Influence of BI-RADS Breast Density Scores on the Implementation of Supplemental Imaging Modalities in Those With Average Risk and Negative Mammogram by Primary Care Providers in British Columbia.

Breast Imaging-Reporting and Data System (BI-RADS) density scores have been included in screening mammography reports in BC since 2018. Despite these density scores being present in screening mammography reports for numerous years, there remains insufficient evidence to guide supplemental testing for patients with dense breasts. The primary objective of this study was to evaluate how primary care providers in Canada utilize BI-RADS density scores reported on normal screening mammograms of average risk, asymptomatic patients in their clinical practice.

Interhospital variations in diagnostic work-up following recall at biennial screening mammography-a population-based study.

Objectives: Quality control in breast cancer screening programmes has been subject of several studies. However, less is known about the clinical diagnostic work-up in recalled women with a suspicious finding at screening mammography. The current study focuses on interhospital differences in diagnostic work-up strategies.

Invasive cervical root resorption in a cancer patient: A rare case report with 2 years of follow-up and literature review.

Introduction: Cases involving invasive cervical root resorption (ICRR) in oncological patients are rare, in addition, follow-up of these patients has not yet been reported in the literature.

Objective: This study aims to present a literature review and report a case of denosumab as a possible cause of ICRR in a patient with breast cancer with 2 years of follow-up.

Case Report: A 39-year-old female with a history of luminal breast cancer was treated with denosumab semiannually for osteopenia with discontinuation 1 year ago.

Automated Identification of Breast Cancer Relapse in Computed Tomography Reports Using Natural Language Processing.

Purpose: Breast cancer relapses are rarely collected by cancer registries because of logistical and financial constraints. Hence, we investigated natural language processing (NLP), enhanced with state-of-the-art deep learning transformer tools and large language models, to automate relapse identification in the text of computed tomography (CT) reports.

Methods: We analyzed follow-up CT reports from patients diagnosed with breast cancer between January 1, 2005, and December 31, 2014.

Quantifying interpretation reproducibility in Vision Transformer models with TAVAC.

Deep learning algorithms can extract meaningful diagnostic features from biomedical images, promising improved patient care in digital pathology. Vision Transformer (ViT) models capture long-range spatial relationships and offer robust prediction power and better interpretability for image classification tasks than convolutional neural network models. However, limited annotated biomedical imaging datasets can cause ViT models to overfit, leading to false predictions due to random noise.

Digital breast tomosynthesis system concept addressing the needs in breast cancer screening and diagnosis.

Purpose: Digital breast tomosynthesis (DBT) has been introduced more than a decade ago. Studies have shown higher breast cancer detection rates and lower recall rates, and it has become an established imaging method in diagnostic settings. However, full-field digital mammography (FFDM) remains the most common imaging modality for screening in many countries, as it delivers high-resolution planar images of the breast.

A new subtype of papillary ductal carcinoma in situ with tall cell and reversed polarity morphology: a rare case report.

Background: According to previous studies, tall cell carcinoma with reversed polarity can be easily distinguished from ductal carcinoma in situ based on the absence of myoepithelium and a typical histologic feature. However, to the best of our knowledge, no cases of papillary ductal carcinoma exhibiting tall cell and reversed polarity features with intact myoepithelium have been reported, and thus its diagnosis and prognosis remain unclear.

Case Presentation: A 54-year-old female with a palpable lump in her right breast for 3 years.

A prediction model based on dual-layer spectral detector computed tomography for distinguishing nonluminal from luminal invasive breast cancer.

Background: The identification of the molecular subtypes of breast cancer is critical to determining appropriate treatment strategies and assessing prognosis. This study aimed to evaluate the ability of dual-layer spectral detector computed tomography (DLCT) metrics to differentiate luminal from nonluminal invasive breast cancer.

Methods: A total of 220 patients with invasive breast cancer who underwent routine DLCT examination were included in the study.

Novel study on the prediction of BI-RADS 4A positive lesions in mammography using deep learning technology and clinical factors.

Background: The classification of Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions in mammography is complicated by subjective interpretations and unclear criteria, which can lead to potential misclassifications and unnecessary biopsies. Thus, more accurate assessment methods need to be developed. This study aimed to improve the classification prediction of BI-RADS 4A positive lesions in mammography by combining deep learning (DL) technology with relevant clinical factors.

Comparing core needle biopsy and surgical excision in breast cancer diagnosis: implications for clinical practice from a retrospective cohort study.

Background: Preoperative ultrasound-guided core needle biopsy (CNB) is currently the standard procedure for managing breast illnesses. However, the differences in outcomes between CNB and surgical excision (SE) have not been thoroughly assessed. This study aimed to explore the disparities in pathological outcomes between these two procedures, using a large sample dataset.

Preoperatively predicting human epidermal growth factor receptor 2-low expression in breast cancer using neural network model based on multiparameter magnetic resonance imaging.

Background: Preoperative prediction of human epidermal growth factor receptor 2 (HER2)-low expression using magnetic resonance imaging (MRI) can enhance the selection of clinical treatment strategies and enhance patient outcomes. Herein, we investigated the value of a neural network model constructed with multiparametric MRI in diagnosing HER2-low breast cancer.

Methods: This retrospective study involved two different centers.

Comparison of Full-Field Digital Mammography with Synthesized Mammography from Tomosynthesis in a Diagnostic population: Prospective Study.

 Synthesized mammography (SM) refers to two-dimensional (2D) images derived from the digital breast tomosynthesis (DBT) data. It can reduce the radiation dose and scan duration when compared with conventional full-field digital mammography (FFDM) plus tomosynthesis.  To compare the diagnostic performance of 2D FFDM with synthetic mammograms obtained from DBT in a diagnostic population.

A holistic physics-informed neural network solution for precise destruction of breast tumors using focused ultrasound on a realistic breast model.

This study presented a novel approach for the precise ablation of breast tumors using focused ultrasound (FUS), leveraging a physics-informed neural network (PINN) integrated with a realistic breast model. FUS has shown significant promise in treating breast tumors by effectively targeting and ablating cancerous tissue. This technique employs concentrated ultrasonic waves to generate intense heat, effectively destroying cancerous tissue.

Ultrasound nanodroplets loaded with Siglec-G siRNA and FeO activate macrophages and enhance phagocytosis for immunotherapy of triple-negative breast cancer.

Background: The progression of triple-negative breast cancer is shaped by both tumor cells and the surrounding tumor microenvironment (TME). Within the TME, tumor-associated macrophages (TAMs) represent a significant cell population and have emerged as a primary target for cancer therapy. As antigen-presenting cells within the innate immune system, macrophages are pivotal in tumor immunotherapy through their phagocytic functions.

Segmentation for mammography classification utilizing deep convolutional neural network.

Background: Mammography for the diagnosis of early breast cancer (BC) relies heavily on the identification of breast masses. However, in the early stages, it might be challenging to ascertain whether a breast mass is benign or malignant. Consequently, many deep learning (DL)-based computer-aided diagnosis (CAD) approaches for BC classification have been developed.

Comparative analysis of metabolic characteristics and prognostic stratification of HER2-low and HER2-zero breast cancer using F-FDG PET/CT imaging.

Background: Recent advancements in novel anti-human epidermal growth factor receptor 2 (HER2) antibody-drug conjugates (ADCs) have highlighted the emerging HER2-low breast cancer subtype with promising therapeutic efficacy. This study aimed to comparatively analyze the metabolic characteristics and prognostic stratification of HER2-low and HER2-zero breast cancer using baseline fluorine-18 fluorodeoxyglucose (F-FDG) positron emission tomography/computed tomography (PET/CT) imaging.

Methods: Consecutive patients with newly diagnosed breast cancer who underwent F-FDG PET/CT prior to therapy in our hospital were retrospectively reviewed.

Evaluation of functional magnetic resonance APT and DKI imaging for breast cancer.

Objective: This study aimed to compare the performance of amide proton transfer-weighted imaging (APTWI) and diffusion kurtosis imaging (DKI) in differentiating benign from malignant breast lesions, evaluate molecular subtypes of breast cancer, and determine the diagnostic efficacy of the quantitative magnetic resonance imaging (qMRI) parameters in differentiating benign from malignant breast diseases.

Methods: The study included 168 women who underwent breast APTWI and DKI at Yunnan Cancer Hospital between December 2022 and July 2023. The APT signal intensity (SI), apparent kurtosis coefficient (Kapp), non-Gaussian diffusion coefficient (Dapp), and apparent diffusion coefficient (ADC) values were measured before surgery.

Women's perspectives of molecular breast imaging: a qualitative study.

Background: Mammography has poor sensitivity in dense breast tissue. Retrospective studies suggest that Molecular Breast Imaging (MBI), has superior diagnostic accuracy to mammography in women with very dense breast tissue. Women's perspectives of MBI are unknown, but are crucial to understanding the feasibility of, and routes to, adoption into practice.

Attention-based Fusion Network for Breast Cancer Segmentation and Classification Using Multi-modal Ultrasound Images.

Objective: Breast cancer is one of the most commonly occurring cancers in women. Thus, early detection and treatment of cancer lead to a better outcome for the patient. Ultrasound (US) imaging plays a crucial role in the early detection of breast cancer, providing a cost-effective, convenient, and safe diagnostic approach.

Research trends on AI in breast cancer diagnosis, and treatment over two decades.

Objective: Recently, the integration of Artificial Intelligence (AI) has significantly enhanced the diagnostic accuracy in breast cancer screening. This study aims to deliver an extensive review of the advancements in AI for breast cancer diagnosis and prognosis through a bibliometric analysis.

Methodology: Therefore, this study gathered pertinent peer-reviewed research articles from the Scopus database, spanning the years 2000 to 2024.

Diagnostic performance of MRI-guided vacuum-assisted breast biopsy (VABB): an essential but still underused technique.

Background: Magnetic resonance imaging (MRI)-guided vacuum-assisted breast biopsy (VABB) is an increasingly requested procedure, but it implies training and experience both in its execution and in determining radiological-pathological concordance and is therefore performed in dedicated breast centers. The purpose of this study is to evaluate the diagnostic performance of MRI-guided vacuum-assisted biopsy and to determine the upgrade rate after surgery or follow-up.

Methods: We retrospectively evaluated all consecutive patients with suspicious MRI findings without corresponding mammographic and ultrasonographic findings who underwent MRI-guided vacuum-assisted breast biopsy (VABB) at our Institution from November 2020 to March 2023.

Fibromatosis-Like Metaplastic Carcinoma of the Breast: A Challenging Diagnosis With Potential Pitfalls.

Fibromatosis-like metaplastic carcinoma (FLMC) is a rare subtype of metaplastic carcinoma of the breast. Diagnosing this entity poses significant challenges, particularly in core biopsies due to limited sampling and overlap with benign spindle cell lesions such as nodular fasciitis and fibromatosis. We present an example of FLMC in an asymptomatic middle-aged woman.

Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers-Based Model.

Background: Morphological and vascular characteristics of breast cancer can change during neoadjuvant chemotherapy (NAC). Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-acquired pre- and mid-treatment quantitatively capture information about tumor heterogeneity as potential earlier indicators of pathological complete response (pCR) to NAC in breast cancer.

Aims: This study aimed to develop an ensemble deep learning-based model, exploiting a Vision Transformer (ViT) architecture, which merges features automatically extracted from five segmented slices of both pre- and mid-treatment exams containing the maximum tumor area, to predict and monitor pCR to NAC.