Utilization of artificial intelligence to triage patients with delayed follow-up of probably benign breast ultrasound findings.

Purpose: This study aimed to evaluate our institution's experience in using artificial intelligence (AI) decision support (DS) as part of the clinical workflow to triage patients with Breast Imaging Reporting and Data System (BI-RADS) 3 sonographic lesions whose follow-up was delayed during the coronavirus disease 2019 (COVID-19) pandemic, against subsequent imaging and/or pathologic follow-up results.

Methods: This retrospective study included patients with a BI-RADS category 3 (i.e.

Non-enhancing asymmetries on screening contrast-enhanced mammography: Is further diagnostic workup required?

Objectives: Asymmetries on screening contrast-enhanced mammography (CEM) often lead to patient recall. However, in diagnostic settings, negative CEM has effectively classified these as normal or benign, questioning the need for further workup of non-enhancing asymmetries (NEAs).

Material And Methods: A computational search of all screening CEM examinations performed between December-2012 and June-2021 was conducted to identify cases reporting NEAs.

Extremely dense breasts: A comprehensive review of increased cancer risk and supplementary screening methods.

Women with extremely dense breasts account for approximately 10% of the screening population and face an increased lifetime risk of developing breast cancer. At the same time, the sensitivity of mammography, the first-line screening modality, is significantly reduced in this breast density group, owing to the masking effect of the abundant fibroglandular tissue. Consequently, this population has garnered increasing scientific attention due to the unique diagnostic challenge they present.

Racial Disparities in Preoperative Breast MRI Use and Surgical Margin Outcomes among Patients with Recently Diagnosed Breast Cancer.

Purpose To evaluate racial disparities in preoperative breast MRI use and surgical margin outcomes among patients with recently diagnosed breast cancer. Materials and Methods This retrospective study included patients with breast cancer who presented to a single cancer center between 2008 and 2020, underwent breast surgery, and self-identified as White or Black. Patients were divided into MRI or no-MRI cohorts based on preoperative MRI use.

Breast Lesion Classification with Multiparametric Breast MRI Using Radiomics and Machine Learning: A Comparison with Radiologists' Performance.

This multicenter retrospective study compared the performance of radiomics analysis coupled with machine learning (ML) with that of radiologists for the classification of breast tumors. A total of 93 consecutive women (mean age: 49 ± 12 years) with 104 histopathologically verified enhancing lesions (mean size: 22.8 ± 15.

Differentiation Between Benign and Metastatic Breast Lymph Nodes Using Apparent Diffusion Coefficients.

The aim of this study was to determine the range of apparent diffusion coefficient (ADC) values for benign axillary lymph nodes in contrast to malignant axillary lymph nodes, and to define the optimal ADC thresholds for three different ADC parameters (minimum, maximum, and mean ADC) in differentiating between benign and malignant lymph nodes. This retrospective study included consecutive patients who underwent breast MRI from January 2017-December 2020. Two-year follow-up breast imaging or histopathology served as the reference standard for axillary lymph node status.

Assessing PD-L1 Expression Status Using Radiomic Features from Contrast-Enhanced Breast MRI in Breast Cancer Patients: Initial Results.

The purpose of this retrospective study was to assess whether radiomics analysis coupled with machine learning (ML) based on standard-of-care dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict PD-L1 expression status in patients with triple negative breast cancer, and to compare the performance of this approach with radiologist review. Patients with biopsy-proven triple negative breast cancer who underwent pre-treatment breast MRI and whose PD-L1 status was available were included. Following 3D tumor segmentation and extraction of radiomic features, radiomic features with significant differences between PD-L1+ and PD-L1- patients were determined, and a final predictive model to predict PD-L1 status was developed using a coarse decision tree and five-fold cross-validation.

MRI Screening of Mutation Carriers: Comparison of Standard Protocol and Abbreviated Protocols With and Without T2-Weighted Images.

Increasing evidence supports the role of abbreviated MRI protocols for breast cancer detection. However, abbreviated protocols have been poorly studied in patients who are or mutation carriers. Furthermore, the need for T2-weighted sequences in abbreviated protocols remains controversial.

Multidimensional Diffusion Magnetic Resonance Imaging for Characterization of Tissue Microstructure in Breast Cancer Patients: A Prospective Pilot Study.

Diffusion-weighted imaging is a non-invasive functional imaging modality for breast tumor characterization through apparent diffusion coefficients. Yet, it has so far been unable to intuitively inform on tissue microstructure. In this IRB-approved prospective study, we applied novel multidimensional diffusion (MDD) encoding across 16 patients with suspected breast cancer to evaluate its potential for tissue characterization in the clinical setting.

Using Deep Learning to Improve Nonsystematic Viewing of Breast Cancer on MRI.

Objective: To investigate the feasibility of using deep learning to identify tumor-containing axial slices on breast MRI images.

Methods: This IRB-approved retrospective study included consecutive patients with operable invasive breast cancer undergoing pretreatment breast MRI between January 1, 2014, and December 31, 2017. Axial tumor-containing slices from the first postcontrast phase were extracted.

Diagnostic value of radiomics and machine learning with dynamic contrast-enhanced magnetic resonance imaging for patients with atypical ductal hyperplasia in predicting malignant upgrade.

Purpose: To investigate whether radiomics features extracted from magnetic resonance imaging (MRI) of patients with biopsy-proven atypical ductal hyperplasia (ADH) coupled with machine learning can differentiate high-risk lesions that will upgrade to malignancy at surgery from those that will not, and to determine if qualitatively and semi-quantitatively assessed imaging features, clinical factors, and image-guided biopsy technical factors are associated with upgrade rate.

Methods: This retrospective study included 127 patients with 139 breast lesions yielding ADH at biopsy who were assessed with multiparametric MRI prior to biopsy. Two radiologists assessed all lesions independently and with a third reader in consensus according to the BI-RADS lexicon.

[Multimodal, multiparametric and genetic breast imaging].

Clinical/methodological Issue: Multiparametric magnetic resonance imaging (MRI) aims to visualize and quantify biological, physiological and pathological processes at the cellular and molecular level and provides valuable information about key processes in cancer development and progression. "Omics" strategies (genomics, transcriptomics, proteomics, metabolomics) have many uses in oncology.

Standard Radiological Methods: Multiparametric MRI of the breast currently includes T2-weighted, diffusion-weighted and dynamic contrast-enhanced MRI (DCE-MRI) METHODOLOGICAL INNOVATIONS: Additional parameters such as proton magetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), blood oxygen level-dependent (BOLD), hyperpolarized (HP) MRI or lipid MRS are currently being developed and are being evaluated in breast cancer diagnostics.

Pharmacokinetic Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging at 7T for Breast Cancer Diagnosis and Characterization.

The purpose of this study was to investigate whether ultra-high-field dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast at 7T using quantitative pharmacokinetic (PK) analysis can differentiate between benign and malignant breast tumors for improved breast cancer diagnosis and to predict molecular subtypes, histologic grade, and proliferation rate in breast cancer. In this prospective study, 37 patients with 43 lesions suspicious on mammography or ultrasound underwent bilateral DCE-MRI of the breast at 7T. PK parameters (K, k, V) were evaluated with two region of interest (ROI) approaches (2D whole-tumor ROI or 2D 10 mm standardized ROI) manually drawn by two readers (senior reader, R1, and R2) independently.

MRI background parenchymal enhancement, fibroglandular tissue, and mammographic breast density in patients with invasive lobular breast cancer on adjuvant endocrine hormonal treatment: associations with survival.

Background: To investigate if baseline and/or changes in contralateral background parenchymal enhancement (BPE) and fibroglandular tissue (FGT) measured on magnetic resonance imaging (MRI) and mammographic breast density (MD) can be used as imaging biomarkers for overall and recurrence-free survival in patients with invasive lobular carcinomas (ILCs) undergoing adjuvant endocrine treatment.

Methods: Women who fulfilled the following inclusion criteria were included in this retrospective HIPAA-compliant IRB-approved study: unilateral ILC, pre-treatment breast MRI and/or mammography from 2000 to 2010, adjuvant endocrine treatment, follow-up MRI, and/or mammography 1-2 years after treatment onset. BPE, FGT, and mammographic MD of the contralateral breast were independently graded by four dedicated breast radiologists according to BI-RADS.

Multimodality imaging of intrauterine devices with an emphasis on the emerging role of 3-dimensional ultrasound.

The intrauterine contraceptive device (IUD) is one of the most widely used reversible contraception methods throughout the world. With advancing technology, it has rapidly gained acceptance through its increased effectiveness and practicality compared with more invasive means such as laparoscopic tubal ligation. This pictorial essay will present the IUDs most commonly used today.

Characterization of subsets of CD4+ memory T cells reveals early branched pathways of T cell differentiation in humans.

The pathways for differentiation of human CD4(+) T cells into functionally distinct subsets of memory cells in vivo are unknown. The identification of these subsets and pathways has clear implications for the design of vaccines and immune-targeted therapies. Here, we show that populations of apparently naive CD4(+) T cells express the chemokine receptors CXCR3 or CCR4 and demonstrate patterns of gene expression and functional responses characteristic of memory cells.