CD4FOXP3Exon2 regulatory T cell frequency predicts breast cancer prognosis and survival.

CD4FOXP3 regulatory T cells (T) suppress immune responses to tumors, and their accumulation in the tumor microenvironment (TME) correlates with poor clinical outcome in several cancers, including breast cancer (BC). However, the properties of intratumoral T remain largely unknown. Here, we found that a functionally distinct subpopulation of T, expressing the FOXP3 Exon2 splicing variants, is prominent in patients with hormone receptor-positive BC with poor prognosis.

New techniques versus standard mapping for sentinel lymph node biopsy in breast cancer: a systematic review and meta-analysis.

New tracers for sentinel lymph node biopsy (SLNB), as indocyanine green (ICG), superparamagnetic iron oxide (SPIO) and micro bubbles, have been recently introduced in clinical practice showing promising but variable results. We reviewed the available evidence comparing these new techniques with the standard tracers to evaluate their safety. To identify all available studies, a systematic search was performed in all electronic databases.

Generation and Characterization of a Tumor Stromal Microenvironment and Analysis of Its Interplay with Breast Cancer Cells: An In Vitro Model to Study Breast Cancer-Associated Fibroblast Inactivation.

Breast cancer-associated fibroblasts (BCAFs), the most abundant non-cancer stromal cells of the breast tumor microenvironment (TME), dramatically sustain breast cancer (BC) progression by interacting with BC cells. BCAFs, as well as myofibroblasts, display an up regulation of activation and inflammation markers represented by α-smooth muscle actin (α-SMA) and cyclooxygenase 2 (COX-2). BCAF aggregates have been identified in the peripheral blood of metastatic BC patients.

Clinical value of radiomics and machine learning in breast ultrasound: a multicenter study for differential diagnosis of benign and malignant lesions.

Objectives: We aimed to assess the performance of radiomics and machine learning (ML) for classification of non-cystic benign and malignant breast lesions on ultrasound images, compare ML's accuracy with that of a breast radiologist, and verify if the radiologist's performance is improved by using ML.

Methods: Our retrospective study included patients from two institutions. A total of 135 lesions from Institution 1 were used to train and test the ML model with cross-validation.