BRCA1/2 pathogenic variant carriers face a high risk of breast cancer, making early detection vital for minimizing systemic treatments. Contrast-enhanced magnetic resonance imaging (MRI) outperforms mammography and ultrasound in detecting lesions that are often missed, particularly in individuals with BRCA1 or BRCA2 variants. However, the effectiveness of MRI-guided biopsy remains unclear. Thus, the aim was to evaluate the effectiveness of MRI-guided biopsy in detecting malignancy among BRCA1/2 pathogenic variant carriers with MRI-only-detected breast lesions and compare these findings with those in non-carriers and assess lesion characteristics and diagnostic yield. We retrospectively analyzed. We compared the effectiveness of MRI-guided biopsy for BRCA1/2 pathogenic variant carriers with MRI-only-detected lesions with that of non-carriers between April 2018 and December 2022. We examined the clinicopathological characteristics and MRI findings of the BRCA1/2 carriers. A total of 130 lesions from 126 patients were reviewed. The BRCA1/2 mutation group had a significantly higher incidence of category 3 lesions on MRI. Invasive carcinoma was more prevalent among BRCA1/2 carriers, and non-carriers predominantly presented with ductal carcinoma in situ. MRI-guided biopsy identified malignant tumors in 30.1% of lesions. The positive predictive values were 42.9% for BRCA1/2 carriers and 28.6% for non-carriers. MRI-guided biopsy was effective in detecting early-stage invasive carcinoma in BRCA1/2 carriers, highlighting its role in tailored surveillance strategies. For new lesions categorized as breast imaging reporting and data system 3 on MRI, biopsy should be considered, particularly for BRCA1/2 carriers. Prospective studies are needed to validate these findings and assess long-term clinical outcomes to inform personalized management approaches for high-risk populations.

To evaluate the feasibility of using deep learning models applied to digital breast tomosynthesis (DBT) images for non-invasive prediction of breast cancer biomarkers, including estrogen receptor (ER), progesterone receptor (PR), human epithelial growth factor receptor 2 (HER2), Ki-67 proliferation index, and triple-negative breast cancer (TNBC). In this retrospective study, patients with histopathologically-confirmed, invasive breast cancer were included. Furthermore, all included patients had complete, immunohistochemically-assessed biomarker data available. For each case, a representative DBT slice showing the tumor was selected and preprocessed using histogram equalization. Two pretrained convolutional neural networks (VGG19 and ResNet50) were fine-tuned for binary classification of each biomarker. Model performance was evaluated using accuracy, area under the curve (AUC), F1 score, and Matthews correlation coefficient. The study sample included 43 anonymized female patients. Deep learning models achieved strong predictive performance for ER (AUC = 0.81) and TNBC (AUC = 0.93). HER2 (AUC = 0.74) and Ki-67 index (AUC = 0.70) were predicted with moderate accuracy. PR results varied, with VGG19 reaching AUC = 0.76 while ResNet50 performed poorly (AUC = 0.24). Deep learning models applied to DBT images enabled non-invasive prediction of some key breast cancer biomarkers, especially ER status and TNBC type. This approach may function as a virtual biopsy to complement histopathology, guide biopsy targeting, and support treatment planning. Although preliminary, the findings highlight the potential of artificial intelligence-enhanced DBT assessment and warrant validation in larger, multi-center prospective studies.

The breast constitutes an essential component of a woman's identity, body image, and self-esteem. The objective of this article is to present a rare case of breast skin pathology and to examine its differential diagnosis. A fifty-year-old woman consulted our department for a burning sensation in her right breast in combination with a rash involving the nipple-areola complex. The patient received treatment for evolving bacterial mastitis; however, a zosteriform vesicular rash subsequently developed over the right scapular region. The diagnosis of varicella zoster virus infection was confirmed, and oral medication was adjusted to antivirals, resulting in progressive reduction of the rash. To the best of our knowledge, reports on herpes zoster involving the nipple are scarce in the literature. This article presents such an atypical manifestation, underscoring the importance of including herpes zoster in the differential diagnosis of nipple-areolar complex lesions, and provides a brief review of the relevant literature.

Juvenile giant fibroadenoma (GFA) is defined as a benign tumor larger than 5 cm, 500 grams, and/or involving at least 80% of the breast. It typically occurs in young patients and causes breast deformity and asymmetry. Surgical treatment involves resection of the tumor (enucleation), rearrangement of the skin envelope, and repositioning of the nipple-areola complex. However, the expected re-expansion of the breast following tumor removal, often managed through periareolar approaches, can be unpredictable and prolonged in certain cases. For this reason, oncoplastic surgery techniques have been developed, which allow for immediate partial reconstruction and are now among the available therapeutic options. This report describes three cases in which an oncoplastic approach was used for the treatment of GFA.

Hypofractionated radiotherapy (RT) is the standard adjuvant treatment for breast cancer patients after surgery. The recent results of the FAST-FORWARD trial on ultra-hypofractionated RT, delivered over one week, support a viable alternative regimen for early-stage breast cancer. Whether the addition of a tumor bed boost could further improve patient outcomes is still under investigation. We report the results of a single-center prospective study involving 26 early-stage (T1, 2N0) breast cancer patients treated with whole-breast RT consisting of five daily fractions of 5.2 Gy (FAST-FORWARD regimen) followed by a tumor-bed boost of three daily fractions of 3 Gy. Grade 1 early breast toxicity (skin changes and altered breast consistency) was documented in 20% of patients within the first 3 months after treatment completion. No events of acute pneumonitis were reported. Whole-breast and tumor-bed boost volumes did not affect the occurrence of breast toxicity. Minimal radiation-induced lung injury (grade 1) was noted in 95.8% of patients, while one patient (4.2%) developed grade 2 lung toxicity, which was later downgraded to grade 1 at the 12-month post-RT time point. With a median follow-up of 72 months, none of the patients presented with locoregional recurrence or distant metastases. The present study highlights the safety of a hypofractionated RT boost to the tumor bed after ultra-hypofractionated whole-breast RT. No clear evidence exists to date regarding the superiority of delivering a tumor bed boost after ultra-hypofractionated RT or the specific patient subgroups to which a boost should be prescribed.

High mobility group box 1 (HMGB1) is a nonhistone chromatin-associated protein involved in chromatin remodeling, transcription, DNA replication, and repair. The purpose of this study was to assess the relationship between tissue expression of HMGB1, clinical outcomes, and histopathological characteristics in patients with breast cancer. The study included 282 patients with breast cancer. An in vitro diagnostic HMGB1 antibody was applied to the slides of tumor specimens. Overexpression of HMGB1 was found in tumor cells of 123 (43.6%) patients. HMGB1 was only expressed in the nucleus in most tumors (88.7%), while in 32 (11.3%) tumors HMBG1 expression was cytoplasmic and/or extracellular. Severe inflammatory infiltration of the peritumoral stroma was observed in 76 (27%) patients. There was a correlation between remarkable inflammatory cell infiltration in the tumor microenvironment and HMGB1 overexpression, regardless of the molecular subtype, as well as the extranuclear location of HMGB1 expression (p = 0.023). HMGB1 expression was not found to be associated with overall or disease-free survival. However, axillary lymph node metastasis was significantly more common in tumors with intense inflammation (p = 0.024). The proportion of breast cancer patients with HMGB1 expression was lower in the present study than that reported previously. Furthermore, we did not detect a relationship between HMGB1 expression and prognosis. However, the relationship between HMGB1 expression and prognosis had been previously reported only in aggressive breast cancers. It is suggested that understanding the significance of HMGB1 expression in breast cancer may open new treatment opportunities, especially in aggressive and/or triple negative tumors.

Over the recent years, machine learning (ML) models have been increasingly used in predicting breast cancer survival because of improvements in ML algorithms. However, cancer researchers still face a significant challenge in accurately predicting breast cancer patients' survival rates. The purpose was to predict breast cancer survival using a Bayesian network. This retrospective study included 2,995 patients diagnosed with breast cancer and subsequently hospitalized between January 1, 2012, and December 30, 2024. SPSS Modeler version 18.0 was used to build prediction models. The data were randomly split into a training set (2,097 cases, 70%) and a test set (898 cases, 30%) for developing the Bayesian network model and predicting the overall survival of patients diagnosed with breast cancer. The model included demographic variables (age, marital status, and governorate), laboratory/clinical variables (hemoglobin level, white blood cell count, presence of hypertension, and diabetes mellitus) and the outcome variable, patient survival status (binary value: survived/died). The discriminative ability of models was evaluated by accuracy and the area under the curve (AUC) in terms of superior predictive performance for breast cancer outcomes. The Bayesian model exhibited the best discriminatory performance among the nine models, with an AUC of 0.859 and the highest accuracy of 96.661%. In the context of feature importance, white blood cell value at the time of diagnosis was the most important feature for predicting the survival of breast cancer. Patients who had below-normal hemoglobin and above-normal white blood count values had a higher death probability than patients who had normal white blood count and hemoglobin values. The presence of hypertension and diabetes mellitus in patients with breast cancer led to a reduced survival probability. The Bayesian model outperformed the other models in predicting the survival probability of breast cancer. Routine laboratory testing and demographic data can be included in a ML model to predict breast cancer survival. Accurate prediction of breast cancer survival is vital for clinical decision-making.

The mammary angiosarcoma is a rare malignant mesenchymal tumor that develops from the vascular tissue of the breast. It represents 0.004 to 1% of all malignant breast tumors and 8 to 10% of breast sarcomas. It can be primary in a 40-year-old woman or radiation-induced in an older woman who has undergone conservative treatment for breast cancer, including conservative surgery and adjuvant radiotherapy. Herein, we present four cases involving breast angiosarcoma in young and relatively older women and the different treatment they received. Our discussion encompasses the epidemiological, diagnostic, and therapeutic facets of this rare and aggressive tumor type.

Having good knowledge and performing regular pre-tests under physician supervision play a crucial role in the early detection of breast cancer. The aim of this study was to investigate the level of awareness, frequency of performing routine screening, types of screening methods prior to detection, and who detected the case, among women diagnosed with breast cancer. A cross-sectional study that used a designed questionnaire applied to investigate demographic data and four other aspects: level of awareness, screening practices, type of screening methods used, and who detected the case for the first time. Women who were diagnosed with breast cancer and registered at Nanakali Hospital were included. A total of 150 women participated. Most of the participants (80%) had no previous knowledge regarding causes, signs and symptoms, or detection methods, while only 20% had little information. Among the participants, most (87.3%) did not undergo any pre-tests before the time of diagnosis, while only 12.7% did pre-test at least once. The screening methods used prior to the diagnosis were: breast self-exam (n=9); ultrasonography (n=8), and only two had mammography. Detecting the case for the first time, 68.7% of the cases were detected by chance or accidentally, and 31.3% were detected by physicians. The level of awareness and performance of routine screening differ greatly among different populations and countries. Women in Erbil, generally have a low level of awareness and insufficient knowledge regarding breast cancer; most women do not undertake any regular screening for early detection of this cancer compared to Western countries. Having previous knowledge and doing pre-tests regularly play a key role in the early detection of this cancer, which minimizes the consequences.