History Of Benign Breast Disease Research Articles

Risk factors for breast cancer subtypes by race and ethnicity: A scoping review.

Breast cancer is comprised of distinct molecular subtypes. Studies have reported differences in risk factor associations with breast cancer subtypes, especially by tumor estrogen receptor (ER) status, but their consistency across racial and ethnic populations has not been comprehensively evaluated. We conducted a qualitative, scoping literature review using the Preferred Reporting Items for Systematic Reviews and Meta-analysis, extension for Scoping Reviews to investigate consistencies in associations between 18 breast cancer risk factors (reproductive, anthropometric, lifestyle, and medical history) and risk of ER-defined subtypes in women who self-identify as Asian, Black or African American, Hispanic or Latina, or White. We reviewed publications between January 1, 1990 and July 1, 2022. Etiologic heterogeneity evidence (convincing, suggestive, none, or inconclusive) was determined by expert consensus. Publications per risk factor ranged from 14 (benign breast disease history) to 66 (parity). Publications were most abundant for White women, followed by Asian, Black or African American, and Hispanic or Latina women. Etiologic heterogeneity evidence was strongest for parity, followed by age at first birth, post-menopausal BMI, oral contraceptive use, and estrogen-only and combined menopausal hormone therapy. Evidence was limited for other risk factors. Findings were consistent across racial and ethnic groups, although the strength of evidence varied. The literature supports etiologic heterogeneity by ER for some established risk factors that are consistent across race and ethnicity groups. However, in non-White populations evidence is limited. Larger, more comparable data in diverse populations is needed to better characterize breast cancer etiologic heterogeneity.

Breast cancer screening patterns and associated factors in Iranian women over 40 years

Screening is a key component of breast cancer early detection programs that canconsiderably reduce relevant mortality rates. The purpose of this study was to determinethe breast cancer screening behavioral patterns and associated factors in women over40 years of age. In this descriptive‑analytical cross‑sectional study, 372 over 40 years of agewomen visiting health centers in Tabriz, Iran, in 2023 were enrolled using cluster sampling.The data were collected using the sociodemographic characteristics questionnaire, breastcancer perception scale, health literacy for Iranian adults scale, and the Breast CancerScreening Behavior Checklist. The obtained data were analyzed in SPSS version 16 usingdescriptive statistics (frequency, percentage, mean, and standard deviation) and inferentialstatistics (univariate and multivariate logistic regression analyses). In total, 68.3% of allparticipants performed breast self‑examination (BSE) (9.9% regularly, once per month),60.2% underwent clinical breast examination (CBE) (8.9% regularly, twice per year),51.3% underwent mammography (12.3% regularly, once per year), and 36.2% underwentsonography (3.8% regularly, twice per year). The findings also showed that women withbenign breast diseases were more likely to undergo CBE (OR = 8.49; 95% CI 2.55 to 28.21;P < 0.001), mammography (OR = 8.84; 95% CI 2.98 to 10; P < 0.001), and sonography(OR = 18.84; 95% CI 6.40 to 53.33; P < 0.001) than others. Participants with low and moderatebreast cancer perception scores were more likely to perform BSE than women with highbreast cancer perception scores (OR = 2.20; 95% CI 1.21 to 4.00; P = 0.009) and women whohad a history of benign breast disease were more likely to perform screening behaviors thanothers (OR = 2.47; 95% CI 1.27 to 4.80; P = 0.008). Women between the ages of 50 and 59 weremore likely to undergo mammography (OR = 2.33; 95% CI 1.29 to 4.77; P = 0.008) and CBE(OR = 2.40; 95% CI 1.347 to 4.20; P = 0.003) than those ≥ 60 years. Given the low participationof women in regular breast cancer screening, it is suggested that health care providershighlight the need for screening at the specified intervals in their training programs. Inaddition, health authorities are recommended to use reminder systems to remind women,especially those over 40 years of age, of the best time for breast screening. Moreover, healthcare providers must seek to improve breast cancer knowledge, attitudes, and perceptionsof women who visit health centers, which are the first level of contact with the healthcaresystem for the general population.

Abstract PS10-09: Development of an absolute risk prediction model for premenopausal breast cancer in an international consortium

Abstract Risk prediction models that have been developed for overall breast cancer risk are based on a limited number of premenopausal cases in individual cohorts. As some risk factors differ in their associations with pre- versus postmenopausal breast cancer, a distinct risk prediction model is needed for premenopausal breast cancer. We developed a risk prediction model for premenopausal breast cancer using 779,601 participants and 9,665 incident cases from 18 prospective studies within the Premenopausal Breast Cancer Collaborative Group (PBCCG), across North America (N=9), Europe (N=6), Australia (N=1), and Asia (N=2). Data were split, within each cohort, into training (2/3) and testing (1/3) datasets. Individual risk was assessed in five-year intervals, using variables reported at the start of the time interval. Cox proportional hazards regression was used to model risk factors in a backwards-selection method, stratified by cohort: age at menarche, age at first birth, parity, breastfeeding (months), height (cm), BMI (kg/m2), BMI at age 18, recent weight change (kg), alcohol consumption (drinks/week), first-degree family history of breast cancer, and personal history of benign breast disease. To enable the use of information from all cohorts despite differences in missing variables by design, cohorts were grouped by available variables and risk models were fit for each group. In cohorts with incomplete data, model coefficients were adjusted based on the correlation of covariates with the missing variables in the complete case dataset. Coefficients were meta-analyzed with inverse variance weighting to obtain final coefficients. Discrimination was evaluated in the testing dataset by calculation of the c-index. Work is ongoing to calibrate the model based on five-year absolute risk using GLOBOCAN continent- and age-specific incidence rates to represent baseline risk. The final model included age at menarche, parity, height, BMI, BMI at age 18, first-degree family history of breast cancer, and history of benign breast disease. Young adulthood BMI and BMI (at start of the 5-year risk interval) were associated with a decreased risk (Hazard Ratio (HR) (95% confidence interval (CI)) per 5 kg/m2 = 0.87 (0.81-0.93) and 0.90 (0.86-0.95), respectively) as was parity (HR (95% CI) = 0.92 (0.90-0.94)). Height was associated with increased risk (HR (95% CI) per 10 cm = 1.14 (1.07-1.21)), while history of benign breast disease and family history were associated with larger increases in risk (HR (95% CI) = 1.64 (1.30-2.06) and 1.76 (1.63-1.90), respectively). Model discrimination was higher than that reported for women under 50 years in existing breast cancer risk prediction models considering clinical factors (AUC (95% CI) = 0.61 (0.59-0.62)). Calibration of absolute 5-year risk is ongoing. Several factors driving risk prediction of postmenopausal breast cancer have similar influence on risk of premenopausal breast cancer, while family history has a stronger influence on premenopausal breast cancer risk. Our model demonstrates acceptable discrimination and will enable individual 5-year absolute risk prediction for premenopausal breast cancer. Citation Format: Kristen Brantley, Michael Jones, Minouk Schoemaker, Hazel Nichols, Anthony Swerdlow, Robert MacInnis, Roger Milne, Tess Clenenden, Yu Chen, Xiao-Ou Shu, Wei Zheng, Woon-Puay Koh, Jian-Min Yuan, Cari Kitahara, Martha Linet, Dale Sandler, Bernard A Rosner, Peter Kraft, A. Heather Eliassen. Development of an absolute risk prediction model for premenopausal breast cancer in an international consortium [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 PS10-09.

Sleep duration and mortality among breast cancer survivors in the Western New York Exposures and Breast Cancer (WEB) Study.

There is increasing evidence that sleep duration may affect breast cancer survival through effects on circadian function, influencing disease progression. However, further investigation of this association is needed. In a population-based, prospective cohort study of women from the Western New York Exposures and Breast Cancer Study, we examined mortality outcomes with invasive breast cancer identified using the National Death Index. Cox proportion hazards ratios with 95% confidence intervals were used to estimate risk of all-cause (AC) and breast cancer-specific (BC) mortality associated with self-reported usual sleep duration with adjustment for age, race/ethnicity, years of education, body mass index (BMI), menopausal status, pack-years ofsmoking, tumor stage, and estrogen-receptor (ER) status. We further examined associations within strata of BMI, tumor stage, menopausal status, and ER status. A sample of 817 patients with breast cancer were followed for a median of 18.7years, during which 339 deaths were reported, including 132 breast cancer-specific deaths. Those who reported shorter or longer sleep tended to have a slightly higher BMI, to be less proportionately non-Hispanic White, to report a previous history of benign breast disease, and to have consumed more alcohol during their lifetime. We found no significant associations between sleep duration and AC or BC mortality, including within stratified analyses. Sleep duration was not associated with either AC or BC mortality including within strata of BMI, tumor stage, menopausal status, or ER status.

PD-L1 and TILs expression and their association with clinicopathological characteristics in Vietnamese women with primary invasive breast cancer.

Tumor-infiltrating lymphocytes (TILs) and programmed death ligand 1 (PD-L1) are promising new factors in the prognosis and prediction of breast cancer patients. Our study evaluated the prevalence of expression of TILs on hematoxylin and eosin (H&E) slides, PD-L1 expression on immunohistochemistry, and their association with clinicopathological characteristics in Vietnamese women with invasive breast cancer. This study was conducted on 216 women with primary invasive breast cancer. The evaluation of TILs on the HE slides was based on the International TILs Working Group 2014 recommendation. PD-L1 protein expression was determined using the Combined Positive Score, the number of tumor cells, lymphocytes, and macrophages stained by PD-L1 divided by the total viable tumor cells multiplied by 100. Based on the cutoff of 11%, the prevalence of TILs expression was 35.6%, of which highly expressed TILs (≥50%) accounted for 15.3%. Postmenopausal women and those who had a body mass index of 25 kg/m2 or greater had a higher odds of having TILs expression. However, patients who had the expression of Ki-67, HER-positive molecular subtype, and triple-negative subtype were more likely to have TILs expression. The prevalence of PD-L1 expression was 30.1%. A significantly higher odds of having PD-L1 was found in patients who had a history of benign breast disease, self-detected tumor and had TILs expression. The expression of TILs and PD-L1 is common in Vietnamese women with invasive breast cancer. Because of the importance of these expressions, routine evaluation to find women who had TILs and PD-L1 is needed so that treatment and prognosis can be optimized. Such routine evaluation can be targeted to those who had a high-risk profile found in this study.

Prediction of breast cancer risk for sisters of women attending screening.

Risk assessment is important for breast cancer prevention and early detection. We aimed to examine whether common risk factors, mammographic features, and breast cancer risk prediction scores of a woman were associated with breast cancer risk for her sisters. We included 53 051 women from the Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) study. Established risk factors were derived using self-reported questionnaires, mammograms, and single nucleotide polymorphism genotyping. Using the Swedish Multi-Generation Register, we identified 32 198 sisters of the KARMA women (including 5352 KARMA participants and 26 846 nonparticipants). Cox models were used to estimate the hazard ratios of breast cancer for both women and their sisters, respectively. A higher breast cancer polygenic risk score, a history of benign breast disease, and higher breast density in women were associated with an increased risk of breast cancer for both women and their sisters. No statistically significant association was observed between breast microcalcifications and masses in women and breast cancer risk for their sisters. Furthermore, higher breast cancer risk scores in women were associated with an increased risk of breast cancer for their sisters. Specifically, the hazard ratios for breast cancer per 1 standard deviation increase in age-adjusted KARMA, Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA), and Tyrer-Cuzick risk scores were 1.16 (95% confidence interval [CI] = 1.07 to 1.27), 1.23 (95% CI = 1.12 to 1.35), and 1.21 (95% CI = 1.11 to 1.32), respectively. A woman's breast cancer risk factors are associated with her sister's breast cancer risk. However, the clinical utility of these findings requires further investigation.

Abstract 5241: Association of polygenic risk scores with breast cancer risk among women with benign breast disease

Abstract Over one million biopsies are performed annually in the U.S. to evaluate suspicious breast lesions. Most biopsies lead to a diagnosis of benign breast disease (BBD) and these women have a 1.5- to fourfold increase in subsequent breast cancer (BC) risk compared with the general population. A polygenic risk score (PRS) including 313 common variants was developed to predict BC risk in the general population and has improved performance when added to existing BC prediction models. However, the value of PRS in risk prediction models among women with BBD has not been evaluated. To evaluate the role of the established PRS, BBD, and BC risk, we pooled data from 15,194 white women (6,706 BC cases and 8,488 controls) participating in five Breast Cancer Association Consortium case-control studies. BC risk associations were evaluated by self-reported BBD history and combinations of PRS expressed in tertiles using odds ratios (OR), considering the lowest category of PRS among women without BBD as the referent. Associations were also assessed in an independent case-cohort study (831 BC cases and 1,845 controls) using hazard ratios (HR). Among the case-control studies, the PRS was strongly associated with BC risk (OR=2.50 per standard deviation increase), and associations were similar in women with (OR=2.60) and without a history of BBD (OR=2.44, p-interaction=0.23). Women with BBD have a higher risk of BC than those without BBD within each level of PRS. Compared to women without BBD and in the lowest PRS category, those with BBD and PRS in the highest tertile had 5-fold increased odds of BC (OR = 5.38; 95% CI 4.73-6.13). Associations were similar in an independent case-cohort study. These findings indicate that BBD history and PRS are independent contributors to BC risk, and consideration of both simultaneously could improve breast cancer risk prediction. Future studies are needed to integrate PRS with other risk measures to refine absolute BC risk prediction among BBD patients. Associations of BC risk with combinations of PRS (tertiles) and history of BBD in five case-control studies and a case-cohort study from the Breast Cancer Association Consortium. Case-control studies Case-cohort study PRS score range History of BBD n BC cases n controls OR (95% CI)1 n BC cases n controls HR (95% CI)2 Tertile 1 (≤ -0.57) No 1009 2692 1.00 (Ref) 117 584 1.00 (Ref) Yes 508 902 1.54 (1.35-1.76) 69 163 2.05 (1.45-2.89) Tertile 2 (-0.58, -0.03) No 1469 2001 1.97 (1.78-2.17) 181 475 1.93 (1.48-2.51) Yes 760 798 2.65 (2.34-3.01) 108 170 2.99 (2.18-4.09) Tertile 3 (≥ -0.02) No 1910 1551 3.32 (3.01-3.67) 214 336 3.28 (2.52-4.26) Yes 1050 544 5.38 (4.73-6.13) 142 117 4.83 4.25-8.02) 1.Logistic regression analysis, adjusted for study site/genomic platform and age at enrollment. OR, odds ratio; CI, confidence interval.2.Cox proportional hazards regression analysis, accounting for the case-cohort study design and adjusted for age at enrollment. HR, hazard ratio; CI, confidence interval. Citation Format: Stacey J. Winham, Mark Sherman, Robert Vierkant, Bryan McCauley, Christopher Scott, Mia Gaudet, Melissa Troester, Sandhya Pruthi, Derek Radisky, Amy Degnim, Kristan Aronson, Rachel Murphy, Pascal Guenel, Therese Truong, Jenny Chang-Claude, Heiko Becher, Montserrat Garcia-Closas, Stephen Chanock, Thomas Ahearn, Xiaohong Yang, Doug Easton, Manjeet Bolla, Celine Vachon, Breast Cancer Association Consortium. Association of polygenic risk scores with breast cancer risk among women with benign breast disease. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5241.

Abstract PD14-04: PD14-04 Whole mammogram image-based Cox regression improves 5-year breast cancer prediction

Abstract To efficiently capture data from mammographic breast images and classify long-term risk of breast cancer, we developed FLIP, a novel Cox regression-based framework that fully utilizes data in the mammograms beyond current density measures. FLIP use the extensive existing data that are currently ignored in the context of breast cancer risk stratification. More than 20 studies support texture features add value to risk prediction beyond breast density. However, the entire mammogram imaging data has a high dimension of pixels (~13 million per image), greatly exceeding the number of women in a cohort. FLIP was fitted and cross-validated within the Joanne Knight Breast Health Cohort excluding cases diagnosed in the first 6 months of entry. The Joanne Knight Breast Health Cohort is comprised of over 10,000 women undergoing repeated mammography screening at Siteman Cancer Center and followed since 2010. All women had baseline mammogram at entry, provided a blood sample and completed a risk factor questionnaire. Mammograms are all using the same technology (Hologic). During follow-up through October 2020, we identified 246 incident breast cancer cases (pathology confirmed) and matched them to controls from the perspective cohort based on month of mammogram and age at entry. We obtained an AUC of 0.68 (SE 0.03) including the whole mammogram image, age and BI-RADS (4th edition) density category; and AUC of 0.72 (SE 0.04) by adding in BMI and menopausal status to this model. These 5-year prediction performances exceed that of well-developed models based on epidemiologic risk factors (P &amp;lt; 0.001). FLIP offers standard statistical solutions and removes barriers to wider clinical use without prohibitive training data and extensive computational requirements, providing a transparent workflow ensuring high reproducibility. It should be accessible anywhere mammograms are used. We conclude that using full mammogram images for breast cancer risk prediction captures additional information on breast tissue characteristics that relate to cancer risk, and improves prediction classification. This prediction algorithm can run efficiently in real time (in seconds) with processing of digital mammograms. Thus, this model can be easily implemented in mammography screening services and other clinical settings to guide real-time risk stratification to improve precision prevention of the leading cancer in women world-wide. Further analysis will quantify the value of adding other breast cancer risk factors, including polygenic risk scores. Addition of repeated mammogram images over time should further increase classification performance. This approach has the potential to improve risk classification by using data already available for the vast majority of women already having repeated screening mammograms. Schema overview of FLIP The raw images are in the form of .dcm files before entering into FLIP. After automated processing and image alignment, the two CC-views (left and right) are average between the two breasts for characterization. The inputted 2D mammograms are first characterized with bivariate splines over triangulation to preserve spatial distribution of pixels and accommodate the irregular semi-circular breast boundary. The characterization is further optimized (see Supplemental Material) which provides a unique and closed-form solution. b. A simple Cox proportional hazards model is adopted using well-established risk factors (RF), including age, breast density (BI-RADS), BMI, menopausal status, parity, family history, and history of benign breast disease. The mammogram image acts as an additional risk factor in the Cox regression accompanied with a 2D coefficient surface. All inferential procedures with Cox regression are applicable to FLIP which provides a transparent workflow ensuring high reproducibility. h_i (t) denotes the hazard function at time t for individual i, and h_0 (t) denotes the nonparametric baseline hazard function. c. Women who are diagnosed with breast cancer within the first 6 month of their mammogram date have been removed from this analysis and we focus on the 5-year risk. Discriminatory performance is assessed with AUC and validated via a 10-fold cross-validation. Citation Format: Shu Jiang, Graham A. Colditz. PD14-04 Whole mammogram image-based Cox regression improves 5-year breast cancer prediction [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD14-04.

Factors Associated With False-Positive Recalls in Mammography Screening.

We aimed to identify factors associated with false-positive recalls in mammography screening compared with women who were not recalled and those who received true-positive recalls. We included 29,129 women, aged 40 to 74 years, who participated in the Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) between 2011 and 2013 with follow-up until the end of 2017. Nonmammographic factors were collected from questionnaires, mammographic factors were generated from mammograms, and genotypes were determined using the OncoArray or an Illumina custom array. By the use of conditional and regular logistic regression models, we investigated the association between breast cancer risk factors and risk models and false-positive recalls. Women with a history of benign breast disease, high breast density, masses, microcalcifications, high Tyrer-Cuzick 10-year risk scores, KARMA 2-year risk scores, and polygenic risk scores were more likely to have mammography recalls, including both false-positive and true-positive recalls. Further analyses restricted to women who were recalled found that women with a history of benign breast disease and dense breasts had a similar risk of having false-positive and true-positive recalls, whereas women with masses, microcalcifications, high Tyrer-Cuzick 10-year risk scores, KARMA 2-year risk scores, and polygenic risk scores were more likely to have true-positive recalls than false-positive recalls. We found that risk factors associated with false-positive recalls were also likely, or even more likely, to be associated with true-positive recalls in mammography screening.

Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study.

It is believed that smoking is not the cause of approximately 53% of lung cancers diagnosed in women globally. The study aimed to develop and validate a simple and noninvasive model that could assess and stratify lung cancer risk in nonsmoking Chinese women. Based on the population-based Cancer Screening Program in Urban China, this retrospective, cross-sectional cohort study was carried out with a vast population base and an immense number of participants. The training set and the validation set were both constructed using a random distribution of the data. Following the identification of associated risk factors by multivariable Cox regression analysis, a predictive nomogram was developed. Discrimination (area under the curve) and calibration were further performed to assess the validation of risk prediction nomogram in the training set, which was then validated in the validation set. In sum, 151,834 individuals signed up to take part in the survey. Both the training set (n=75,917) and the validation set (n=75,917) were comprised of randomly selected participants. Potential predictors for lung cancer included age, history of chronic respiratory disease, first-degree family history of lung cancer, menopause, and history of benign breast disease. We displayed 1-year, 3-year, and 5-year lung cancer risk-predicting nomograms using these 5 factors. In the training set, the 1-year, 3-year, and 5-year lung cancer risk areas under the curve were 0.762, 0.718, and 0.703, respectively. In the validation set, the model showed a moderate predictive discrimination. We designed and validated a simple and noninvasive lung cancer risk model for nonsmoking women. This model can be applied to identify and triage people at high risk for developing lung cancers among nonsmoking women.

Benign Breast Disease, NSAIDs, and Postmenopausal Breast Cancer Risk in the CPS-II Cohort.

Nonsteroidal anti-inflammatory agents (NSAID) are associated with modest inconsistent reductions in breast cancer risk in population-based cohorts, whereas two focused studies of patients with benign breast disease (BBD) have found lower risk with NSAID use. Given that BBD includes fibroinflammatory lesions linked to elevated breast cancer risk, we assessed whether NSAID use was associated with lower breast cancer risk among patients with BBD.Participants were postmenopausal women in the Cancer Prevention Study-II (CPS-II), a prospective study of cancer incidence and mortality, who completed follow-up surveys in 1997 with follow-up through June 30, 2015. History of BBD, NSAID use, and covariate data were updated biennially. This analysis included 23,615 patients with BBD and 36,751 patients with non-BBD, including 3,896 incident breast cancers over an average of 12.72 years of follow-up among participants. NSAID use, overall and by formulation, recency, duration, and pills per month was analyzed versus breast cancer risk overall and by BBD status using multivariable-adjusted Cox models; BBD status and NSAID use were modeled as time-dependent exposures.Patients with BBD who reported using NSAIDs experienced lower breast cancer risk (HR, 0.87; 95% CI, 0.78-0.97), with similar effects for estrogen receptor (ER)-positive breast cancers [HR, 0.85; 95% confidence interval (CI), 0.74-0.97] and ER-negative breast cancers (HR, 0.87; 95% CI, 0.59-1.29); among women without BBD, NSAID use was unrelated to risk (HR, 1.02; 95% CI, 0.92-1.13; Pinteraction = 0.04). Associations stratified by age, obesity, menopausal hormone use, and cardiovascular disease were similar.Among patients with BBD, NSAID use appears linked to lower breast cancer risk. Further studies to assess the value of NSAID use among patients with BBD are warranted. We examined whether NSAID use, a modifiable exposure, is associated with breast cancer risk in postmenopausal women from the Cancer Prevention Study-II with self-reported benign breast disease, an often inflammatory condition associated with higher rates of breast cancer.

CN16 Lung cancer risk prediction nomogram in Chinese female non-smokers

About 53% of lung cancers in females are not attributable to smoking worldwide. The aim was to develop and validate a simple and non-invasive model which could assess and stratify lung cancer risk in female non-smokers in China. A large-sample size, population-based study was conducted under the framework of a population-based Cancer Screening Program in Urban China (CanSPUC). Data on the lung cancer screening in Henan province, China, from October 2013 to October 2019 were used and randomly divided into the training and validation sets. Related risk factors were identified through multivariable Cox regression analysis, followed by establishment of risk prediction nomogram. Discrimination [area under the curve (AUC)] and calibration were further performed to assess the validation of risk prediction nomogram in the training set, and then validated by the validation set. A total of 151,834 eligible subjects were included, with a mean age of 55.34 years. Subjects were randomly divided into the training (75,917) and validation (75,917) sets. Elder age, history of chronic respiratory disease, first-degree family history of lung cancer, menopause, and history of benign breast disease were the independent risk factors for lung cancer. Using these five variables, we plotted 1-year, 3-year, and 5-year lung cancer risk prediction nomogram. The AUC was 0.762, 0.718, and 0.703 for the 1-, 3- and 5-year lung cancer risk in the training set, respectively. In the validation set, the model showed a good predictive discrimination, with the AUC was 0.646, 0.658, and 0.650 for the 1-, 3- and 5-year lung cancer risk. We developed and validated a simple and non-invasive lung cancer risk model in female non-smokers. This model can be applied to identify and triage patients at high risk for developing lung cancers in female non-smokers.

Predictors of use of prevention strategies among women at high-risk for breast cancer.

10554 Background: Uptake of chemoprevention and prophylactic surgery among women at high risk for breast cancer is low, despite proven efficacy. We evaluated breast cancer risk factors as predictors of uptake of prevention treatment (PT) to better understand potential associations with these decisions. Methods: An IRB approved registry established in 2003 at the University of Vermont of high-risk women was used to evaluate the association between both modifiable (obesity, sedentary lifestyle and alcohol use) and non-modifiable (age, history of benign breast disease [BDD], genetic predisposition) risk factors and uptake of PT. Women were eligible for inclusion in this analysis if they had one of the following risk factors (BDD, strong family history, &gt; 20% lifetime modeled risk or genetic predisposition) and completed questionnaires regarding diet and physical activity. Alcohol use was assessed using a health questionnaire and physical activity was assessed using a 7-Day Physical Activity Recall questionnaire. We used logistic regression to estimate odds ratios (OR). Results: 504 women were included and had been followed for median of 13 years. Mean age was 44.5 years (range 19 – 75), 98% were Caucasian, and mean BMI was 26.9 (range 17-57). 78% had a family history of breast cancer, 60% had &gt;20% lifetime modeled risk, 14% had a history of benign breast disease (BBD), and 9% of women had confirmed genetic risk in high or moderate risk genes. Women may have had more than one risk factor. 55% were physically active and 55%. 4.2% were sedentary and 12.3% consumed &gt; 1 alcoholic beverage daily. 20.8% of the cohort participated in PT (8.1% took chemoprevention, 2.0% underwent prophylactic bilateral total mastectomy and 10.7% underwent risk reducing salpingo-oophorectomy). Among non-modifiable risk factors, age, genetic predisposition (ORadj 8.66, 95% CI 2.30–32.33, p &lt; 0.0001), and history of benign breast disease (ORadj 4.09, 95% CI 1.89–8.86, p &lt; 0.001) were associated with uptake of PT. Increasing age was associated with increasing uptake (ptrend was &gt; 0.0001) and highest among women aged 60-69 years (ORadj8.19, 95% CI 2.87-23.37 relative to women aged &lt; 40). Women with a strong family history were significantly less likely to take up PT (p = 0.04). BMI, physical activity status and alcohol use were not associated with uptake of PT. Conclusions: Like other studies, we found a low uptake of PT among women at high risk for developing breast cancer. We found that modifiable risk factors were not associated with uptake of PT, which might suggest that high-risk women with modifiable risk factors could be targeted for interventions designed to modify risk (i.e. chemoprevention, weight reduction, etc). Interestingly, women with a strong family history may be less likely to take up PT, and studies to further examine this relationship may identify opportunities for interventions to improve uptake.

Lung cancer risk prediction nomogram in Chinese female non-smokers.

10530 Background: About 53% of lung cancers in females are not attributable to smoking worldwide. The aim was to develop and validate a simple and non-invasive model which could assess and stratify lung cancer risk in female non-smokers in China. Methods: A large-sample size, population-based study was conducted under the framework of a population-based Cancer Screening Program in Urban China (CanSPUC). Data on the lung cancer screening in Henan province, China, from October 2013 to October 2019 were used and randomly divided into the training and validation sets. Related risk factors were identified through multivariable Cox regression analysis, followed by establishment of risk prediction nomogram. Discrimination [area under the curve (AUC)] and calibration were further performed to assess the validation of risk prediction nomogram in the training set, and then validated by the validation set. Results: A total of 151,834 eligible subjects were included, with a mean age of 55.34 years. Subjects were randomly divided into the training (75,917) and validation (75,917) sets. Elder age, history of chronic respiratory disease, first-degree family history of lung cancer, menopause, and history of benign breast disease were the independent risk factors for lung cancer. Using these five variables, we plotted 1-year, 3-year, and 5-year lung cancer risk prediction nomogram. The AUC was 0.762, 0.718, and 0.703 for the 1-, 3- and 5-year lung cancer risk in the training set, respectively. In the validation set, the model showed a good predictive discrimination, with the AUC was 0.646, 0.658, and 0.650 for the 1-, 3- and 5-year lung cancer risk. Conclusions: We developed and validated a simple and non-invasive lung cancer risk model in female non-smokers. This model can be applied to identify and triage patients at high risk for developing lung cancers in female non-smokers.

Accuracy of the Breast Cancer Surveillance Consortium Model Among Women with LCIS.

The Breast Cancer Surveillance Consortium (BCSC) model predicts risk of invasive breast cancer risk based on age, race, family history, breast density, and history of benign breast disease, including lobular carcinoma in situ (LCIS). However, validation studies for this model included few women with LCIS. We sought to evaluate the accuracy of the BCSC model among this cohort. Women with LCIS diagnosed between 1983 and 2017 were identified from a prospectively maintained database. The BCSC score was calculated; those with prior breast cancer, unknown breast density, age < 35 years or > 74 years, or with history of chemoprevention use were excluded. The Kaplan-Meier method was used to estimate incidence rates. Time-dependent receiver operating characteristic (ROC) analysis was used to analyze the discriminative capacity of the model. 1302 women with LCIS were included. At a median follow-up of 7years, 152 women (12%) developed invasive cancer (6 with bilateral disease). Cumulative incidences of invasive breast cancer were 7.1% (95% CI 5.6-8.7) and 13.3% (95% CI 10.9-15.6), respectively, and the median BCSC risk scores were 4.9 and 10.4, respectively, at 5 and 10years. The median 10-year BCSC score was significantly lower than the 10-year Tyrer-Cuzick score (10.4 vs 20.8, p < 0.001). The ROC curve scores (AUC) for BCSC at 5 and 10years were 0.59 (95% CI 0.52-0.66) and 0.58 (95% CI 0.52-0.64), respectively. The BCSC model has moderate accuracy in predicting invasive breast cancer risk among women with LCIS with fair discrimination for risk prediction between individuals.

Abstract P3-12-28: Reproductive factors and molecular subtypes of breast cancer in premenopausal and postmenopausal women from 3 Colombian cities: PRECAMA &amp; POSCAMA studies

Abstract P3-12-28: Reproductive factors and molecular subtypes of breast cancer in premenopausal and postmenopausal women from 3 Colombian cities: PRECAMA &amp; POSCAMA studies

Abstract P2-11-01: Breast cancer (BC) risk among patients with benign breast disease (BBD) by NSAID use

Abstract Background: Approximately one million U.S women are diagnosed with benign breast disease (BBD) annually, which increases breast cancer (BC) risk between 1.5- and four-fold, depending on the pathologic characteristics of the lesion and other factors. BBD lesions are associated with inflammation and fibrosis, suggesting that inflammatory pathways may play a role in progression of BBD to BC. Aspirin and other non-steroidal anti-inflammatory drugs (NSAIDs) are well tolerated, widely used drugs that block inflammation by inhibiting cyclooxygenase enzymes, which lowers prostaglandin synthesis, and by exerting numerous additional anti-inflammatory effects. NSAIDs are inversely associated with several types of cancer, including BC in some, but not all studies, and have been linked to lower BC risk among BBD patients in prior publications. We examined the association between NSAID use and BC risk, overall and by self-reported history of BBD, using data from the American Cancer Society Cancer Prevention Study II (CPS-II). Methods: The Cancer Prevention Study-II (CPS-II) Nutrition Cohort is a study of approximately 189,000 U.S. men and women followed prospectively for cancer incidence and mortality. Study participants filled out a detailed questionnaire at baseline in 1992/1993 and follow-up surveys in 1997 and every two years until 2017. On each follow-up survey, participants reported type, quantity, and duration of NSAID use as well as diagnoses of BBD. Incident BCs were either self-reported and validated via medical records or cancer registry linkage, or identified via National Death Index linkage. Women in the analytic cohort were followed from date of 1997 survey until diagnosis of BC, reported use of tamoxifen, report of cancer other than BC, date of death, loss to follow-up or end of follow-up. Hazard ratios (HRs) and 95% confidence intervals (CIs) assessing associations of BBD and NSAID use with BC risk were estimated using Cox proportional hazards regression analysis. Statistical interactions between BBD and NSAID were evaluated by fitting and testing the corresponding cross-product terms, along with the main effects of each. Both NSAID use and BBD were modeled as time-dependent variables, allowing exposure status to change over the course of the multiple surveys returned. Results: Of the 60,517 women included in the study, 3,899 (6.4%) developed BC. Mean follow-up time was 12.7 years. A total of 23,661 women (39%) reported a diagnosis of BBD at some point during the study period, and 54,656 (90%) reported some NSAID use. Women self-reporting BBD were 46% more likely to develop BC than those without self-reported BBD (HR 1.46, 95% CI 1.36-1.57). Overall, BC risk was not increased among NSAID users versus non-users (HR 0.95, 95% CI 0.88-1.02). In contrast, BC risk was reduced among current NSAID users with a history of BBD (HR 0.87, 95% CI 0.78-0.97), but not among NSAID users who did not report a history of BBD (HR 1.02, 95% CI 0.92-1.13, p-interaction=0.04). Risk did not differ across number of pills per month (RR=0.84 for &amp;lt;15 pills/month, 0.89 for 15-29, 0.88 for 30-44, 0.78 for 45-59, 0.88 for 60+). Conclusion: In our study, NSAID use had a statistically-significant protective effect on BC risk among women with BBD, suggesting its potential use as a chemopreventive agent in this high-risk group of women, but not among those who did not report a history of BBD. Citation Format: Robert A Vierkant, Matthew Masters, Lauren R Teras, Mark E Sherman. Breast cancer (BC) risk among patients with benign breast disease (BBD) by NSAID use [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-11-01.

Clinical Characteristics Predict Recurrence in Borderline Ovarian Tumor Patients with Fertility-Preserving Surgery.

PurposeTo identify prognostic factors in patients with borderline ovarian tumor (BOT) and establish and validate a nomogram predicting recurrence in BOT patients treated with fertility-preserving surgery.Patients and MethodsPatients with BOT who underwent surgery at two institutions between January 2000 and June 2017 were included and categorized into training and validation cohorts. Univariate log rank test and Cox regression analysis were performed in the training cohort to identify prognostic factors, and a nomogram was developed to predict the recurrence rate. The model was validated by calculating the C-index and drawing the calibration curve and receiver operating curve (ROC).ConclusionIn the multivariate Cox regression analysis, practice period, past history of benign ovarian disease, past history of benign breast disease, elevated CA125 levels, elevated CA199 levels, surgical methods, greater omentum resection, FIGO stage, postoperative pregnancy, and re-operation were independently associated with recurrence-free survival (p<0.05). The aforementioned prognostic factors were used to develop a nomogram. The nomogram demonstrated a good ability to predict the risk of recurrence (training cohort C-index: 0.866, validation cohort C-index: 0.920). The calibration curve suggested that the predicted recurrence-free survival was closely related to the actual recurrence. ROC analysis showed that the nomogram had a good discriminatory power with the area under curve between 0.776 and 0.956. The nomogram can predict the 1-, 3-, and 5-year recurrence-free survival of BOT patients undergoing fertility-preserving surgery. The predictive model can help guide surgical plans, postoperative monitoring, and prognostic evaluation of BOT patients.

Factors associated with mammographic breast density among women in Karachi Pakistan

BackgroundThere are no studies done to evaluate the distribution of mammographic breast density and factors associated with it among Pakistani women.MethodsParticipants included 477 women, who had received either diagnostic or screening mammography at two hospitals in Karachi Pakistan. Mammographic breast density was assessed using the Breast Imaging Reporting and Data System. In person interviews were conducted using a detailed questionnaire, to assess risk factors of interest, and venous blood was collected to measure serum vitamin D level at the end of the interview. To determine the association of potential factors with mammographic breast density, multivariable polytomous logistic regression was used.ResultsHigh-density mammographic breast density (heterogeneously and dense categories) was high and found in 62.4% of women. There was a significant association of both heterogeneously dense and dense breasts with women of a younger age group < 45 years (OR 2.68, 95% CI 1.60–4.49) and (OR 4.83, 95% CI 2.54–9.16) respectively. Women with heterogeneously dense and dense breasts versus fatty and fibroglandular breasts had a higher history of benign breast disease (OR 1.90, 95% CI 1.14–3.17) and (OR 3.61, 95% CI 1.90–6.86) respectively. There was an inverse relationship between breast density and body mass index. Women with dense breasts and heterogeneously dense breasts had lower body mass index (OR 0.94 95% CI 0.90–0.99) and (OR 0.81, 95% CI 0.76–0.87) respectively. There was no association of mammographic breast density with serum vitamin D levels, diet, and breast cancer.ConclusionsThe findings of a positive association of higher mammographic density with younger age and benign breast disease and a negative association between body mass index and breast density are important findings that need to be considered in developing screening guidelines for the Pakistani population.

Menstrual factors, reproductive history, and risk of lung cancer: a multi-center population-based cohort study in Chinese females.

BackgroundFemale menstrual and reproductive factors, as remarkable indicators of hormone effect, were hypothesized to be associated with lung cancer risk, whereas the existed epidemiological evidence was inconsistent. Our study aims to investigate the association between menstrual and reproductive factors and lung cancer risk based on the Chinese Lung Cancer Screening Program.MethodsThis study was based on a large-scale multi-center population cohort across China recruiting individuals aged 40–74 years old between 2013–2018. Cox regression model was applied to estimate the HRs and 95% CIs. Restricted cubic spline (RCS) analysis was used to estimate dose-response relationships and test for nonlinear associations.ResultsAmong 553,434 female participants, 1,529 incident lung cancer cases were identified with a median follow-up of 3.61 years. With adjustment for multiple covariates and all significant hormonal factors, elevated lung cancer risk was associated with later age (15, or ≥16 years) at menarche (HR =1.27, 95% CI: 1.04–1.56; HR =1.45, 95% CI: 1.19–1.76), later age (25–29, or ≥30 years) at first live birth (HR =1.27, 95% CI: 1.13–1.43; HR =1.23, 95% CI: 1.00–1.51), and benign breast disease history (HR =1.25, 95% CI: 1.10-1.41). For postmenopausal females specifically, surgical menopause (HR =1.62; 95% CI: 1.29–2.05) and other surgeries on the reproductive system (HR =1.19; 95% CI: 1.01–1.40) both appeared to be predictive of elevated lung cancer risk. Concerning age at menopause, a nonlinear association was observed (P-nonlinear =0.0126). Increased lung cancer risk was observed among females with age at menopause especially above 50. Although we observed no significant associations between longer time (≥13 months) of breastfeeding and lung cancer risk among all participants (HR =0.86; 95% CI: 0.71–1.04), significant decreased adenocarcinoma risk (HR =0.65; 95% CI: 0.53–0.81) was noted among nonsmoking females.ConclusionsOur findings add some support for the role of menstrual and reproductive factors in lung carcinogenesis. However, these relationships were complex, and required further investigations addressing the biological mechanisms.