Breast Cancer Risk Assessment Research Articles

Risk Assessment Tool of Breast Cancer and Barriers against Breast Self-Examination among Nurses: An Educational Program

Purpose. Breast cancer is considered a huge health problem among women all over the world due to its increased mortality rate. Breast cancer deaths are decreased by 28–34% due to improvement in treatment and early detection. In order to detect breast cancer early and provide the best possible therapy, knowledge and awareness are essential. So, this study aimed to assess risk of breast cancer among nurses, the knowledge and practices of breast self-examination (BSE) and determine its barriers, and implement an educational program for them. Design. A quasiexperimental research design was used. Methods. This study was conducted at two university hospitals (main university hospital and woman’s health hospital). A purposive sample of 280 nurses with age more than thirty-five years were included in the study using three tools, namely, structure interview questionnaire, breast cancer risk assessment tool, and a structured breast cancer knowledge and practice questionnaire. Nurses’ knowledge and practices regarding breast cancer and BSE were first assessed and then they were provided with two educational courses covering theoretical and practical information. One month after the program ended, the nurses’ knowledge and practices were assessed again. Results. About 3.2% of the participants have five years high risk of developing breast cancer according the risk assessment tool, the main barriers of do not do BSE did not find any symptoms and scared from finding any abnormality. There was a significant increase in knowledge and practice of breast self-examination posteducation compared to pre-education (73.2 and 98.2%, respectively). Also, there was a statistically significant difference between pre- and postprogram with a p value of 0.0001. Conclusion. This study concluded that 3.2% of the nurses with high risk of developing breast cancer needed a follow-up; more than half of them do not perform breast self-examination because they think that do not have any symptoms in the breast and the educational program with a significant effect on knowledge and practices of nurses. So, the researchers recommend the nurses need for an empowerment program and support by providing assistance in places of work as done free clinical examination and mammography by female specialist for privacy.

A Review of Artificial Intelligence in Breast Imaging

With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women’s physical and mental health. Early breast cancer screening—through mammography, ultrasound, or magnetic resonance imaging (MRI)—can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.

Breast cancer risk assessment for prescription of Menopausal Hormone Therapy in women who have a family history of breast cancer

Background Menopausal Hormone Therapy (MHT) can alleviate menopausal symptoms but is associated with increased risk of breast cancer (BC). MHT prescription should be preceded by individualised risk/benefit evaluation; however, data outlining the impact of family history alongside different MHT therapeutic approaches are lacking. Aim To quantify the risks associated with MHT use in women with varying BC family histories of i) developing and ii) dying from BC. Design and setting An epidemiological modelling study (UK women). Method We used i) background risks of BC by age and family history, ii) relative risks for BC associated with MHT use, and iii) 10-year BC-specific net mortality rates to model the risk of developing and dying from BC between the ages of 50 and 80 in women with four different BC family history profiles: ‘average’, ‘modest’, ‘intermediate’, and ‘strong’. Results For a woman of ‘average’ family history taking no MHT, the cumulative BC risk (age 50-80) is 9.8%, and the risk of dying from the BC is 1.7%. Five years’ exposure to combined-cyclical MHT (age 50-55) increases these risks to 11.0% and 1.8%, respectively. For a woman with a ‘strong’ family history taking no MHT, the cumulative BC risk is 19.6%, and the risk of dying is 3.2%. With 5 years of MHT (age 50-55), this increases to 22.4% and 3.5%. Conclusion Both family history and MHT are associated with increased risk of BC. Estimates of the risks associated with MHT for women with different family histories can support decision-making around MHT prescription.

Critical Risk Assessment, Diagnosis, and Survival Analysis of Breast Cancer

Breast cancer is the most prevalent type of cancer in women. Risk factor assessment can aid in directing counseling regarding risk reduction and breast cancer surveillance. This research aims to (1) investigate the relationship between various risk factors and breast cancer incidence using the BCSC (Breast Cancer Surveillance Consortium) Risk Factor Dataset and create a prediction model for assessing the risk of developing breast cancer; (2) diagnose breast cancer using the Breast Cancer Wisconsin diagnostic dataset; and (3) analyze breast cancer survivability using the SEER (Surveillance, Epidemiology, and End Results) Breast Cancer Dataset. Applying resampling techniques on the training dataset before using various machine learning techniques can affect the performance of the classifiers. The three breast cancer datasets were examined using a variety of pre-processing approaches and classification models to assess their performance in terms of accuracy, precision, F-1 scores, etc. The PCA (principal component analysis) and resampling strategies produced remarkable results. For the BCSC Dataset, the Random Forest algorithm exhibited the best performance out of the applied classifiers, with an accuracy of 87.53%. Out of the different resampling techniques applied to the training dataset for training the Random Forest classifier, the Tomek Link exhibited the best test accuracy, at 87.47%. We compared all the models used with previously used techniques. After applying the resampling techniques, the accuracy scores of the test data decreased even if the training data accuracy increased. For the Breast Cancer Wisconsin diagnostic dataset, the K-Nearest Neighbor algorithm had the best accuracy with the original dataset test set, at 94.71%, and the PCA dataset test set exhibited 95.29% accuracy for detecting breast cancer. Using the SEER Dataset, this study also explores survival analysis, employing supervised and unsupervised learning approaches to offer insights into the variables affecting breast cancer survivability. This study emphasizes the significance of individualized approaches in the management and treatment of breast cancer by incorporating phenotypic variations and recognizing the heterogeneity of the disease. Through data-driven insights and advanced machine learning, this study contributes significantly to the ongoing efforts in breast cancer research, diagnostics, and personalized medicine.

A constrained maximum likelihood approach to developing well-calibrated models for predicting binary outcomes.

The added value of candidate predictors for risk modeling is routinely evaluated by comparing the performance of models with or without including candidate predictors. Such comparison is most meaningful when the estimated risk by the two models are both unbiased in the target population. Very often data for candidate predictors are sourced from nonrepresentative convenience samples. Updating the base model using the study data without acknowledging the discrepancy between the underlying distribution of the study data and that in the target population can lead to biased risk estimates and therefore an unfair evaluation of candidate predictors. To address this issue assuming access to a well-calibrated base model, we propose a semiparametric method for model fitting that enforces good calibration. The central idea is to calibrate the fitted model against the base model by enforcing suitable constraints in maximizing the likelihood function. This approach enables unbiased assessment of model improvement offered by candidate predictors without requiring a representative sample from the target population, thus overcoming a significant practical challenge. We study theoretical properties for model parameter estimates, and demonstrate improvement in model calibration via extensive simulation studies. Finally, we apply the proposed method to data extracted from Penn Medicine Biobank to inform the added value of breast density for breast cancer risk assessment in the Caucasian woman population.

Abstract PO2-28-09: Long-term Breast Cancer Risk Prediction in Black Women: External Validation of a Mammography-driven AI Model

Abstract Despite the demonstrated potential of artificial intelligence (AI) in breast cancer risk assessment for personalizing screening recommendations, further validation is required regarding AI model bias and generalizability. We performed external validation in Black women of a mammography-driven AI breast cancer risk model (Mirai) originally developed on screening cohorts primarily consisting of White women. In this institutional review board-approved, Health Insurance Portability and Protection Act (HIPAA)-compliant study under a waiver of consent, we retrospectively analyzed a case–cohort sample nested within the core academic breast cancer screening practice of BJC Healthcare, the hospital partner of Washington University in St. Louis. For the purposes of this validation study, relying on 2D digital mammography (DM) images, we focused on Black women presenting for annual DM screening (Selenia or Selenia Dimensions; Hologic) between 2008 and 2018. Eligible breast cancer cases were derived from all women with a breast cancer diagnosis (with associated biopsy-confirmed tumor pathology via institutional cancer registry) after negative (BI-RADS 1 or 2) DM screening 1 to 5 years prior to cancer diagnosis. We also identified a random sample of controls, defined as women who had negative (BI-RADS 1 or 2) DM screening, with 1 to 5 years of screening follow-up without a cancer diagnosis. Risk scores for all DM exams were calculated via the Mirai model. Performance was evaluated using concordance-index (C) analyses and associated 95% confidence intervals (CIs) for the entire cohort, as well as for study subgroups of invasive versus in-situ cancer and cancer molecular subtypes. We analyzed 1368 DM screening exams, including 672 DM exams from 391 women diagnosed with breast cancer (mean age, 58 years; standard deviation, 10 years) and 696 DM exams from 406 controls (mean age, 55 years; standard deviation, 10 years). The overall C-index was 0.62 [95% CIs 0.60–0.64] for all Black women, which was lower compared to previously reported validation results for Mirai in studies of similar design on predominantly White and racially diverse screening cohorts (C-index = 0.67-0.78). There was no evidence of a significant difference between invasive and in-situ cancer (C-index = 0.64 [95% CIs 0.61–0.66] vs. 0.64 [95% CIs 0.61–0.67]). Compared to other cancer molecular subtypes, performance was significantly higher among triple-negative (C-index = 0.67 [95% CIs 0.62–0.71]) and estrogen receptor (ER) negative cancer (C-index = 0.66 [95% CIs 0.61–0.70]). A previously developed mammography-driven AI model showed overall good performance in long-term breast cancer risk assessment in a dataset of Black women only, particularly for triple-negative and ER- cancer types. However, performance was lower compared to previously reported validation results from similar studies on predominantly White and racially diverse screening cohorts. Our results suggest that further refinements are needed towards more accurate breast cancer risk assessment in Black women. Long-term breast cancer risk prediction performance (C-indices) of Mirai in the full cohort of Black women and in study subgroups. Citation Format: Juanita Hernandez Lopez, Zhixin Sun, Shirin Shoushtari, Ulugbek Kamilov, Debbie Bennett, Aimilia Gastounioti. Long-term Breast Cancer Risk Prediction in Black Women: External Validation of a Mammography-driven AI Model [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-28-09.

Abstract PO5-11-11: Risk Assessment, Prevention and Early Detection of Breast Cancer Related Lymphedema – Objective Measurements and Patient Reported Outcomes

Abstract Background Breast cancer-related lymphedema (BCRL) affects the quality of life, but there is no consensus regarding early detection and monitoring. Patient-reported outcomes (PROs) are important in assessing cancer survivor outcomes, and different questionnaires have been developed. LYMPHA (Lymphatic Microsurgical Preventive Healing Approach) and S-LYMPHA (Simplified LYMPHA) have reduced BCRL rates. This study aims to identify the most reliable PRO in a South Florida multi-ethnic population and to study correlations between PROs and objective measurements in the first 6 months after axillary surgery. Methods Patients undergoing axillary lymph node dissection (ALND) or axillary radiation were included. L-Dex score (Bioimpedance Spectroscopy) and three validated questionnaires (LyQLI, Lymphedema Quality of Life Inventory; LyQOL, Lymphedema Quality of Life and FACT-B4+ Functional Assessment of Cancer Therapy – Lymphedema) were recorded at baseline, and 6 months post-surgery to assess the agreement between patient-reported symptoms and BCRL. L-Dex score outside the normal range or a 10 unit increase above baseline was considered lymphedema. Additional variables such as demographics, tumor characteristics, and treatment modalities were recorded. Results Out of 40 recruited patients, 39 were analyzed (excluding one deceased patient). Patient and treatment characteristics are described in Table 1. Patients were divided into three cohorts (Figure 1) for analysis: ALND with no axillary radiation (7.69%; n=33), Sentinel Lymph Node Biopsy (SLNB) with axillary radiation (7.69 %; n=3), and ALND with axillary radiation (85%; n=3). In the first cohort, LYMPHA or S-LYMPHA were associated with lower rates of lymphedema (19% vs, 50%) (p=0.116). No patients in the second or third cohort developed lymphedema. There were 4 out of 20 (20%) patients with lymphedema with scores above the median for FACT B4+ (p= 0.480) and a lower score (higher quality of life) correlated with lower L-Dex (p=0.76). The higher value in the physical domain of LyQLI showed higher L-Dex (p=0.826). There was a significant inter-domain correlation in LyQLI and LyQOL (p= < 0.001). Conclusion These results validate the use of PROs alongside objective measurements to assess BCRL. In addition, LYMPHA and S-LYMPHA significantly reduce lymphedema rates. Larger numbers will be necessary to reach statistical significance and to identify the best PRO. Figure 1 Division of patient population into three cohorts Table 1 Demographic and Treatment Summary Citation Format: Anshumi Desai, Orli-Friedman Eldar, Gili Halfteck, Mecker Moller, Susan Kesmodel, Dido Franceschi, Neha Goel, Jessica Crystal, Laura Huang, Jennifer Hu, Koru-Sengal Tulay, Wei Zhao, Alexis Narvaez-Rojas, Eli Avisar. Risk Assessment, Prevention and Early Detection of Breast Cancer Related Lymphedema – Objective Measurements and Patient Reported Outcomes [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-11-11.

Abstract PO1-20-01: A Prospective Single-Arm Pilot Interventional Trial to Assess Individual Breast Cancer Risk Prior to Gender-Affirming Chest Masculinization Surgery

Abstract Background: The number of individuals who identify as transgender and/or gender-diverse (TGD) is steadily increasing and the number of individuals seeking chest masculinization surgery, commonly called “top-surgery,” is also increasing. In persons assigned female at birth, top surgery removes most breast tissue to allow the chest to appear masculine. However, top surgery is not equivalent to oncologic risk-reducing mastectomies (RRM), which aim to remove all breast tissue to reduce future breast cancer (BC) risk. While top surgery has significant psychological and health benefits, its impact on future BC risk is unclear, especially for those individuals who are at high-risk for future BC development (defined as ≥17% by international consensus guidelines) and/or have a pathogenic germline mutation for BC. The aims of this study are to 1) determine the percentage of TGD persons assigned female at birth who are considering undergoing top surgery and are at elevated BC risk using validated risk models, 2) determine the percentage of TGD persons at elevated BC risk who choose to undergo RRM as part of top surgery, and 3) assess and compare self-perceived BC risk compared to calculated risk. Methods: A single-institution prospective single-arm pilot study is currently underway from March 2023 through January 2024. Individuals assigned female at birth, who are age ≥18 years, and considering undergoing top surgery, but have not yet undergone top surgery, are eligible to enroll (goal accrual N=35). Participants are being actively recruited form our institution’s Comprehensive LGBTQIA+ Inclusion Health Clinic and Plastic Surgery Clinic. Consented participants undergo a personalized BC risk assessment the validated IBIS and Gail (for those ≥35 years old) BC risk models. Those with an average lifetime BC risk (< 17%) are recommended to continue top surgery as planned/no intervention, participants with a moderate lifetime BC risk (17-30%) are counseled they may consider RRM as part of their top surgery operation given that sparse data exists in this space, while those with a high lifetime BC risk ( >30% or pathogenic germline variant) are counseled on the potential benefits of RRM. Participants with a family history suggestive of a hereditary cancer syndrome meet with a certified Genetics counselor for consideration of genetic testing, per NCCN guidelines. Results: We anticipate reporting 1) general demographic description of the cohort, 2) the percentage who have an elevated lifetime BC risk and/or a pathogenic genetic mutation, 3) the percentage of those with an elevated risk who choose to undergo RRM as part of top surgery, and 4) report the differences between self-perceived vs calculated lifetime BC risk. Conclusion: Our findings will inform TGD persons and surgeons on the utility of a personalized BC risk assessment prior to top surgery to assist in surgical decision-making, report the accuracy of self-perceived BC risk in TGD persons, and potentially change the standard pre-operative evaluation for TGD persons considering top surgery to include a formal BC risk assessment to ensure high-risk individuals have the option to undergo RRM to appropriately reduce their future BC risk. Citation Format: Chandler Cortina, Anna Purdy, Samantha Stachowiak, Kristen Klement, Sarah Sasor, Kate Krucoff, Andrew Petroll, Kevin Robertson, Annie Lakatos, Erin Doren. A Prospective Single-Arm Pilot Interventional Trial to Assess Individual Breast Cancer Risk Prior to Gender-Affirming Chest Masculinization Surgery [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-20-01.

Abstract PS10-07: A second-generation polygenic risk score (PRS) based on genetic ancestry improves breast cancer (BC) risk prediction for all ancestries

Abstract Background: Common genetic variants, mainly single-nucleotide polymorphisms (SNPs) explain substantial genetic susceptibility to BC. PRS have been developed to quantify the combined effects of BC-associated SNPs, providing important information about BC risk. Historically, genome-wide association studies have been conducted in predominantly European populations, resulting in miscalibrated and inaccurate PRS for non-Europeans. We previously described a multiple-ancestry PRS (MA-PRS-149) based on 56 ancestry-informative and 93 BC-associated SNPs. The MA-PRS-149 achieved accuracy for all women by characterizing the genetic ancestry of each BC-associated SNP in terms of three reference ancestries (African, East Asian, and European), applying ancestry-specific SNP risks and frequencies, and combining the results as a weighted sum of three ancestry-specific PRS. Here, we aimed to improve the predictive accuracy of MA-PRS-149, particularly for non-Europeans, through the inclusion of additional BC-associated SNPs. Methods: Women referred for hereditary cancer testing and negative for pathogenic variants in BC-associated genes were divided into consecutive study cohorts to (1) quantify ancestry-specific SNP risks, (2) combine the three ancestry-specific PRS, and (3) pre-specified clinical validation. To select an optimal set of BC-associated SNPs, we developed a novel synthetic stepwise regression methodology that accounts for linkage disequilibrium. Ancestry-specific SNP risks were determined from meta-analyses of literature with clinical cohorts of 57,827 Black/African and 26,992 East Asian women. Ancestry-specific PRS were combined based on a diverse cohort of 157,740 women. Clinical validation was conducted in an independent cohort of 77,774 women. We used multivariable logistic regression adjusted for age, ancestry, and cancer history to test for improved BC risk prediction over clinical factors. We tested for improvement over the MA-PRS-149, and a European PRS, by including additional PRS as covariates. Calibration was assessed through goodness-of-fit tests. All analyses were conducted within the full cohort and ancestral subpopulations. Odds ratios (ORs) and 95% confidence intervals (CIs) are reported per standard deviation within the corresponding population. Results: An optimal set of 383 SNPs (56 ancestry-informative and 327 BC-associated) was included in the final PRS (MA-PRS-383). MA-PRS-383 added significant predictive information to clinical factors in the full cohort and within each ancestry (Table 1). MA-PRS-383 had greater predictive accuracy than MA-PRS-149 or a 383-SNP PRS with European weights. Goodness-of-fit tests showed that MA-PRS-383 was well-calibrated and predicted risk accurately in the tails of the distribution for both European and non-European women. Conclusion: MA-PRS-383 was well-calibrated and substantially improved upon existing PRS in all tested ancestral populations. Incorporation of MA-PRS-383 into BC risk assessment may lead to more accurate identification of women who are most likely to benefit from screening and preventive medications. Citation Format: Timothy Simmons, Elisha Hughes, Dmitry Pruss, Matthew Kucera, Benjamin Roa, Thaddeus Judkins, Thomas Slavin, Victor Abkevich, Ryan Hoff, Srikanth Jammulapati, Susanne Wagner, Dale Muzzey, Jerry Lanchbury, Alexander Gutin. A second-generation polygenic risk score (PRS) based on genetic ancestry improves breast cancer (BC) risk prediction for all ancestries [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS10-07.

Abstract PO5-13-03: Early Detection of Breast Cancer using Targeted Plasma Metabolomic Profiling

Abstract Background: Breast cancer (BC) is the second leading cause of cancer death among women. Accordingly, early diagnosis is key to the successful treatment, management, and care of BC. Recent studies confirmed that plasma metabolites could be reliable cancer biomarkers, allowing for the development of a minimally invasive routine blood test that can be used for screening, as well as for monitoring disease evolution in patients. The exact metabolic pathways involved in early BC development remain unclear. Metabolomic profiling of women with BC may help to identify new biomarkers to predict breast cancer long before symptoms appear. The purpose of this study was to validate a plasma metabolomic biomarker panel for an improved risk assessment for early detection BC in 241 patients, and to understand the potential role and the relationship between BC subtypes and hormone receptor status. Methods: Our study included a total of 185 plasma samples from women with biopsy-confirmed BC and 56 plasma samples from healthy controls. A targeted, quantitative mass spectrometry (MS)-based metabolomics approach was used to analyze 138 metabolites in plasma samples using a combination of direct injection (DI) MS and reverse-phase high performance liquid chromatography (HPLC) tandem mass spectrometry (MS/MS). The sample set was split into a discovery set and validation set. Metabolite concentration data, clinical data, and hormones receptor status were used to determine optimal biomarker sets. The same biomarkers and regression models were used and assessed on the validation models. The area under the receiver operator characteristic curves (AUROC), sensitivities and specificities at selected cut off points were calculated for each subgroup. Results: A large proportion of BC patients were at an early stage, with 98 at stage I (53.0 %), 70 at stage II (37.8%), and 17 at stage III (9.2%). The BC patients were of all subtypes: 138 luminal A (76,2%), 23 luminal B (12.7%), 5 hormone receptor-negative/HER2-positive (2.8%), and 15 triples negative (8.3%). A feature selection was performed on the training set using Partial Least-Squares Discriminant Analysis (PLS-DA), and the top performing metabolites were identified as the most important in discriminating BC from healthy subjects. The impact of age was also investigated. Features with >80% missing values were removed. To predict BC, the best signature comprised 9 variables implicated in fatty acid metabolism, amino acid metabolism, polyamine biosynthesis and related signaling pathways We further developed and validated a logistic regression with AUROC > 0.9 using these metabolites and other clinical data for detecting different stages and subtypes of BC. Conclusion: This study identified and validated a simple, high-performing, metabolite-based test for the early detection of BC. After confirmation in other independent cohort studies, our findings could provide the foundations for the development of a blood-based routine test for women at the highest risk for BC that is cost-effective, accurate and reliable. In addition, this approach could be used to complement other modalities especially for BC patients with dense breasts. Citation Format: Jean-Francois Haince, Lun Zhang, Rashid Ahmed Bux, Paramjit S. Tappia, Bram Ramjiawan, David Wishart, Andrew Maksymiuk. Early Detection of Breast Cancer using Targeted Plasma Metabolomic Profiling [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-13-03.

Abstract PO3-08-01: Decision curve analysis to compare breast cancer risk predictions for a polygenic integrated clinical risk model with those of a gold standard

Abstract Risk-based guidelines have been developed to provide advice on options that are available to women who are at increased risk of breast cancer. We and others are focused on improving risk prediction models for general population use. For breast cancer, polygenic risk has been identified as important for risk prediction, especially when combined with clinical risk factors such as family history, anthropometric measures, breast imaging measures, and hormonal and reproductive risk factors. When comparing the performance of risk prediction models, the AUC is most often used but it focuses on performance in terms of distinguishing between affected and unaffected individuals and does not take into account differences in the specificity (true negative rate) and sensitivity (true positive rate) of the models being compared. In contrast, decision curve analysis takes sensitivity and specificity into account and enables the comparison of the net benefit of risk prediction tools at the clinical risk thresholds that are used to guide clinical management decisions. Herein, we use decision curve analysis to compare breast cancer risk prediction guidelines for BRISK and the Breast Cancer Risk Assessment Tool (BCRAT) in the UK Biobank, and for BRISK and IBIS version 7 in the Nurses’ Health Study. We evaluated the net benefit at the 5-year risk thresholds (1.67% and 3%) that are used to guide clinical management decisions around risk-reducing medication and at the remaining lifetime risk of 20% that is used to guide supplemental MRI surveillance. In the UK Biobank, BRISK showed a net benefit over BCRAT at the 5-year risk thresholds of 1.67% and 3.0%. The AUCs were 0.649 (95% CI = 0.640, 0.695) for BRISK and 0.567 (95% CI = 0.556, 0.577) for BCRAT and showed that, overall, BRISK discriminated between affected and unaffected women better than BCRAT (P < 0.001). In the Nurses’ Health Study, BRISK showed a net benefit over IBIS at the remaining lifetime risk threshold of 20%. The AUCs were 0.647; 95% CI = 0.627, 0.668 for BRISK and 0.571; 95% CI = 0.546, 0.595 for IBIS, a statistically significant improvement for BRISK over IBIS (P < 0.0001). We also looked at sensitivity, specificity, positive predictive value and negative predictive value even though these risk models are one-step removed from breast cancer diagnosis by screening/risk-reduction options. The positive predictive values are higher for the BRISK (27.1%) model compared to BCRAT (5.5%) in the UK Biobank dataset and higher for BRISK (15.4%) compared to IBISv7 (14.0%) in the Nurses’ dataset. We have shown the application of a statistical tool that can be used to directly compare risk models at clinically relevant thresholds. We have shown that BRISK, which comprises polygenic risk and clinical risk factors, shows higher net benefit at both 1.67% and 3% thresholds compared to BCRAT and at 20% remaining lifetime risk for IBIS version 7. In breast cancer risk prediction, a tool with high specificity (and therefore a necessary trade-off for low sensitivity) is preferred because we do not want to incorrectly classify women as not being at high risk and deny them the opportunity for intervention in the form of more frequent screening, alternative modes of screening or risk-reducing medication. This improvement in general population risk stratification that BRISK can provide has the potential to have a clinically significant effect on women’s health. Citation Format: Erika Spaeth, Bernard A Rosner, Gill Dite. Decision curve analysis to compare breast cancer risk predictions for a polygenic integrated clinical risk model with those of a gold standard [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-08-01.

Abstract PO2-28-06: An on-line deep learning decision support tool, iBRISK, aimed at improving breast cancer risk estimation and reducing unnecessary biopsies for BI-RADS 4 patients

Abstract Probability of malignancy (POM) for Breast Imaging Reporting and Data System (BI-RADS) category 4 designated breast lesions ranges from 2% – 95% and contributes to a high unnecessary biopsy rate. This is as most clinicians often stick to the biopsy option to rule in or out breast cancer early; withholding biopsy could be risky, and biopsies of BI-RADS 4 lesions serve as a quality metric and performance standard. At 21.1%, biopsy-proven positive predictive value (PPV3) rates for BI-RADS 4 have not improved for decades, translating to high false-positive rates of mammography. Unnecessary biopsies are a big issue in the management of BI-RADS 4 lesions with negative implications including increased medical costs, healthcare wastes, unnecessary psychological burdens to the patients, and potential complications and risks. Objectives 1. Optimize the precision breast cancer risk assessment tool, iBRISK, that utilizes artificial intelligence (AI) technologies, including natural language processing (NLP), image processing, and deep learning, with clinical risk factors and imaging features. 2. Develop a user-friendly web interface for iBRISK to facilitate clinicians or insurers in estimating cancer risk and making informed biopsy decisions for BI-RADS 4 lesions. Methods Our intelligent-augmented breast cancer risk calculator (iBRISK) model was trained on multimodal data collected from 10,778 patients, including demographic factors, historical and clinical characteristics, mammographic features, and pathologic signatures. We validated iBRISK using 4,200 patients from multiple leading hospitals, including Houston Methodist Neal Cancer Center, the University of Texas MD Anderson Cancer Center, and the University of Texas Health San Antonio MD Anderson Mays Cancer Center. The iBRISK was connected to a backend server which is linked to a frontend web user interface using technologies like Hypertext Preprocessor (PHP) for the backend application running on an APACHE HTTP server and React-JavaScript for the front-end web application that communicates with the backend using Representational State Transfer Application Programming Interface (RESTful API) and JavaScript Object Notation (JSON) format. The communication was secured using Secure Sockets Layer (SSL) encryption. Results The iBRISK model demonstrated high sensitivity in malignancy prediction and achieved an accuracy of 89.5%, area under the receiver operating characteristic curve of 0.93 (95% CI: 0.92-0.95), sensitivity of 100%, and specificity of 81%. Only 0.16% of lesions determined to have low POM by the model were malignant. Our multi-center study shows that iBRISK achieves at least 50% reduction in unnecessary biopsies of BI-RADS 4 cases. Data elements for the required 20 features are entered into the interface using a variety of imputation methods including direct text, dropdown menus and radio button selections. The user-friendly web interface provides risk scores (0 – 1), risk levels (low, medium, and high), and associated biopsy recommendations. Conclusion The user-friendly iBRISK web interface is proposed as an adjunct to the BI-RADS system, enhancing the precision of BI-RADS 4 lesion cancer risk stratification. It is expected to reduce unnecessary biopsies, lower health costs, and enhance the quality of health care. This approach aims to tackle a critical issue in breast cancer diagnosis by leveraging advanced AI technologies and big data and providing clinicians with a tool to make more informed and precise biopsy decisions for BI-RADS 4 lesions. Citation Format: Chika Ezeana, Xiaohui Yu, Zhihao Wan, Tiancheng He, Tejal Patel, Virginia Kaklamani, Maryam Elmi, Erika Brigmon, Pamela Otto, Kenneth Kist, Lin Wang, Joe Ensor, Heather Speck, Ya-Chen Shih, Bumyang Kim, I-Wen Pan, David Spak, Wei Yang, Jenny Chang, Stephen Wong. An on-line deep learning decision support tool, iBRISK, aimed at improving breast cancer risk estimation and reducing unnecessary biopsies for BI-RADS 4 patients [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-28-06.

Abstract PO4-08-06: Causal Relationships and Familial Aggregation of Mammogram Risk Scores: Insights from Breast Cancer in Twins and Families

Abstract Background: Mammogram risk scores based on texture and density, determined by brightness thresholds, are associated with breast cancer risk differently and could reveal distinct information about breast cancer risk. This study aimed to investigate the causal relationships between these intercorrelated risk scores and the familial aggregation of a texture-based mammogram measure, shedding light on the relevance to breast cancer aetiology. Methods: We analysed data from the Australian Mammographic Density Twins and Sisters Study, including 3195 breast-cancer-free women from 1519 families. The dataset comprised 527 pairs of monozygotic (MZ) twins, 271 pairs of dizygotic (DZ) twins, and 1599 sisters. The average age at mammography for sister pairs ranged from 40 to 70 years. Mammogram risk scores based on texture (named Cirrus) and three density-defined areas (light, bright, and brightest) that were spatially independent were generated. The Inference about Causation from Examination of Familial Confounding (ICE FALCON) method was employed for causal inference. Familial aggregation and the variance of Cirrus were assessed as a function of age using a multivariate normal model for pedigree analysis. The classic twin model was employed but allowing for varying shared environmental effects among different sister pairs. The single-nucleotide polymorphism (SNP)-based heritability was estimated using genetic variants from both closely and distantly related individuals. Model fits were compared using likelihood ratio tests and the Akaike Information Criterion. Results: Causal analysis using ICE FALCON revealed significant positive causal effects, with Cirrus, light areas, and bright areas influencing the brightest areas (accounting for 34%, 55%, and 85% of the associations, respectively). Similarly, light areas and bright areas demonstrated causal effects on Cirrus (accounting for 37% and 28% of the associations, respectively). No age-related differences in familial correlations or Cirrus variance were found for women aged 40 to 70 years. Familial correlations were estimated at 0.51 (0.03) for MZ pairs and 0.16 (0.03) for combined DZ and non-twin sister pairs, with no significant difference between DZ and non-twin sister pairs (P=0.3). Additive genetic effects accounted for up to 32% (5%) of the variance, consistent with SNP-based heritability estimates of 36% (12%). MZ-specific environmental effects contributed to at least 20% (3%), while shared environmental effects for DZ and non-twin sibling pairs accounted for less than 40% of that for MZ pairs. Conclusion: In a mammogram, the lighter (less dense) areas causally influence the brightest (highly dense) areas, including through textural features. These causal relationships provide insights into the relative importance of different mammogram features in breast cancer aetiology. For example, the brightest areas are more aetiologically important for screen-detected breast cancer, while the light areas are more aetiologically important for interval breast cancer. Additionally, specific textural features capture aetiologically independent breast cancer risk information from dense areas. We also confirm the partial heritability of Cirrus, the texture-based mammogram measure, substantially influenced by environmental factors (familial and non-familial). As well as genetic factors, Cirrus could be determined by factors operating at times of life when MZ pairs share their environments to a greater extent than do DZ and non-twin sisters, starting in utero and prior to adulthood, and remaining constant thereafter. These insights from twin and family data enhance our understanding of mammogram risk scores and their implications for breast cancer risk assessment. Citation Format: Zhoufeng Ye, Shuai Li, Gill Dite, Tuong Nguyen, Robert MacInnis, John Hopper. Causal Relationships and Familial Aggregation of Mammogram Risk Scores: Insights from Breast Cancer in Twins and Families [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-08-06.

Breast density classification in mammograms using VGG convolutional networks

In recent years, multiple studies have highlighted the growing correlation between breast density and the risk of developing breast cancer. In this research, the performance of two convolutional neural network architectures, VGG16 and VGG19, was evaluated for breast density classification across three distinct scenarios aimed to compare the masking effect on the models performance. These scenarios encompass both binary classification (fatty and dense) and multi-class classification based on the BI-RADS categorization, utilizing a subset of the ABC-Digital Mammography Dataset. In the first experiment, focusing on cases with no masses, VGG16 achieved an accuracy of 93.33% and 90.00% for two and four-class classification. The second experiment, which involved cases with benign masses, yielded a remarkable accuracy of 95.83% and 93.33% with VGG16, respectively. In the third and last experiment, an accuracy of 88.00% was obtained using VGG16 for the two-class classification, while VGG19 delivered an accuracy of 93.33% for the four-class classification. These findings underscore the potential of deep learning models in enhancing breast density classification, with implications for breast cancer risk assessment and early detection.

Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024.

The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects of healthcare, particularly in the medical imaging field. This review focuses on recent developments in the application of deep learning (DL) techniques to breast cancer imaging. DL models, a subset of AI algorithms inspired by human brain architecture, have demonstrated remarkable success in analyzing complex medical images, enhancing diagnostic precision, and streamlining workflows. DL models have been applied to breast cancer diagnosis via mammography, ultrasonography, and magnetic resonance imaging. Furthermore, DL-based radiomic approaches may play a role in breast cancer risk assessment, prognosis prediction, and therapeutic response monitoring. Nevertheless, several challenges have limited the widespread adoption of AI techniques in clinical practice, emphasizing the importance of rigorous validation, interpretability, and technical considerations when implementing DL solutions. By examining fundamental concepts in DL techniques applied to medical imaging and synthesizing the latest advancements and trends, this narrative review aims to provide valuable and up-to-date insights for radiologists seeking to harness the power of AI in breast cancer care.

A Novel Disulfidptosis-related lncRNAs Prognostic Signature for Prognosis Predicting and Immune Microenvironment Characterization in Breast Cancer.

Breast cancer (BRCA) is one of the leading causes of cancer-related death in women. The improvement of the BRCA risk assessment method is of positive clinical significance. Although many clues showed the potential role of disulfidptosis in BRCA as a novel type of programmed cell death, whether disulfidptosis is involved in BRCA tumorigenesis remains unclear. We used LASSO-univariate Cox analysis and multivariate Cox analysis to identify six disulfidptosis-related lncRNAs (DPLs) that correlated with BRCA clinical outcome and confirmed that these DPLs were independent prognostic factors for BRCA (YTHDF3-AS1, AC002398.1, AL451085.2, AC092718.4, AC097662.1 and AC053503.5). The BRCA risk prognosis model was subsequently established based on these DPLs. After verifying the model reliability in predicting prognosis, immune infiltration and somatic mutation analysis showed significant differences in the immune microenvironment and mutation of DPLs by risk stratification. Immunotherapy response and drug resistance analysis suggest the reference value of DPLs in clinical individualized therapy. The abnormal expressions of selected DPLs were further validated by the BRCA cell line experiment. Our results shed new light on the role of DPLs in BRCA.

Identifying SLC2A6 as the novel protective factor in breast cancer by TP53-related genes affecting M1 macrophage infiltration.

The high heterogeneity of breast cancer (BC) caused by pathogenic gene mutations poses a challenge to immunotherapy, but the underlying mechanism remains unknown. The difference in the infiltration of M1 macrophages induced by TP53 mutations has a significant impact on BC immunotherapy. The aim of this study was to develop a TP53-related M1 macrophage infiltration molecular typing risk signature in BC and evaluate the biological functions of the key gene to find new immunotherapy biomarkers. Weighted correlation network analysis (WGCNA) and negative matrix factorization (NMF) were used for distinguishing BC subtypes. The signature and the nomogram were both constructed and evaluated. Biological functions of the novel signature gene SLC2A6 were confirmed through in vitro and in vivo experiments. RNA-Sequencing and protein profiling were used for detecting the possible mechanism of SLC2A6. The results suggested that four BC subtypes were distinguished by TP53-related genes that affect M1 macrophage infiltration. The signature constructed by molecular typing characteristics could evaluate BC's clinical features and tumor microenvironment. The nomogram could accurately predict the prognosis. The signature gene SLC2A6 was found to have an abnormally low expression in tumor tissues. Overexpression of SLC2A6 could inhibit proliferation, promote mitochondrial damage, and result in apoptosis of tumor cells. The HSP70 family member protein HSPA6 could bind with SLC2A6 and increase with the increased expression of SLC2A6. In summary, the risk signature provides a reference for BC risk assessment, and the signature gene SLC2A6 could act as a tumor suppressor in BC.

396 Unraveling the Immunological Basis of Lobular Involution in Breast Cancer Development

OBJECTIVES/GOALS: Reveal common immune mechanisms in dysregulated age-related lobular involution (ARLI) and post-partum lobular involution (PPLI) to understand their link to increased breast cancer risk, challenging the traditional view of their distinctiveness. Ultimately, to improve breast cancer risk assessment and personalized prevention METHODS/STUDY POPULATION: The Mayo Clinic Benign Breast Disease (BBD) cohort comprises of ~20,000 women with benign biopsies, including ~1000 women with sequential benign biopsies. Lobular involution (LI) status was assessed by selecting perimenopausal women, ages 45-55, with sequential biopsies, comparing acini number and lobule size between initial and subsequent biopsies. NanoString IO360/ BC360 RNA profiling identified differentially expressed genes associated with dysregulated LI. Using multiplex immunofluorescence (mIF), I'll analyze and spatially map immune biomarkers related to dysregulated ARLI and PPLI in BBD tissue from perimenopausal women who did or did not go on to develop breast cancer, assessing the commonality of ARLI and PPLI markers and exploring their potential as risk biomarkers for breast cancer. RESULTS/ANTICIPATED RESULTS: Preliminary findings link patients who display dysregulated ARLI with an increased breast cancer risk and identify vital PPLI biomarkers in perimenopausal women. I expect the biopsies of women who developed post-menopausal breast cancer (PMBC) and post-partum breast cancer (PPBC) to exhibit elevated levels of dysregulated ARLI immune biomarkers and PPLI biomarkers. Spatially mapping these markers promises to provide a more comprehensive understanding of their interactions, potentially revealing common immunological pathways. These findings could transform our current paradigm of ARLI and PPLI as distinct processes and demonstrate their interconnection in shaping breast cancer risk. DISCUSSION/SIGNIFICANCE: PMBC and PPBC dominate majority of breast cancer cases. Both involve activation of the understudied process of lobular involution, which has been shown to have pro-tumorigenic traits. Elucidating these mechanisms will aid more efficient risk stratification and personalized prevention to reduce incidence and mortality of breast cancer.

Abstract 849: Deciphering the link between aging, lobular involution, and breast cancer risk: The role of LIFR/pSTAT3 signaling

Abstract Background: Age-related lobular involution (ARLI), characterized by regression of breast epithelial tissue during the peri-and post-menopausal period, plays a crucial role in breast cancer risk. Delayed or incomplete ARLI is linked with increased postmenopausal breast cancer susceptibility.This study focuses on unraveling the molecular mechanisms of ARLI, drawing parallels with the well-characterized postpartum involution (PPI). Our aim was to discover biomarkers differentiating women who progressed through ARLI from those with stagnant ARLI. Methods: We utilized the Mayo Clinic Benign Breast Disease (BBD) cohort, analyzing 108 perimenopausal women aged 45-55 who had a subsequent benign biopsy between 2-15 years later. We assessed Lobular Involution (Ll) status by comparing the number and size of lobules in initial and subsequent biopsies. RNA profiling of initial biopsies was performed using NanoString 10360 and BC360, identifying differentially expressed genes between ARLI progression and stagnation. Immunohistochemistry (IHC) mapped the expression of these biomarkers in epithelial lobules. Results: We identified 37 genes with significant differential expression, highlighting Leukemia Inhibitory Factor Receptor (LIFR) as upregulated in ARLI stagnant samples. LIFR, part of the IL-6 cytokine signaling pathway, is known for its role in the induction of apoptosis during PPI via pSTAT3 activation. Our IHC analyses of initial biopsies of women who differentially progress through ARLI confirmed differential expression of LIFR that is coordinately regulated with pSTAT3. Conclusion: Our findings reveal significant parallels between ARLI and PPI. Future research will assess how LIFR/pSTAT3 signaling is linked to induction of apoptotic markers in perimenopausal women and will investigate how anomalies in the LIFR/pSTAT3 pathway might contribute to heightened breast cancer risk in women with delayed or incomplete ARLI. This study not only sheds light on the complex biological processes of aging in breast tissue but also opens new avenues for breast cancer risk assessment and prevention strategies. Citation Format: Jaida C. Lue, Derek Radisky, Melody Stallings-Mann, Mark Sherman, Amy Degnim, Bryan McCauley, Tanya Hoskin. Deciphering the link between aging, lobular involution, and breast cancer risk: The role of LIFR/pSTAT3 signaling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 849.

Personalized survivorship care: Routine breast cancer risk assessment in the gynecologic oncology clinic

IntroductionGynecologic and breast cancers share several risk factors. Breast cancer risk assessment tools can identify those at elevated risk and allow for enhanced breast surveillance and chemoprevention, however such tools are underutilized. We aim to evaluate the use of routine breast cancer risk assessment in a gynecologic oncology clinic. MethodsA patient-facing web-based tool was used to collect personal and family history and run four validated breast cancer risk assessment models (Tyrer-Cuzick (TC), Gail, BRCAPRO, and Claus) in a gynecologic oncology clinic. We evaluated completion of the tools and identification of patients at elevated risk for breast cancer using the four validated models. ResultsA total of 99 patients were included in this analysis. The BRCAPRO model had the highest completion rate (84.8%), followed by the TC model (74.7%), Gail model (74.7%), and the Claus model (52.1%). The TC model identified 21.6% of patients completing the model as having ≥20% lifetime risk of breast cancer, compared to 6.8% by the Gail model, and 0% for both the BRCAPRO and Claus models. The Gail model identified 52.5% of patients as having ≥1.67% 5-year risk of breast cancer. Among patients identified as high-risk for breast cancer and eligible for screening, 9/9 (100%) were referred to a high-risk breast clinic. ConclusionAmong patients that completed the TC breast cancer risk assessment in a gynecologic oncology clinic, approximately 1 in 5 were identified to be at significantly elevated lifetime risk for breast cancer. The gynecologic oncologist's office might offer a convenient and feasible setting to incorporate this risk assessment into routine patient care, as gynecologic oncologists often have long-term patient relationships and participate in survivorship care.