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

INTRODUCTION: Gynecologic 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. METHODS: A 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. RESULTS: A 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. CONCLUSION: Among 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.

Overlap of high-risk individuals predicted by family history, and genetic and non-genetic breast cancer risk prediction models: implications for risk stratification.

BACKGROUND: Family history, and genetic and non-genetic risk factors can stratify women according to their individual risk of developing breast cancer. The extent of overlap between these risk predictors is not clear. METHODS: In this case-only analysis involving 7600 Asian breast cancer patients diagnosed between age 30 and 75 years, we examined identification of high-risk patients based on positive family history, the Gail model 5-year absolute risk [5yAR] above 1.3%, breast cancer predisposition genes (protein-truncating variants [PTV] in ATM, BRCA1, BRCA2, CHEK2, PALB2, BARD1, RAD51C, RAD51D, or TP53), and polygenic risk score (PRS) 5yAR above 1.3%. RESULTS: Correlation between 5yAR (at age of diagnosis) predicted by PRS and the Gail model was low (r=0.27). Fifty-three percent of breast cancer patients (n=4041) were considered high risk by one or more classification criteria. Positive family history, PTV carriership, PRS, or the Gail model identified 1247 (16%), 385 (5%), 2774 (36%), and 1592 (21%) patients who were considered at high risk, respectively. In a subset of 3227 women aged below 50 years, the four models studied identified 470 (15%), 213 (7%), 769 (24%), and 325 (10%) unique patients who were considered at high risk, respectively. For younger women, PRS and PTVs together identified 745 (59% of 1276) high-risk individuals who were not identified by the Gail model or family history. CONCLUSIONS: Family history and genetic and non-genetic risk stratification tools have the potential to complement one another to identify women at high risk.

Gail model utilization in predicting breast cancer risk in Egyptian women: a cross-sectional study.

PURPOSE: Herein, our purpose was to calculate the 5-year and lifetime risk of breast cancer and to assess new breast cancer potential contributors among Egyptian women utilizing the modified Gail model, while presenting a global comparison of risk assessment. METHODS: This study included 7009 women from both urban and rural areas scattered across 40% of the Egyptian provinces. The 5-year risk categories were defined as low risk (≤ 1.66%) or high risk (> 1.66%), whereas the lifetime risk categories were defined as low risk (≤ 20%) or high risk (> 20%). Pearson's Chi-squared test was performed to determine the association between participants' characteristics and distinct risk categories. Binary logistic regression was carried out for correlation analysis. RESULTS: The mean estimated risk for developing invasive breast cancer over 5 years was 0.86 (± 0.67), whereas the mean lifetime breast cancer risk score was 11.26 (± 5.7). Accordingly, only 614 (8.75%) and 470 (6.7%) women were categorized as individuals with high risk of breast cancer incidence in 5-year and lifetime, respectively. Only 192 participants (2.7%) conferred both high 5-year and high lifetime risk scores. Marital status, method of feeding, physical activity behavior, contraceptive use, menopause and number of children were found to have a statistically significant association with both 5-year and lifetime breast cancer risk categories. CONCLUSION: We revealed that modified Gail model had a well-fitting and discrimination accuracy in Egyptian women when compared with other countries.

Distribution of Estimated Lifetime Breast Cancer Risk Among Women Undergoing Screening Mammography.

OBJECTIVE. Supplemental screening breast MRI is recommended for women with an estimated lifetime risk of breast cancer of greater than 20-25%. The performance of risk prediction models varies for each individual and across groups of women. The present study investigates the concordance of three breast cancer risk prediction models among women presenting for screening mammography. SUBJECTS AND METHODS. In this prospective study, we calculated the estimated lifetime risk of breast cancer using the modified Gail, Tyrer-Cuzick version 7, and BRCAPRO models for each woman who presented for screening mammography. Per American Cancer Society guidelines, for each woman the risk was categorized as less than 20% or 20% or greater as well as less than 25% or 25% or greater with use of each model. Venn diagrams were constructed to evaluate concordance across models. The McNemar test was used to test differences in risk group allocations between models, with p ≤ .05 considered to denote statistical significance. RESULTS. Of 3503 screening mammography patients who underwent risk stratification, 3219 (91.9%) were eligible for risk estimation using all three models. Using at least one model, 440 (13.7%) women had a lifetime risk of 20% or greater, including 390 women (12.1%) according to the Tyrer-Cuzick version 7 model, 18 (0.6%) according to the BRCAPRO model, and 141 (4.4%) according to the modified Gail model. Six women (0.2%) had a risk of 20% or greater according to all three models. Women were significantly more likely to be classified as having a high lifetime breast cancer risk by the Tyrer-Cuzick version 7 model compared with the modified Gail model, with thresholds of 20% or greater (odds ratio, 6.4; 95% CI, 4.7-8.7) or 25% or greater (odds ratio, 7.4; 95% CI, 4.7-11.9) used for both models. CONCLUSION. To identify women with a high lifetime breast cancer risk, practices should use estimates of lifetime breast cancer risk derived from multiple risk prediction models.

Assessment of breast cancer risk among Iraqi women in 2019.

BACKGROUND: Breast cancer is one of the most common cancers among women worldwide and the leading cause of death among Iraqi women. Breast cancer cases in Iraq were found to have increased from 26.6/100,000 in 2000 to 31.5/100,000 in 2009. The present study aims to assess the established risk factors of breast cancer among Iraqi women and to highlight strategies that can aid in reducing the incidence. METHODS: 1093 Iraqi females were enrolled in this cross-sectional study by purposive sampling methods. Data collection occurred from July 2019 to September 2019. 1500 women participated in the study, and 407 women were ultimately excluded. The questionnaire was conducted as a self-administrated form in an online survey. Ethical approval was obtained from the College of Medicine in the University of Baghdad. The Gail Model risk was calculated for each woman by the Breast Cancer Risk Assessment Tool (BCRAT), an interactive model developed by Mitchell Gail that was designed to estimate a woman's absolute risk of developing breast cancer in the upcoming five years of her life and in her lifetime. RESULTS: The ages of the participants ranged from 35 to 84 years old. The mean 5-year risk of breast cancer was found to be 1.3, with 75.3% of women at low risk and 24.7% of women at high risk. The mean lifetime risk of breast cancer was found to be 13.4, with 64.7% of women at low risk, 30.3% at moderate risk, and 5.0% at high risk. The results show that geographically Baghdad presented the highest 5-year risk, followed by Dhi Qar, Maysan, and Nineveh. However, the highest lifetime risk was found in Najaf, followed by Dhi Qar, Baghdad, and Nineveh, successively. CONCLUSION: Breast cancer is a wide-spreading problem in the world and particularly in Iraq, with Gail Model estimations of high risk in several governorates. Prevention programs need to be implemented and awareness campaigns organized in order to highlight the importance of early detection and treatment.

Accuracy of self-perceived risk perception of breast cancer development in Iranian women.

BACKGROUND: The accuracy of subjective risk perception is a matter of concern in breast cancer development. The objective of this study was to evaluate the accuracy of self-perceived risk assessment of breast cancer development and compared to actual risk in Iranian women. METHODS: The demographic, clinical, and reproductive characteristics of 800 women aged 35-85 years were collected with an in-person interview. The self-perceived risk and the actual risk were assessed using the visual analog scale (VAS) and he Gail model respectively. Gail's cutoff of 1.66% risk was used to categorize the estimated 5-year actual risk as low/average risk (< 1.66%) and high risk (≥ 1.66). In low/average risk, if the self-perceived risk > actual risk, then individuals were considered as overestimating. Similarly, in high-risk women, if the perceived risk < actual risk, then, the subjects were labeled as under-estimate; otherwise, it was labeled as accurate. The Kappa statistics were used to determine the agreement between self-perceived risk and actual risk. ROC analysis was applied to determine the accuracy of self-perceived risk in the prediction of actual risk. RESULTS: The perceived risk was significantly higher than actual risk (p = 0.001, 0.01 for 5-year and lifetime risk respectively). Both in low and high-risk groups about half of the women over-estimate and underestimate the risk by subjective risk perception. For a 5-year risk assessment, there was no agreement between perceived risk and actual risk (Kappa = 0.00, p = 0.98) but a very low agreement between them in lifetime risk assessment (Kappa = 0.09, p = 0.005). The performance of accuracy of risk perception versus actual risk was very low (AUC = 0.53, 95% CI 0.44-0.61 and AUC = 0.58, 95% CI 0.54-0.62 for the 5-year risk and lifetime risk respectively). CONCLUSION: The clinical performance of risk perception based on VAS is very poor. Thus, the efforts of the public health education program should focus on the correct perception of breast cancer risk among Iranian women.

End-to-End AUV Motion Planning Method Based on Soft Actor-Critic.

This study aims to solve the problems of poor exploration ability, single strategy, and high training cost in autonomous underwater vehicle (AUV) motion planning tasks and to overcome certain difficulties, such as multiple constraints and a sparse reward environment. In this research, an end-to-end motion planning system based on deep reinforcement learning is proposed to solve the motion planning problem of an underactuated AUV. The system directly maps the state information of the AUV and the environment into the control instructions of the AUV. The system is based on the soft actor-critic (SAC) algorithm, which enhances the exploration ability and robustness to the AUV environment. We also use the method of generative adversarial imitation learning (GAIL) to assist its training to overcome the problem that learning a policy for the first time is difficult and time-consuming in reinforcement learning. A comprehensive external reward function is then designed to help the AUV smoothly reach the target point, and the distance and time are optimized as much as possible. Finally, the end-to-end motion planning algorithm proposed in this research is tested and compared on the basis of the Unity simulation platform. Results show that the algorithm has an optimal decision-making ability during navigation, a shorter route, less time consumption, and a smoother trajectory. Moreover, GAIL can speed up the AUV training speed and minimize the training time without affecting the planning effect of the SAC algorithm.

Development of Malignancy-Risk Gene Signature Assay for Predicting Breast Cancer Risk.

BACKGROUND: Breast cancer (BC) risk assessment models are statistical estimates based on patient characteristics. We developed a gene expression assay to assess BC risk using benign breast biopsy tissue. METHODS: A NanoString-based malignancy risk (MR) gene signature was validated for formalin-fixed paraffin-embedded (FFPE) tissue. It was applied to FFPE benign and BC specimens obtained from women who underwent breast biopsy, some of whom developed BC during follow-up to evaluate diagnostic capability of the MR signature. BC risk was calculated with MR score, Gail risk score, and both tests combined. Logistic regression and receiver operating characteristic curves were used to evaluate these 3 models. RESULTS: NanoString MR demonstrated concordance between fresh frozen and FFPE malignant samples (r = 0.99). Within the validation set, 563 women with benign breast biopsies from 2007 to 2011 were identified and followed for at least 5 y; 50 women developed BC (affected) within 5 y from biopsy. Three groups were compared: benign tissue from unaffected and affected patients and malignant tissue from affected patients. Kruskal-Wallis test suggested difference between the groups (P = 0.09) with trend in higher predicted MR score for benign tissue from affected patients before development of BC. Neither the MR signature nor Gail risk score were statistically different between affected and unaffected patients; combining both tests demonstrated best predictive value (AUC = 0.71). CONCLUSIONS: FFPE gene expression assays can be used to develop a predictive test for BC. Further investigation of the combined MR signature and Gail Model is required. Our assay was limited by scant cellularity of archived breast tissue.

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

BACKGROUND: Models that accurately predict risk of breast cancer are needed to help younger women make decisions about when to begin screening. Premenopausal concentrations of circulating anti-Müllerian hormone (AMH), a biomarker of ovarian reserve, and testosterone have been positively associated with breast cancer risk in prospective studies. We assessed whether adding AMH and/or testosterone to the Gail model improves its prediction performance for women aged 35-50. METHODS: In a nested case-control study including ten prospective cohorts (1762 invasive cases/1890 matched controls) with pre-diagnostic serum/plasma samples, we estimated relative risks (RR) for the biomarkers and Gail risk factors using conditional logistic regression and random-effects meta-analysis. Absolute risk models were developed using these RR estimates, attributable risk fractions calculated using the distributions of the risk factors in the cases from the consortium, and population-based incidence and mortality rates. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory accuracy of the models with and without biomarkers. RESULTS: The AUC for invasive breast cancer including only the Gail risk factor variables was 55.3 (95% CI 53.4, 57.1). The AUC increased moderately with the addition of AMH (AUC 57.6, 95% CI 55.7, 59.5), testosterone (AUC 56.2, 95% CI 54.4, 58.1), or both (AUC 58.1, 95% CI 56.2, 59.9). The largest AUC improvement (4.0) was among women without a family history of breast cancer. CONCLUSIONS: AMH and testosterone moderately increase the discriminatory accuracy of the Gail model among women aged 35-50. We observed the largest AUC increase for women without a family history of breast cancer, the group that would benefit most from improved risk prediction because early screening is already recommended for women with a family history.

Utilization of a breast cancer risk assessment tool by internal medicine residents in a primary care clinic: impact of an educational program.

BACKGROUND: Despite strong evidence of benefit, breast cancer risk assessment and chemoprevention are underutilized by primary care physicians. This study evaluates the impact of an educational program on knowledge and utilization of the NCI Breast Cancer Risk Assessment Tool (BCRAT) by internal medicine residents. METHODS: Internal medicine residents at the primary care clinic at William Beaumont Hospital participated in an educational program on breast cancer risk assessment and chemoprevention. A questionnaire was used to assess knowledge and practice before and after participation. Electronic health records of women between the ages of 35 and 65 who were seen by participating residents for annual health exams between Dec 15, 2015 and Dec 14, 2016 were reviewed. Utilization of BCRAT by the residents was compared pre- and post-educational program. RESULTS: A total of 43 residents participated in the study. 31 (72.1%) residents reported no prior knowledge about BCRAT. The remaining 12 (27.9%) reported limited knowledge of BCRAT, but the majority of these (n = 10, 83.3%) had not used it in the last six months. For each question on the pre-educational knowledge assessment, fewer than 10% of the residents responded correctly. After implementation of the educational program, there was a significant increase in the proportion of residents who answered correctly (Range: 67 to 100%, p < 0.001). Electronic health records of 301 clinic patients were reviewed, 118 (39.2%) in the pre-educational program group and 183 (60.8%) in the post-educational program group. There was a higher use of BCRAT in the post-educational program group compared to the pre-intervention group (3.8% vs. 0%, p < 0.05). However, a majority (n = 294, 98.7%) of eligible patients from both groups did not undergo breast cancer risk assessment. CONCLUSIONS: Our study demonstrates that an educational intervention improved residents' knowledge of BCRAT. Despite this improvement, a significant proportion of patients did not undergo breast cancer risk assessment. Expanding the scope and duration of this intervention and combining it with innovative use of technology to improve utilization should be the subject of future investigation.

Breast cancer chemoprevention: An update on current practice and opportunities for primary care physicians.

Several risk assessment models have been validated for the estimation of risk of breast cancer in women. Chemoprevention through hormonal therapy is an effective way to reduce the incidence of breast cancer in women with high risk. Selective estrogen receptor modulators, tamoxifen and raloxifene, are approved for this indication by the United States Food and Drug Administration, and aromatase inhibitors have also shown promise in recent studies. These medications are generally well tolerated, except for reported increased rates of fractures and venous thromboembolic events. Despite strong recommendations from several regulatory bodies, advocacy for chemoprevention has been inadequate in practice, more so among the primary care physicians. Studies have identified several barriers in physicians, patients, and the system, contributing to this problem. Lack of knowledge about risk assessment models and chemoprevention options preclude physicians from prescribing these medications with confidence. Fear of potential adverse events, confusion regarding the purpose of the therapy, and need for continued adherence for five years are among the principal reasons for reduced chemoprevention uptake and early discontinuation among patients. Multifaceted interventions directed at education and training of health care professionals, proper counseling of women at high risk, and promotion of the development of improved medications might help ensure better chemoprevention uptake in the target population.

Assessment of performance of the Gail model for predicting breast cancer risk: a systematic review and meta-analysis with trial sequential analysis.

BACKGROUND: The Gail model has been widely used and validated with conflicting results. The current study aims to evaluate the performance of different versions of the Gail model by means of systematic review and meta-analysis with trial sequential analysis (TSA). METHODS: Three systematic review and meta-analyses were conducted. Pooled expected-to-observed (E/O) ratio and pooled area under the curve (AUC) were calculated using the DerSimonian and Laird random-effects model. Pooled sensitivity, specificity and diagnostic odds ratio were evaluated by bivariate mixed-effects model. TSA was also conducted to determine whether the evidence was sufficient and conclusive. RESULTS: Gail model 1 accurately predicted breast cancer risk in American women (pooled E/O = 1.03; 95% CI 0.76-1.40). The pooled E/O ratios of Caucasian-American Gail model 2 in American, European and Asian women were 0.98 (95% CI 0.91-1.06), 1.07 (95% CI 0.66-1.74) and 2.29 (95% CI 1.95-2.68), respectively. Additionally, Asian-American Gail model 2 overestimated the risk for Asian women about two times (pooled E/O = 1.82; 95% CI 1.31-2.51). TSA showed that evidence in Asian women was sufficient; nonetheless, the results in American and European women need further verification. The pooled AUCs for Gail model 1 in American and European women and Asian females were 0.55 (95% CI 0.53-0.56) and 0.75 (95% CI 0.63-0.88), respectively, and the pooled AUCs of Caucasian-American Gail model 2 for American, Asian and European females were 0.61 (95% CI 0.59-0.63), 0.55 (95% CI 0.52-0.58) and 0.58 (95% CI 0.55-0.62), respectively. The pooled sensitivity, specificity and diagnostic odds ratio of Gail model 1 were 0.63 (95% CI 0.27-0.89), 0.91 (95% CI 0.87-0.94) and 17.38 (95% CI 2.66-113.70), respectively, and the corresponding indexes of Gail model 2 were 0.35 (95% CI 0.17-0.59), 0.86 (95% CI 0.76-0.92) and 3.38 (95% CI 1.40-8.17), respectively. CONCLUSIONS: The Gail model was more accurate in predicting the incidence of breast cancer in American and European females, while far less useful for individual-level risk prediction. Moreover, the Gail model may overestimate the risk in Asian women and the results were further validated by TSA, which is an addition to the three previous systematic review and meta-analyses. TRIAL REGISTRATION: PROSPERO CRD42016047215 .

Prospective validation of the NCI Breast Cancer Risk Assessment Tool (Gail Model) on 40,000 Australian women.

BACKGROUND: There is a growing interest in delivering more personalised, risk-based breast cancer screening protocols. This requires population-level validation of practical models that can stratify women into breast cancer risk groups. Few studies have evaluated the Gail model (NCI Breast Cancer Risk Assessment Tool) in a population screening setting; we validated this tool in a large, screened population. METHODS: We used data from 40,158 women aged 50-69 years (via the lifepool cohort) participating in Australia's BreastScreen programme. We investigated the association between Gail scores and future invasive breast cancer, comparing observed and expected outcomes by Gail score ranked groups. We also used machine learning to rank Gail model input variables by importance and then assessed the incremental benefit in risk prediction obtained by adding variables in order of diminishing importance. RESULTS: Over a median of 4.3 years, the Gail model predicted 612 invasive breast cancers compared with 564 observed cancers (expected/observed (E/O) = 1.09, 95% confidence interval (CI) 1.00-1.18). There was good agreement across decile groups of Gail scores (χ2 = 7.1, p = 0.6) although there was some overestimation of cancer risk in the top decile of our study group (E/O = 1.65, 95% CI 1.33-2.07). Women in the highest quintile (Q5) of Gail scores had a 2.28-fold increased risk of breast cancer (95% CI 1.73-3.02, p < 0.0001) compared with the lowest quintile (Q1). Compared with the median quintile, women in Q5 had a 34% increased risk (95% CI 1.06-1.70, p = 0.014) and those in Q1 had a 41% reduced risk (95% CI 0.44-0.79, p < 0.0001). Similar patterns were observed separately for women aged 50-59 and 60-69 years. The model's overall discrimination was modest (area under the curve (AUC) 0.59, 95% CI 0.56-0.61). A reduced Gail model excluding information on ethnicity and hyperplasia was comparable to the full Gail model in terms of correctly stratifying women into risk groups. CONCLUSIONS: This study confirms that the Gail model (or a reduced model excluding information on hyperplasia and ethnicity) can effectively stratify a screened population aged 50-69 years according to the risk of future invasive breast cancer. This information has the potential to enable more personalised, risk-based screening strategies that aim to improve the balance of the benefits and harms of screening.

Breast Cancer Risk Evaluation by Utilizing Gail Model and Association between Breast Cancer Risk Perception with Early Diagnosis Applications among Midwives and Nurses Working in Primary Health Services.

OBJECTIVE: The present study was performed to detect cancer risk of the midwifes and nurses playing central role in raising awareness in the society using Gail's model. MATERIALS AND METHODS: Sample of the present cross sectional study consists of 750 volunteer midwifes and nurses in 2016. Breast cancer risk was calculated using the Gail Risk Assessment Tool. Perceived and calculated risk levels were compared. Descriptive statistics and Chi-Square analysis, t-test, Multivariate Linear Regression Analysis, the Logistic Regression Analysis were conducted. RESULTS: A mean of 5 years risk (0.8% ± 0.52) and a mean of lifetime risk (11.03% ± 4.46) were computed. It was found that risk of development of breast cancer over the next 5-years period was high for 7.1% of the midwifes and nurses. The difference between the breast cancer risk perception level of women and the breast cancer risk level according to the Gail Model was significant (p<0.01). It was determined that the midwives and nurses, who thought that they had high risks for individual breast cancer, had mammography with a higher frequency (p<0.00) and went to clinics for breast examination on a regular basis (p<0.01). CONCLUSION: Considering the fact that participants were healthcare professionals, the use of clinical breast examination and mammography practices as a preventive behavior by nurses and midwives was lower than expected.

High use of complementary and alternative medicine among a large cohort of women with a family history of breast cancer: the Sister Study.

Use of complementary and alternative medicine (CAM) is high among U.S. women, yet information is limited on use among women at increased breast cancer risk. We analyzed CAM use among women with a family history of breast cancer. CAM use was analyzed among women enrolled 2003-2009 in the Sister Study cohort. Eligible women were aged 35-74, U.S. or Puerto Rican residents, no personal history of breast cancer, and had ≥1 sister with breast cancer. Baseline data on CAM use in the past year were available for 49,734 women. Logistic regression models examined the association between CAM use and Gail Model breast cancer risk score. Results were compared to female participants in the 2007 National Health Interview Survey (n = 7965). Among Sister Study participants, there was high use of vitamin/mineral supplements (79 %), mind-body practices (41 %), manipulative/body-based practices (32 %), and botanicals (23 %). Overall use was higher than the U.S. female population. No association was observed between familial breast cancer risk and CAM use. Black women were more likely to use spirituality/meditation-based CAM modalities, while non-Hispanic white and Asian women were high users of dietary supplements. In a cohort of women with increased breast cancer risk due to family history, CAM use is higher than women in the general U.S. population and is associated with race/ethnicity. Use was not associated with breast cancer risk. Given the high prevalence of CAM use among women at risk for breast caner, research on the effectiveness of CAM use for disease prevention is needed.

Breast Cancer Risk Perceptions among Relatives of Women with Uninformative Negative BRCA1/2 Test Results: The Moderating Effect of the Amount of Shared Information.

The most common result of BRCA1/2 mutation testing when performed in a family without a previously identified mutation is an uninformative negative test result. Women in these families may have an increased risk for breast cancer because of mutations in non-BRCA breast cancer predisposition genes, including moderate- or low-risk genes, or shared environmental factors. Genetic counselors often encourage counselees to share information with family members, however it is unclear how much information counselees share and the impact that shared information may have on accuracy of risk perception in family members. We evaluated 85 sisters and daughters of women who received uninformative negative BRCA1/2 results. We measured accuracy of risk perception using a latent variable model where accuracy was represented as the correlation between perceived risk (indicators = verbal and quantitative measures) and calculated risk (indicators = Claus and BRCAPRO). Participants who reported more information was shared with them by their sister or mother about her genetic counseling session had greater accuracy of risk perception (0.707, p = 0.000) than those who reported little information was shared (0.326, p = 0.003). However, counselees shared very little information; nearly 20 % of family members reported their sister or mother shared nothing with them about her genetic counseling. Family members were generally not aware of the existence of a genetic counseling summary letter. Our findings underscore the need for effective strategies that facilitate counselees to share information about their genetic counseling sessions. Such communication may help their relatives better understand their cancer risks and enhance risk appropriate cancer prevention.

The Gail Model and Its Use in Preventive Screening: A Comparison of the Corbelli Study.

Background This study aims to determine the usage of the Gail model in screening for breast cancer during physical examinations of women by sampling primary care physicians in two regions of Texas - Hidalgo County and Johnson County. A Gail score of 1.66% or higher indicates increased breast cancer risk. Three specialties are surveyed: internal medicine (IM), family medicine (FM), and gynecology (GYN). The null hypothesis for this study is that primary care physicians do not use the Gail model in screening for breast cancer during physical examinations of women. Methods A survey was distributed to 100 physicians with specialties in IM, FM, and GYN from May 2022 to July 2022. The survey assessed the physician's frequency of use of the Gail model and chemoprevention. Data were collected by distributing survey questionnaires to physicians in person. Descriptive statistics were used for response distributions. Fisher's exact probability test was used for comparisons across specialties. Results The response rate was 34% (34/100). Thirty-eight percent of the physicians surveyed reported using the Gail model in their practice (IM 46%, FM 23%, and GYN 31%). All 13 of the physicians using the Gail model were open to using chemoprevention. Conclusions Only 38% of the physicians surveyed responded that they use the Gail model in their practice. The study concluded that a minority of primary care physicians used the Gail model to decrease breast cancer risk. Further research would help to define better the Gail model and its use in preventing breast cancer in women. The Gail model appears to be beneficial to breast cancer risk reduction; however, risk reduction medication side effects need to be minimized.

Awareness and Risk Assessment of Breast Cancer Among Women in Saudi Arabia: A Cross-Sectional Study.

Background Breast cancer is the most common female cancer worldwide including in Saudi Arabia. As a result, many cases are diagnosed at an advanced stage, leading to a poor outcome. Understanding risk perception is a significant component of awareness of breast cancer risks. It can be helpful to reduce the mortality of breast cancer via increasing awareness of the risk factors. Objective Our study was designed to assess the level of awareness among women in Saudi Arabia regarding breast cancer, including knowledge about risk factors, symptoms, and the importance of early detection. Methods A cross-sectional study was conducted, and participants were randomly selected. The target population in this study is all women in Saudi Arabia. Data was collected via an online questionnaire. The collected data was analyzed using the Statistical Package for the Social Sciences (IBM SPSS Statistics for Windows, IBM Corp., Version 21.0, Armonk, NY). Results About 713 women were enrolled in this study. Most of them (69%) were within the age group of 35-45 years old. Age at menarche was found to be 12-13 years old in 313 (43.9%) of the participants and age at first live birth was found to be 20-24 in about 360 (50.5%). The number of population with first-degree relatives that have a history of breast cancer is one relative in 126 (17.7%) of the participants and about 36 (5%) had breast biopsy. About 76 (10.7%) of the participants were considered as having a high risk of breast cancer according to the estimated five-year breast cancer-risk assessment (had a five-year breast cancer risk >1.66%). The mean knowledge score of the participants about breast cancer was found to be 4.62 ± 1.86 out of 8. About 509 (71.4%) of the participants were considered to be having a good level of knowledge about breast cancer. Long-term hormone contraceptive use and older age can increase the chance of developing breast cancer as agreed on by 363 (50.9%) and 287 (40.3%) of the participants respectively. A total of 677 (95%) of the participants mentioned that early detection is important to survive breast cancer. And 639 (89.6%) of the participants think that breast cancer is treatable and about 288 (40.4%) think that the suitable age to start mammography is above 30 years old. Conclusion There is a good general knowledge and awareness about breast cancer among study participants. There were few knowledge gaps regarding the effect of obesity, hormonal contraceptives and older age on the association with breast cancer. About 10% of the participants were found to be having high five-year breast cancer risk.

Anastrozole monotherapy further improves near-adult height after the initial combined treatment with leuprorelin and anastrozole in early-maturing girls with compromised growth prediction: results from the second phase of the GAIL study.

Background: The first phase of the GAIL study ("Girls treated with an Aromatase Inhibitor and Leuprorelin," ISRCTN11469487) has shown that the combination of anastrozole and leuprorelin for 24 months is safe and effective in improving the predicted adult height (PAH) in girls with early puberty and compromised growth prediction by +1.21 standard deviation score (SDS; +7.51 cm) compared to inhibition of puberty alone, +0.31 SDS (+1.92 cm). Objectives and hypotheses: In the second phase of the GAIL study, we assessed the adult height (AH)/near-adult height (NAH) at the end of the first phase and, in addition, the efficacy of anastrozole monotherapy thereafter in further improving NAH. Methods: We measured the AH (age 16.5 years)/NAH [bone age (BA), 15 years] of the 40 girls included, divided into two matched groups: group A (20 girls on anastrozole + leuprorelin) and group B (20 girls on leuprorelin alone). Group A was further randomized into two subgroups: A1 and A2. Group A1 (n = 10), after completion of the combined therapy, received anastrozole 1 mg/day as monotherapy until BA 14 years, with a 6-month follow-up. Group A2 (n = 10) and group B (n = 20), who received only the combined treatment and leuprorelin alone, respectively, were recalled for evaluation of AH/NAH. Results: AH or NAH exceeded the PAH at the completion of the 2-year initial phase of the GAIL study in all groups, but the results were statistically significant only in group A1: NAH-PAH group A1, +3.85 cm (+0.62 SDS, p = 0.01); group A2, +1.6 cm (+0.26 SDS, p = 0.26); and group B, +1.7 cm (+0.3 SDS, p = 0.08). The gain in group A1 was significantly greater than that in group A2 (p = 0.04) and in group B (p = 0.03). Anastrozole was determined to be safe even as monotherapy in Group A1. Conclusions: In early-maturing girls with compromised growth potential, the combined treatment with leuprorelin and anastrozole for 2 years or until the age of 11 years resulted in a total gain in height of +9.7 cm when continuing anastrozole monotherapy until the attainment of NAH, as opposed to +7.4 cm if they do not continue with the anastrozole monotherapy and +3.6 cm when treated with leuprorelin alone. Thus, the combined intervention ends at the shortest distance from the target height if continued with anastrozole monotherapy until BA 14 years.

Is using the Gail model to calculate the risk of breast cancer in the Venezuelan population justified?

Objective: To evaluate the accuracy of the Gail model (GM) in women who already have a diagnosis of breast cancer (BC) from the Breast Pathology Service, Hospital Oncology Department of the Venezuelan Social Security Institute (SOH-IVSS) in the period 2004-2014. To compare the accuracy of the GM in women aged above and below 40 years with a diagnosis of BC. Method: Descriptive, retrospective, cross-sectional, 830 records of patients diagnosed with BC were reviewed between 2004 and 2014. Results: The mean age for diagnosis of the disease was 46 ± 13 years; menarche age was 13 years ± 2; age at first birth 22 ± 5 years, with a history of biopsy 32 ± 11, the percentage of relatives with a primary history of BC reported (PHBC) 9.3%. Only 41% of women with a diagnosis of BC reported Gail >1.67 (positive Gail). In the dichotomous logistic regression that related positive Gail with the independent variables, it was observed: greater probability of positive Gail if menarche age <11 years (p < 0.036), PHBC (p = 0.005), previous biopsy (p = 0.007), age at first birth 25-29 years (p = 0.019). When stratifying by age, unlike the bivariate analysis, women over 40 years of age are more likely to have a positive Gail in menarche age <11 years (p = 0.008), PHBC (p = 0.001), previous biopsy (p = 0.025) when compared with younger women, the age at first birth between 25 and 29 years was statistically significant for both groups; however, the probability was higher in younger women (p = 0.008). Conclusion: There is no conclusive evidence to consider that the GM is applicable to Venezuelan women due to its low precision since it only identified 41% of the patients who had BC as high risk; however, when the factors are analysed separately, we found a higher probability of a positive Gail with statistical significance in EM <11 years, PHBC, previous biopsy and age at first birth 25-29 years; When stratifying by age, we observed that the age at first birth 25-29 years in women aged 40 or less increases the probability of a positive Gail. It is necessary to develop new risk assessment models that are adapted to our female population.