Gail Model Underestimates Breast Cancer Risk in Thai Population

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.

Expression-Based Diagnosis, Treatment Selection, and Drug Development for Breast Cancer

There is currently no gene expression assay that can assess if premalignant lesions will develop into invasive breast cancer. This study sought to identify biomarkers for selecting patients with a high potential for developing invasive carcinoma in the breast with normal histology, benign lesions, or premalignant lesions. A set of 26-gene mRNA expression profiles were used to identify invasive ductal carcinomas from histologically normal tissue and benign lesions and to select those with a higher potential for future cancer development (ADHC) in the breast associated with atypical ductal hyperplasia (ADH). The expression-defined model achieved an overall accuracy of 94.05% (AUC = 0.96) in classifying invasive ductal carcinomas from histologically normal tissue and benign lesions (n = 185). This gene signature classified cancer development in ADH tissues with an overall accuracy of 100% (n = 8). The mRNA expression patterns of these 26 genes were validated using RT-PCR analyses of independent tissue samples (n = 77) and blood samples (n = 48). The protein expression of PBX2 and RAD52 assessed with immunohistochemistry were prognostic of breast cancer survival outcomes. This signature provided significant prognostic stratification in The Cancer Genome Atlas breast cancer patients (n = 1100), as well as basal-like and luminal A subtypes, and was associated with distinct immune infiltration and activities. The mRNA and protein expression of the 26 genes was associated with sensitivity or resistance to 18 NCCN-recommended drugs for treating breast cancer. Eleven genes had significant proliferative potential in CRISPR-Cas9/RNAi screening. Based on this gene expression signature, the VEGFR inhibitor ZM-306416 was discovered as a new drug for treating breast cancer.

Lightweight Separable Convolution Network for Breast Cancer Histopathological Identification

Breast cancer is one of the leading causes of death among women worldwide. Histopathological images have proven to be a reliable way to find out if someone has breast cancer over time, however, it could be time consuming and require much resources when observed physically. In order to lessen the burden on the pathologists and save lives, there is need for an automated system to effectively analysis and predict the disease diagnostic. In this paper, a lightweight separable convolution network (LWSC) is proposed to automatically learn and classify breast cancer from histopathological images. The proposed architecture aims to treat the problem of low quality by extracting the visual trainable features of the histopathological image using a contrast enhancement algorithm. LWSC model implements separable convolution layers stacked in parallel with multiple filters of different sizes in order to obtain wider receptive fields. Additionally, the factorization and the utilization of bottleneck convolution layers to reduce model dimension were introduced. These methods reduce the number of trainable parameters as well as the computational cost sufficiently with greater non-linear expressive capacity than plain convolutional networks. The evaluation results depict that the proposed LWSC model performs optimally, obtaining 97.23% accuracy, 97.71% sensitivity, and 97.93% specificity on multi-class categories. Compared with other models, the proposed LWSC obtains comparable performance.

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

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

Performance of the IBIS/Tyrer-Cuzick model of breast cancer risk by race and ethnicity in the Women's Health Initiative

BACKGROUND

The IBIS/Tyrer-Cuzick model is used clinically to guide breast cancer screening and prevention, but was developed primarily in non-Hispanic White women. Little is known about its long-term performance in a racially/ethnically diverse population.

METHODS

The Women's Health Initiative study enrolled postmenopausal women from 1993-1998. Women were included who were aged <80 years at enrollment with no prior breast cancer or mastectomy and with data required for IBIS/Tyrer-Cuzick calculation (weight; height; ages at menarche, first birth, and menopause; menopausal hormone therapy use; and family history of breast or ovarian cancer). Calibration was assessed by the ratio of observed breast cancer cases to the number expected by the IBIS/Tyrer-Cuzick model (O/E; calculated as the sum of cumulative hazards). Differential discrimination was tested for by self-reported race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Asian or Pacific Islander, and American Indian or Alaskan Native) using Cox regression. Exploratory analyses, including simulation of a protective single-nucleotide polymorphism (SNP), rs140068132 at 6q25, were performed.

RESULTS

During follow-up (median 18.9 years, maximum 23.4 years), 6783 breast cancer cases occurred among 90,967 women. IBIS/Tyrer-Cuzick was well calibrated overall (O/E ratio = 0.95; 95% CI, 0.93-0.97) and in most racial/ethnic groups, but overestimated risk for Hispanic women (O/E ratio = 0.75; 95% CI, 0.62-0.90). Discrimination did not differ by race/ethnicity. Exploratory simulation of the protective SNP suggested improved IBIS/Tyrer-Cuzick calibration for Hispanic women (O/E ratio = 0.80; 95% CI, 0.66-0.96).

CONCLUSIONS

The IBIS/Tyrer-Cuzick model is well calibrated for several racial/ethnic groups over 2 decades of follow-up. Studies that incorporate genetic and other risk factors, particularly among Hispanic women, are essential to improve breast cancer-risk prediction.

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.

Expression of long-form prolactin receptor is associated with lower disease-free and overall survival in node-negative breast cancer patients

Background

Breast cancer is the most frequent female malignancy in Thailand. Prolactin (PRL) and prolactin receptor (PRLR) play an important role in normal breast development and carcinogenesis of breast cancer. There are two major isoforms of PRLR, consisting of long-form (LF-PRLR) and short-form (SF-PRLR) that stimulate different signaling pathways. This study aims to explore the associations between all PRLR isoforms (all-PRLR) and LF-PRLR with clinicopathological parameters in breast cancer patients.

Methods

A total of 340 patients were recruited from January 2009 to December 2015. Expressions of PRLR in breast cancer tissue were determined by immunohistochemistry using specific antibodies that recognize different domains of PRLR (B6.2 for all-PRLR and H-300 for LF-PRLR). The associations between all-PRLR and LF-PRLR expressions with clinicopathological parameters were evaluated.

Results

Expression of all-PRLR was observed in 86.2% of all patients while LF-PRLR expression was observed in 54.4%. All-PRLR was co-expressed with estrogen receptor (ER) and progesterone receptor (PR). LF-PRLR expression was associated with high grade tumor and human epidermal growth factor receptor-2 (HER2) overexpression (P=0.010 and <0.001, respectively). Subgroup analysis revealed that LF-PRLR expression was the independent predictor for lower disease-free survival (DFS) in node-negative breast cancer patients with high expression of all-PRLR [hazard ratio (HR): 5.224, 95% confidence interval (CI): 1.089-25.064, P=0.039].

Conclusions

The presence of LF-PRLR in the patients with high expression of all-PRLR was associated with adverse outcome. Evaluation of all-PRLR and LF-PRLR might be used as novel prognosticators in node-negative breast cancers.

Risk-predicted dual nomograms consisting of clinical and ultrasound factors for downgrading BI-RADS category 4a breast lesions - A multiple centre study

Purpose: To develop and to validate a risk-predicted nomogram for downgrading Breast Imaging Reporting and Data System (BI-RADS) category 4a breast lesions. Patients and Methods: We enrolled 680 patients with breast lesions that were diagnosed as BI-RADS category 4a by conventional ultrasound from December 2018 to June 2019. All 4a lesions were randomly divided into development and validation groups at the ratio of 3:1. In the development group consisting of 499 cases, the multiple clinical and ultrasound predicted factors were extracted, and dual-predicted nomograms were constructed by multivariable logistic regression analysis, named clinical nomogram and ultrasound nomogram, respectively. Patients were twice classified as either "high risk" or "low risk" in the two nomograms. The performance of these dual nomograms was assessed by an independent validation group of 181 cases. Receiver Operating Characteristic (ROC) curve and diagnostic value were calculated to evaluate the applicability of the new model. Results: After multiple logistic regression analysis, the clinical nomogram included 2 predictors: age and the first-degree family members with breast cancer. The area under the curve (AUC) value for the clinical nomogram was 0.661 and 0.712 for the development and validation groups, respectively. The ultrasound nomogram included 3 independent predictors (margins, calcification and strain ratio), and the AUC value in this nomogram was 0.782 and 0.747 in the development and validation groups, respectively. In the development group of 499 patients, approximately 50.90% (254/499) of patients were twice classified "low risk", with a malignancy rate of 1.18%. In the validation group of 181 patients, approximately 47.51% (86/181) of patients had been twice classified as "low risk", with a malignancy rate of 1.16%. Conclusions: A dual-predicted nomogram incorporating clinical factors and imaging characteristics is an applicable model for downgrading the low-risk lesions in BI-RADS category 4a and shows good stability and accuracy, which is useful for decreasing the rate of invasive examinations and surgery.

Expression of androgen receptor and its regulatory molecule Lin28 in non-luminal subtype breast cancer

Androgen receptor (AR) was associated with favourable outcome in luminal breast cancer. However, the role of AR in non-luminal breast cancer remains inconclusive. The aim of the present study was to evaluate the clinical significance of the AR and its regulatory pathway in non-luminal subtypes of breast cancer. In total, 284 breast cancer patients were recruited from January 2007 to January 2016. Tissue microarrays were constructed from archival paraffin blocks and assessed for AR and its regulatory molecule, Lin28, by immunohistochemistry. The association between AR and Lin28 expression and clinicopathological parameters was analyzed. Results showed that AR and Lin28 were co-expressed. No association between these proteins and clinicopathological parameters, and survival outcome was found. However, a higher proportion of the patients with AR and Lin28 expression were observed in HER2 subtype. In conclusion, Lin28 may be a novel marker for prognosis and targeted for treatment in HER2 subtype breast cancer.