

The USPSTF reviewed evidence on the accuracy of risk assessment methods to identify women who could benefit from risk-reducing medications for breast cancer, as well as evidence on the effectiveness, adverse effects, and subgroup variations of these medications. The USPSTF reviewed evidence from randomized trials, observational studies, and diagnostic accuracy studies of risk stratification models in women without preexisting breast cancer or ductal carcinoma in situ.

FINDINGS

The USPSTF found convincing evidence that risk assessment tools can predict the number of cases of breast cancer expected to develop in a population. However, these risk assessment tools perform modestly at best in discriminating between individual women who will or will not develop breast cancer. The USPSTF found convincing evidence that risk-reducing medications (tamoxifen, raloxifene, or aromatase inhibitors) provide at least a moderate benefit in reducing risk for invasive estrogen receptor-positive breast cancer in postmenopausal women at increased risk for breast cancer. The USPSTF found that the benefits of taking tamoxifen, raloxifene, and aromatase inhibitors to reduce risk for breast cancer are no greater than small in women not at increased risk for the disease. The USPSTF found convincing evidence that tamoxifen and raloxifene and adequate evidence that aromatase inhibitors are associated with small to moderate harms. Overall, the USPSTF determined that the net benefit of taking medications to reduce risk of breast cancer is larger in women who have a greater risk for developing breast cancer.

CONCLUSIONS AND RECOMMENDATION

The USPSTF recommends that clinicians offer to prescribe risk-reducing medications, such as tamoxifen, raloxifene, or aromatase inhibitors, to women who are at increased risk for breast cancer and at low risk for adverse medication effects. (B recommendation) The USPSTF recommends against the routine use of risk-reducing medications, such as tamoxifen, raloxifene, or aromatase inhibitors, in women who are not at increased risk for breast cancer. (D recommendation) This recommendation applies to asymptomatic women 35 years and older, including women with previous benign breast lesions on biopsy (such as atypical ductal or lobular hyperplasia and lobular carcinoma in situ). This recommendation does not apply to women who have a current or previous diagnosis of breast cancer or ductal carcinoma in situ.

Medication Use for the Risk Reduction of Primary Breast Cancer in Women: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force

IMPORTANCE

Medications to reduce risk of breast cancer are effective for women at increased risk but also cause adverse effects.

OBJECTIVE

To update the 2013 US Preventive Services Task Force systematic review on medications to reduce risk of primary (first diagnosis) invasive breast cancer in women.

DATA SOURCES

Cochrane Central Register of Controlled Trials and Database of Systematic Reviews, EMBASE, and MEDLINE (January 1, 2013, to February 1, 2019); manual review of reference lists.

STUDY SELECTION

Discriminatory accuracy studies of breast cancer risk assessment methods; randomized clinical trials of tamoxifen, raloxifene, and aromatase inhibitors for primary breast cancer prevention; studies of medication adverse effects.

DATA EXTRACTION AND SYNTHESIS

Investigators abstracted data on methods, participant characteristics, eligibility criteria, outcome ascertainment, and follow-up. Results of individual trials were combined by using a profile likelihood random-effects model.

MAIN OUTCOMES AND MEASURES

Probability of breast cancer in individuals (area under the receiver operating characteristic curve [AUC]); incidence of breast cancer, fractures, thromboembolic events, coronary heart disease events, stroke, endometrial cancer, and cataracts; and mortality.

RESULTS

A total of 46 studies (82 articles [>5 million participants]) were included. Eighteen risk assessment methods in 25 studies reported low accuracy in predicting the probability of breast cancer in individuals (AUC, 0.55-0.65). In placebo-controlled trials, tamoxifen (risk ratio [RR], 0.69 [95% CI, 0.59-0.84]; 4 trials [n = 28 421]), raloxifene (RR, 0.44 [95% CI, 0.24-0.80]; 2 trials [n = 17 806]), and the aromatase inhibitors exemestane and anastrozole (RR, 0.45 [95% CI, 0.26-0.70]; 2 trials [n = 8424]) were associated with a lower incidence of invasive breast cancer. Risk for invasive breast cancer was higher for raloxifene than tamoxifen in 1 trial after long-term follow-up (RR, 1.24 [95% CI, 1.05-1.47]; n = 19 747). Raloxifene was associated with lower risk for vertebral fractures (RR, 0.61 [95% CI, 0.53-0.73]; 2 trials [n = 16 929]) and tamoxifen was associated with lower risk for nonvertebral fractures (RR, 0.66 [95% CI, 0.45-0.98]; 1 trial [n = 13 388]) compared with placebo. Tamoxifen and raloxifene were associated with increased thromboembolic events compared with placebo; tamoxifen was associated with more events than raloxifene. Tamoxifen was associated with higher risk of endometrial cancer and cataracts compared with placebo. Symptomatic effects (eg, vasomotor, musculoskeletal) varied by medication.

CONCLUSIONS AND RELEVANCE

Tamoxifen, raloxifene, and aromatase inhibitors were associated with lower risk of primary invasive breast cancer in women but also were associated with adverse effects that differed between medications. Risk stratification methods to identify patients with increased breast cancer risk demonstrated low accuracy.

Improving the diagnostic accuracy of a stratified screening strategy by identifying the optimal risk cutoff

BACKGROUND

The American Cancer Society (ACS) suggests using a stratified strategy for breast cancer screening. The strategy includes assessing risk of breast cancer, screening women at high risk with both MRI and mammography, and screening women at low risk with mammography alone. The ACS chose their cutoff for high risk using expert consensus.

METHODS

We propose instead an analytic approach that maximizes the diagnostic accuracy (AUC/ROC) of a risk-based stratified screening strategy in a population. The inputs are the joint distribution of screening test scores, and the odds of disease, for the given risk score. Using the approach for breast cancer screening, we estimated the optimal risk cutoff for two different risk models: the Breast Cancer Screening Consortium (BCSC) model and a hypothetical model with much better discriminatory accuracy. Data on mammography and MRI test score distributions were drawn from the Magnetic Resonance Imaging Screening Study Group.

RESULTS

A risk model with an excellent discriminatory accuracy (c-statistic [Formula: see text]) yielded a reasonable cutoff where only about 20% of women had dual screening. However, the BCSC risk model (c-statistic [Formula: see text]) lacked the discriminatory accuracy to differentiate between women who needed dual screening, and women who needed only mammography.

CONCLUSION

Our research provides a general approach to optimize the diagnostic accuracy of a stratified screening strategy in a population, and to assess whether risk models are sufficiently accurate to guide stratified screening. For breast cancer, most risk models lack enough discriminatory accuracy to make stratified screening a reasonable recommendation.

Between-Race Differences in Processes Predicting Physician Communication for African American and European American Recipients of Breast Density Notifications

BACKGROUND

Breast density notification laws mandate reporting of dense breast to applicable women. The same psychological and systemic barriers that yield between-race differences in mammography use will probably yield between-race differences in women's psychological and behavioral responses to breast density notifications.

PURPOSE

We used the theory of planned behavior as a framework to examine between-race differences in the likelihood of following-up with physicians after receiving breast density notifications and to examine differences in African American and Caucasian American women's behavioral decision-making processes.

METHODS

A subset of 212 African American and Caucasian American women who participated in an initial and follow-up survey examining responses to breast density notifications were examined for this study. Participants reported background and demographic measures, psychological responses to receiving notifications, and planned behavior measures related to following up with physicians approximately 2 weeks after receiving their mammogram reports. Participants self-reported their behaviors 3 months later.

RESULTS

There were no between-race differences in self-reported physician communication; however, there were differences in processes that predicted behavior. For Caucasian American women, behavioral intentions, education, and income predicted behaviors. Instead of intentions, group-based medical suspicion, confusion, breast cancer worry, and breast density anxiety predicted behaviors for African American women.

CONCLUSIONS

Behavioral decision-making processes for Caucasian American women were in line with well-validated theoretical predictions. For African American women, race-related medical suspicion, prior breast density awareness, and emotional responses to breast density notifications predicted behavior. The results highlight the need to focus on racially distinct psychological targets when designing interventions to support guideline concordant behavioral decisions among women who receive breast density notifications.

A systematic review and quality assessment of individualised breast cancer risk prediction models

Background

Individualised breast cancer risk prediction models may be key for planning risk-based screening approaches. Our aim was to conduct a systematic review and quality assessment of these models addressed to women in the general population.

Methods

We followed the Cochrane Collaboration methods searching in Medline, EMBASE and The Cochrane Library databases up to February 2018. We included studies reporting a model to estimate the individualised risk of breast cancer in women in the general population. Study quality was assessed by two independent reviewers. Results are narratively summarised.

Results

We included 24 studies out of the 2976 citations initially retrieved. Twenty studies were based on four models, the Breast Cancer Risk Assessment Tool (BCRAT), the Breast Cancer Surveillance Consortium (BCSC), the Rosner & Colditz model, and the International Breast Cancer Intervention Study (IBIS), whereas four studies addressed other original models. Four of the studies included genetic information. The quality of the studies was moderate with some limitations in the discriminative power and data inputs. A maximum AUROC value of 0.71 was reported in the study conducted in a screening context.

Conclusion

Individualised risk prediction models are promising tools for implementing risk-based screening policies. However, it is a challenge to recommend any of them since they need further improvement in their quality and discriminatory capacity.

A Case-Control Study to Add Volumetric or Clinical Mammographic Density into the Tyrer-Cuzick Breast Cancer Risk Model

Background

Accurate breast cancer risk assessment for women attending routine screening is needed to guide screening and preventive interventions. We evaluated the accuracy of risk predictions from both visual and volumetric mammographic density combined with the Tyrer-Cuzick breast cancer risk model.

Methods

A case-control study (474 patient participants and 2243 healthy control participants) of women aged 40–79 years was performed using self-reported classical risk factors. Breast density was measured by using automated volumetric software and Breast Imaging and Reporting Data System (BI-RADS) density categories. Odds ratios (95% CI) were estimated by using logistic regression, adjusted for age, demographic factors, and 10-year risk from the Tyrer-Cuzick model, for a change from the 25^th to 75^th percentile of the adjusted percent density distribution in control participants (IQ-OR).

Results

After adjustment for classical risk factors in the Tyrer-Cuzick model, age, and body mass index (BMI), BI-RADS density had an IQ-OR of 1.55 (95% CI = 1.33 to 1.80) compared with 1.40 (95% CI = 1.21 to 1.60) for volumetric percent density. Fibroglandular volume (IQ-OR = 1.28, 95% CI = 1.12 to 1.47) was a weaker predictor than was BI-RADS density (P_diff = 0.014) or volumetric percent density (P_diff = 0.065). In this setting, 4.8% of women were at high risk (8% + 10-year risk), using the Tyrer-Cuzick model without density, and 7.1% (BI-RADS) compared with 6.8% (volumetric) when combined with density.

Conclusion

The addition of volumetric and visual mammographic density measures to classical risk factors improves risk stratification. A combined risk could be used to guide precision medicine, through risk-adapted screening and prevention strategies.

Reminders of cancer risk and pain catastrophizing: relationships with cancer worry and perceived risk in women with a first-degree relative with breast cancer

First-degree relatives of women with breast cancer may experience increased worry or perceived risk when faced with reminders of their own cancer risk. Worry and risk reminders may include physical symptoms (e.g., persistent breast pain) and caregiving experiences. Women who engage in pain catastrophizing may be particularly likely to experience increased distress when risk reminders are present. We examined the degree to which persistent breast pain and experience as a cancer caregiver were related to cancer worry and perceived risk in first-degree relatives of women with breast cancer (N = 85) and how catastrophic thoughts about breast pain could impact these relationships. There was a significant interaction between persistent breast pain and pain catastrophizing in predicting cancer worry (p = .03); among women who engaged in pain catastrophizing, cancer worry remained high even in the absence of breast pain. Pain catastrophizing also moderated the relationships between caregiving involvement and cancer worry (p = .003) and perceived risk (p = .03). As the degree of caregiving responsibility increased, cancer worry and perceived risk increased for women who engaged in pain catastrophizing; levels of cancer worry and perceived risk remained low and stable for women who did not engage in pain catastrophizing regardless of caregiving experience. The results suggest that first-degree relatives of breast cancer survivors who engage in pain catastrophizing may experience greater cancer worry and perceived risk and may benefit from interventions aimed at reducing catastrophic thoughts about pain.

Comparison of Questionnaire-Based Breast Cancer Prediction Models in the Nurses' Health Study

BACKGROUND

The Gail model and the model developed by Tyrer and Cuzick are two questionnaire-based approaches with demonstrated ability to predict development of breast cancer in a general population.

METHODS

We compared calibration, discrimination, and net reclassification of these models, using data from questionnaires sent every 2 years to 76,922 participants in the Nurses' Health Study between 1980 and 2006, with 4,384 incident invasive breast cancers identified by 2008 (median follow-up, 24 years; range, 1-28 years). In a random one third sample of women, we also compared the performance of these models with predictions from the Rosner-Colditz model estimated from the remaining participants.

RESULTS

Both the Gail and Tyrer-Cuzick models showed evidence of miscalibration (Hosmer-Lemeshow P < 0.001 for each) with notable (P < 0.01) overprediction in higher-risk women (2-year risk above about 1%) and underprediction in lower-risk women (risk below about 0.25%). The Tyrer-Cuzick model had slightly higher C-statistics both overall (P < 0.001) and in age-specific comparisons than the Gail model (overall C, 0.63 for Tyrer-Cuzick vs. 0.61 for the Gail model). Evaluation of net reclassification did not favor either model. In the one third sample, the Rosner-Colditz model had better calibration and discrimination than the other two models. All models had C-statistics <0.60 among women ages ≥70 years.

CONCLUSIONS

Both the Gail and Tyrer-Cuzick models had some ability to discriminate breast cancer cases and noncases, but have limitations in their model fit.

IMPACT

Refinements may be needed to questionnaire-based approaches to predict breast cancer in older and higher-risk women.

Background parenchymal enhancement on breast MRI: A comprehensive review

The degree of normal fibroglandular tissue that enhances on breast MRI, known as background parenchymal enhancement (BPE), was initially described as an incidental finding that could affect interpretation performance. While BPE is now established to be a physiologic phenomenon that is affected by both endogenous and exogenous hormone levels, evidence supporting the notion that BPE frequently masks breast cancers is limited. However, compelling data have emerged to suggest BPE is an independent marker of breast cancer risk and breast cancer treatment outcomes. Specifically, multiple studies have shown that elevated BPE levels, measured qualitatively or quantitatively, are associated with a greater risk of developing breast cancer. Evidence also suggests that BPE could be a predictor of neoadjuvant breast cancer treatment response and overall breast cancer treatment outcomes. These discoveries come at a time when breast cancer screening and treatment have moved toward an increased emphasis on targeted and individualized approaches, of which the identification of imaging features that can predict cancer diagnosis and treatment response is an increasingly recognized component. Historically, researchers have primarily studied quantitative tumor imaging features in pursuit of clinically useful biomarkers. However, the need to segment less well-defined areas of normal tissue for quantitative BPE measurements presents its own unique challenges. Furthermore, there is no consensus on the optimal timing on dynamic contrast-enhanced MRI for BPE quantitation. This article comprehensively reviews BPE with a particular focus on its potential to increase precision approaches to breast cancer risk assessment, diagnosis, and treatment. It also describes areas of needed future research, such as the applicability of BPE to women at average risk, the biological underpinnings of BPE, and the standardization of BPE characterization. Level of Evidence: 3 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2020;51:43-61.

Breast cancer risk prediction in women aged 35-50 years: impact of including sex hormone concentrations in the Gail model

Background

Models that accurately predict risk of breast cancer are needed to help younger women make decisions about when to begin screening. Premenopausal concentrations of circulating anti-Müllerian hormone (AMH), a biomarker of ovarian reserve, and testosterone have been positively associated with breast cancer risk in prospective studies. We assessed whether adding AMH and/or testosterone to the Gail model improves its prediction performance for women aged 35–50.

Methods

In a nested case-control study including ten prospective cohorts (1762 invasive cases/1890 matched controls) with pre-diagnostic serum/plasma samples, we estimated relative risks (RR) for the biomarkers and Gail risk factors using conditional logistic regression and random-effects meta-analysis. Absolute risk models were developed using these RR estimates, attributable risk fractions calculated using the distributions of the risk factors in the cases from the consortium, and population-based incidence and mortality rates. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory accuracy of the models with and without biomarkers.

Results

The AUC for invasive breast cancer including only the Gail risk factor variables was 55.3 (95% CI 53.4, 57.1). The AUC increased moderately with the addition of AMH (AUC 57.6, 95% CI 55.7, 59.5), testosterone (AUC 56.2, 95% CI 54.4, 58.1), or both (AUC 58.1, 95% CI 56.2, 59.9). The largest AUC improvement (4.0) was among women without a family history of breast cancer.

Conclusions

AMH and testosterone moderately increase the discriminatory accuracy of the Gail model among women aged 35–50. We observed the largest AUC increase for women without a family history of breast cancer, the group that would benefit most from improved risk prediction because early screening is already recommended for women with a family history.

Electronic supplementary material

The online version of this article (10.1186/s13058-019-1126-z) contains supplementary material, which is available to authorized users.

Modeling risk assessment for breast cancer in symptomatic women: a Saudi Arabian study

Background

Despite the continuing increase in the breast cancer incidence rate among Saudi Arabian women, no breast cancer risk-prediction model is available in this population. The aim of this research was to develop a risk-assessment tool to distinguish between high risk and low risk of breast cancer in a sample of Saudi women who were screened for breast cancer.

Methods

A retrospective chart review was conducted on symptomatic women who underwent breast mass biopsies between September 8, 2015 and November 8, 2017 at King Abdulaziz Medical City, Riyadh, Saudi Arabia.

Results

A total of 404 (63.8%) malignant breast biopsies and 229 (36.2%) benign breast biopsies were analyzed. Women ≥40 years old (aOR: 6.202, CI 3.497–11.001, P=0.001), hormone-replacement therapy (aOR 24.365, 95% CI 8.606–68.987, P=0.001), postmenopausal (aOR 3.058, 95% CI 1.861–5.024, P=0.001), and with a family history of breast cancer (aOR 2.307, 95% CI 1.142–4.658, P=0.020) were independently associated with an increased risk of breast cancer. This model showed an acceptable fit and had area under the receiver-operating characteristic curve of 0.877 (95% CI 0.851–0.903), with optimism-corrected area under the curve of 0.865.

Conclusion

The prediction model developed in this study has a high ability in predicting increased breast cancer risk in our facility. Combining information on age, use of hormone therapy, postmenopausal status, and family history of breast cancer improved the degree of discriminatory accuracy of breast cancer prediction. Our risk model may assist in initiating population-screening programs and prompt clinical decision making to manage cases and prevent unfavorable outcomes.

Examining Mammography Use by Breast Cancer Risk, Race, Nativity, and Socioeconomic Status

Minority and foreign-born women report lower rates of mammograms compared to non-Hispanic white, U.S.-born women, even though they have increased risk for developing breast cancer. We examine disparities in mammography across breast cancer risk groups and determine whether disparities are explained by socioeconomic factors. Propensity score methodology was used to classify individuals from the 2000, 2005, and 2010 National Health Interview Survey according to their risk for developing breast cancer. Logistic regression models were used to predict the likelihood of mammography. Compared to non-Hispanic white women, Mexicans, Asians and "other" racial/ethnic origins were less likely to have undergone a mammogram. After controlling for breast cancer risk, socioeconomic status and health care resources, Mexican, Cuban, Dominican, Central American, Black, and foreign-born women had an increased likelihood of receiving a mammogram. Using propensity scores makes an important contribution to the literature on sub-population differences in the use of mammography by addressing the confounding risk of breast cancer. While other factors related to ethnicity or culture may account for lower breast cancer screening rates in Asian and Mexican women, these findings highlight the need to consider risk, in addition to socioeconomic factors, that may pose barriers to screening in determining mammography disparities.

AACR 2019 — Congrès de l’association américaine de recherche contre le cancer

Dans ce numéro spécial de la revueOncologie, les principaux points discutés au congrès de l’Association américaine pour la recherche sur le cancer (AACR) sont rapportés. L’objectif ici est de présenter de manière concise des exposés qui méritent une attention toute particulière. Le programme de la réunion de l’AACR de cette année, qui a eu lieu à Atlanta, a couvert les dernières découvertes de tout le spectre de la recherche sur le cancer — des sciences de la population à la prévention ; biologie du cancer, études translationnelles et cliniques ; à la survie et à la défense des droits — et souligne le travail des meilleurs esprits en matière de recherche et de médecine d’institutions du monde entier. Le congrès qui a duré cinq jours a proposé un programme multidisciplinaire couvrant tous les aspects de la recherche sur le cancer depuis ses bases fondamentales jusqu’à ses applications translationnelles et cliniques. Grâce à notre compréhension accrue des bases moléculaires du cancer, de nombreuses thérapies ciblées nouvelles ont émergé. Ainsi, notre compréhension sur la façon dont les tumeurs échappent aux attaques du système immunitaire a conduit au développement de nouvelles thérapies. Compte tenu de l’importance accrue de l’immunothérapie dans le traitement du cancer, nous présentons ici les dernières avancées dans ce domaine. Enfin, d’autres approches telles que l’étude du microbiome, l’épigénétique et l’intelligence artificielle comme un outil dans la recherche sur le cancer ont aussi été discutées au congrès de l’AACR 2019.

Long-term risk of screen-detected and interval breast cancer after false-positive results at mammography screening: joint analysis of three national cohorts

BACKGROUND

We assessed the long-term risk of screen-detected and interval breast cancer in women with a first or second false-positive screening result.

METHODS

Joint analysis had been performed using individual-level data from three population-based screening programs in Europe (Copenhagen in Denmark, Norway, and Spain). Overall, 75,513 screened women aged 50-69 years from Denmark (1991-2010), 556,640 from Norway (1996-2008), and 517,314 from Spain (1994-2010) were included. We used partly conditional Cox hazards models to assess the association between false-positive results and the risk of subsequent screen-detected and interval cancer.

RESULTS

During follow-up, 1,149,467 women underwent 3,510,450 screening exams, and 10,623 screen-detected and 5700 interval cancers were diagnosed. Compared to women with negative tests, those with false-positive results had a two-fold risk of screen-detected (HR = 2.04, 95% CI: 1.93-2.16) and interval cancer (HR = 2.18, 95% CI: 2.02-2.34). Women with a second false-positive result had over a four-fold risk of screen-detected and interval cancer (HR = 4.71, 95% CI: 3.81-5.83 and HR = 4.22, 95% CI: 3.27-5.46, respectively). Women remained at an elevated risk for 12 years after the false-positive result.

CONCLUSIONS

Women with prior false-positive results had an increased risk of screen-detected and interval cancer for over a decade. This information should be considered to design personalised screening strategies based on individual risk.

Application of sandwich spatial estimation method in cancer mapping: A case study for breast cancer mortality in the Chinese mainland, 2005

High-accuracy spatial distribution estimation is crucial for cancer prevention and control. Due to their complicated pathogenic factors, the distributions of many cancers' mortalities appear blocky, and spatial heterogeneity is common. However, most of the commonly used cancer mapping methods are based on spatial autocorrelation theory. Sandwich estimation is a new method based on spatial heterogeneity theory. A modified sandwich estimation method suitable for the estimation of cancer mortality distribution is proposed in this study. The variances of cancer mortality data are used to fuse sandwich estimation results from various auxiliary variables, the feasibility of which in estimating cancer mortality distributions is explained theoretically. The breast cancer (BC) mortality of the Chinese mainland in 2005 was taken as a case, and the accuracy of the modified sandwich estimation method was compared with that of the Hierarchical Bayesian (HB), the Co-Kriging (CK) and the Ordinary Kriging (OK) methods. The accuracy of the modified sandwich estimation method was better than the HB, the CK and the OK methods, and the estimation result from the modified sandwich estimation method was more likely to be acceptable. Therefore, this study represents an attempt to apply the sandwich estimation method to the estimation of cancer mortality distributions with strong spatial heterogeneity, which holds great potential for further application.

Between-Race Differences in Supplemental Breast Cancer Screening Before and After Breast Density Notification Law

Guidelines recommend supplemental breast cancer screening for women at increased breast cancer risk; however, the passage of breast density notification laws may lead to supplemental screening that is incongruent with women's risk. We examined supplemental screening (ie, MRI, ultrasound, or tomosynthesis within 6 months of screening mammogram) among a sample of 2,764 African American (AA) and 691 European American (EA) women with negative or benign screening mammograms for whom we had data from both before and after implementation of breast density notification laws in the state of Michigan. Results indicated a 5-fold increase (from 0.14% to 0.7% of women) in supplemental screening among screen-negative women after passage of the law, driven in large part by an increase in supplemental screening among AA women. Breast density was more predictive of supplemental screening and had a marginally greater explanatory role in between-race differences in supplemental screening after passage of the law. Subgroup analyses (n = 250) indicated that whereas 5-year breast cancer risk was positively associated with supplemental screening before the law and negatively associated after the law for EA women, 5-year risk was not associated with supplemental screening either before or after passage of the law for AA women. Our findings suggest that whereas passage of the breast density notification laws may have motivated supplemental screening among AA women in particular, it lessened the consideration of breast cancer risk in supplemental screening decision making.