Estrogens are involved in a number of physiological functions, including in the development of the brain, growth, reproduction and metabolism. The biological actions of estrogens are achieved by binding to estrogen receptors (ERs) in numerous types of tissues. ERα and ERβ belong to the nuclear receptor superfamily and the G‑protein coupled ER1 (GPER1) is a membrane receptor. The primary biologically active estrogen, 17β‑estradiol demonstrates a high affinity for ERs. Mechanistically, estrogens bind to the ERs in the nucleus, and the complex then dimerize and bind to estrogen response elements (EREs) located in the promoter regions of the target genes. This is referred to as the genomic mechanism of ERs\' function. Furthermore, ERs can also act through kinases and other molecular interactions leading to specific gene expression and functions, referred to as the non‑genomic mechanism. While ERα and ERβ exert their functions via both genomic and non‑genomic pathways, GPER1 exerts its function primarily via the non‑genomic pathways. Any aberrations in ER signaling can lead to one of a number of diseases such as disorders of growth and puberty, fertility and reproduction abnormalities, cancer, metabolic diseases or osteoporosis. In the present review, a focus is placed on three target tissues of estrogens, namely the bones, the breasts and the brain, as paradigms of the multiple facets of the ERs. The increasing prevalence of breast cancer, particularly hormone receptor‑positive breast cancer, is a challenge for the development of novel antihormonal therapies other than tamoxifen and aromatase inhibitors, to minimize toxicity from the long treatment regimens in patients with breast cancer. A complete understanding of the mechanism of action of ERs in bones may highlight options for novel targeted treatments for osteoporosis. Likewise, the aging of the brain and related diseases, such as dementia and depression, are associated with a lack of estrogen, particularly in women following menopause. Furthermore, gender dysphoria, a discordance between experienced gender and biological sex, is commonly hypothesized to emerge due to discrepancies in cerebral and genital sexual differentiation. The exact role of ERs in gender dysphoria requires further research.

OBJECTIVE: Observational cohort studies are used to evaluate the effectiveness of screening mammography in women offered screening. Because screening mammography has no effect on causes of death other than breast cancer (BC), cohort studies should show reductions in the risk of BC death substantially greater than possible reductions in the risk of all-cause death. We assessed the risk of BC deaths and of all-cause (or of nonBC) deaths associated with screening mammography attendance reported in cohort studies.
METHODS: Cohort studies published from 2002 to 2022 on women invited to screening mammography were searched in PubMed, Web of Sciences, Scopus, and in review articles. Random effect meta-analyses were performed using relative risks (RRs) of death between women who attended screening compared to women who did not attend screening.
RESULTS: Eighteen cohort studies were identified, nine that reported RRs of BC deaths only, five that reported RRs of all-cause deaths only, and four that reported RRs for both BC deaths and all-cause deaths. The latter four cohort studies reported 12-76 times more all-cause deaths than BC deaths. The random-effect summary of RR for BC mortality in screening attendees vs nonattendees was 0.55 (95% CI: 0.50-0.60) in 13 cohort studies. The summary of RR for all-cause mortality was 0.54 (0.50-0.58) in 10 cohort studies. In the four cohort studies that evaluated both outcomes, the summary of RRs were 0.63 (0.43-0.83) for BC mortality and of 0.54 (0.44-0.64) for all-cause mortality.
CONCLUSIONS: The similar relative reductions in breast- and all-cause (or nonBC) mortality indicates that screening mammography attendance is an indicator of characteristics associated with a lower risk of dying from any cause, including from BC, which observational studies have falsely interpreted as a screening effect.

Breast sarcoma is a rare but aggressive tumor. There are few case reports in the literature and several aspects of this disease are still not completely comprehended. Therefore, reporting new cases can help to enrich the literature. We report a patient with breast mass and pus secretion from her right breast, misdiagnosed as an abscess and mistreated by antibiotics. The patient was referred for an ultrasound examination and mammography, and a needle biopsy was performed that suggested an aggressive tumor. By the pathologist\'s suggestion, a total mastectomy of the right breast was performed with the excision of sentinel nodes. A pathological examination revealed a high-grade undifferentiated pleomorphic sarcoma (UPS) without vascular or lymph node invasion as the final diagnosis. The patient underwent postoperative chemotherapy and is currently in good condition. This case emphasizes considering this rare tumor when approaching a breast mass. Performing surgery with adequate resection margin can improve the patient\'s prognosis. Some suggested breast UPS cases with lung and brain metastasis would be more aggressive tumors than other breast sarcomas. Total mastectomy with negative margins and free-of-tumor lymph nodes may be the key to improve prognosis in such patients.

BACKGROUND: Not all the breast lesions were mass-like, some were non-mass-like at ultrasonography. In these lesions, conventional ultrasonography had a high sensitivity but a low specificity. Sonoelastography can evaluate tissue stiffness to differentiate malignant masses from benign ones. Then what about the non-mass lesions? The aim of this study was to evaluate the current accuracy of sonoelastography in the breast non-mass lesions and compare the results with those of the American College of Radiology breast Imaging-Reporting and Data System (BI-RADS).
METHODS: An independent literature search of English medical databases, including PubMed, Web of Science, Embase & MEDLINE (Embase.com) and Cochrane Library, was performed by 2 researchers. The accuracy of sonoelastography was calculated and compared with those of BI-RADS.
RESULTS: Fourteen relevant studies including 1058 breast non-mass lesions were included. Sonoelastography showed a pooled sensitivity of 0.74 (95% CI: 0.70-0.78), specificity of 0.89 (95% CI: 0.85-0.91), diagnostic odds ratio (DOR) of 25.22 (95% CI: 17.71-35.92), and an area under the curve of 0.9042. Eight articles included both sonoelastography and BI-RADS. The pooled sensitivity, specificity, DOR and AUC were 0.69 versus 0.91 (P < .01), 0.90 versus 0.68 (P < .01), 19.65 versus 29.34 (P > .05), and 0.8685 versus 0.9327 (P > .05), respectively.
CONCLUSIONS: Sonoelastography has a higher specificity and a lower sensitivity for differential diagnosis between malignant and benign breast non-mass lesions compared with BI-RADS, although there were no differences in AUC between them.

Primary mucinous cystadenocarcinoma (MCA) of the breast is a rare variant of breast carcinoma. A 68-year-old female patient presented to the general surgery clinic with pain and swelling in the right breast. A mass was detected in the upper outer quadrant, and a fine-needle aspiration biopsy was performed. The May-Grünwald Giemsa stained slides showed aggregates of mucin-rich pleomorphic cells with large nuclei in a mucinous background containing discohesive single cells. The Papanicolaou stain revealed a papillary structure composed of malignant epithelial cells in a necrotic background. A modified radical mastectomy was performed, and upon gross examination, two tumors were discovered in the central and upper outer quadrants. The first tumor, located centrally, was identified as invasive lobular breast carcinoma. The second tumor was an MCA with cytokeratin 7(+) and cytokeratin 20(-), and was determined to be the primary MCA of the breast based on clinical and radiological information. Immunohistochemistry revealed that the tumor cells were negative for estrogen receptor and progesterone receptor, and HER2 was 2+. Fluorescence in situ hybridization analysis detected HER2 gene amplification. During the 72-month follow-up, there were no findings compatible with recurrence or new metastasis. Although primary MCA is rare, it causes differential diagnosis problems and has different biological behaviors.

Histiocytic sarcoma (HS) is a rare cancerous tumor that originates from fully developed histiocytes. It is most often identified by the presence of certain proteins such as the cluster of differentiation (CD) 68, CD163, or lysozyme. HS has been recorded in different sites outside of the lymph nodes such as the gastrointestinal tract, nasal cavities, skin, and bone marrow. Because HS shares similar clinical features with other forms of malignant diseases, diagnosing it becomes incredibly challenging. We report a case of a 40-year-old female who presented with a breast mass for one year. A preliminary diagnosis of a phyllodes tumor was made. However, the morphology along with the immunophenotype picture was diagnostic for HS. Microscopic features showed a well-defined neoplastic growth arranged in sheets and fascicles. Diffuse immunoreactivity was seen for CD45, CD4, CD68, CD163, and vimentin. We present the important histopathological and immunohistochemical characteristics of the tumor in this case.

BACKGROUND: The two most common types of breast cancer are invasive or infiltrating ductal carcinoma (IDC) and invasive or infiltrating lobular carcinoma (ILC) (Pestalozzi et al., J. Clin. Oncol., 26, 2008, 3006). Between 5% and 15% of invasive breast carcinomas are lobular carcinomas (Pestalozzi et al., J. Clin. Oncol., 26, 2008, 3006; Dossus and Benusiglio, Breast Cancer Res., 17, 2015, 37; Braunstein et al., Breast Cancer Res. Treat., 149, 2015, 555). The paucity of data relating to recurrence rates of lobular cancers prompted this study.
METHODS: A retrospective cohort study of all cases of lobular breast carcinoma reported to the Western Australia Cancer Registry with the clinical and pathological details between 2000 and 2014.
RESULTS: Overall, 2463 subjects with a total of 2526 events of invasive lobular carcinoma of the breast. 11/2463 (0.45%) subjects met criteria for local recurrence of invasive lobular breast cancer, with an incidence of 1 in 224.
CONCLUSIONS: There are clinical implications for the management and follow-up for patients with a diagnosis of lobular cancer of the breast. Due to the low recurrence rate, now, the standard practice in our institution does not offer magnetic resonance imaging (MRI) as part of the follow-up for ILC patients. Other centres should establish local recurrence rates to aid development of appropriate management protocols.

Myofibroblastic sarcoma is a malignancy in which myofibroblasts are the main component, with a very low incidence. In this study, we report a case of low-grade myofibroblastic sarcoma (LGMS) in the breast. After the diagnosis of LGMS, the patient received a mastectomy. The patient showed no relapse or progression during the follow-up time of 3 months following the operation. LGMS in the breast is extremely rare, and the limited experience with its diagnosis and treatment brings obstacles to doctors. Therefore, this report summarizes the preoperative diagnosis, treatment, and prognosis of breast LGMS through a literature review.

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

Breast cancer is a leading cause of mortality among women, both in India and globally. The prevalence of breast masses is notably common in women aged 20 to 60. These breast masses are classified, according to the breast imaging-reporting and data systems (BI-RADS) standard, into categories such as fibroadenoma, breast cysts, benign, and malignant masses. To aid in the diagnosis of breast disorders, imaging plays a vital role, with mammography being the most widely used modality for detecting breast abnormalities over the years. However, the process of identifying breast diseases through mammograms can be time-consuming, requiring experienced radiologists to review a significant volume of images. Early detection of breast masses is crucial for effective disease management, ultimately reducing mortality rates. To address this challenge, advancements in image processing techniques, specifically utilizing artificial intelligence (AI) and machine learning (ML), have tiled the way for the development of decision support systems. These systems assist radiologists in the accurate identification and classification of breast disorders. This paper presents a review of various studies where diverse machine learning approaches have been applied to digital mammograms. These approaches aim to identify breast masses and classify them into distinct subclasses such as normal, benign and malignant. Additionally, the paper highlights both the advantages and limitations of existing techniques, offering valuable insights for the benefit of future research endeavors in this critical area of medical imaging and breast health.