Background: HER2-low expression has gained clinical relevance in breast cancer (BC) due to the availability of anti-HER2 antibody-drug conjugates for patients with HER2-low metastatic BC. The well-reported instability of HER2-low status during disease evolution highlights the need to identify patients with HER2-0 primary BC who may develop a HER2-low phenotype at relapse. In response to the urgency of maximizing treatment access, we utilized artificial intelligence to predict this occurrence.
View Article and Find Full Text PDFBackground: Young women with breast cancer (BC) have an increased chance of carrying germline BRCA pathogenic variants (PVs). Limited data exist on the prognostic impact of tumor histology (i.e.
View Article and Find Full Text PDFRadiomics, analysing quantitative features from medical imaging, has rapidly become an emerging field in translational oncology. Radiomics has been investigated in several neoplastic malignancies as it might allow for a non-invasive tumour characterization and for the identification of predictive and prognostic biomarkers. Over the last few years, evidence has been accumulating regarding potential clinical applications of machine learning in many crucial moments of cancer patients' history.
View Article and Find Full Text PDFBackground: The cutoff of <1% positive cells to define estrogen receptor (ER) negativity by immunohistochemistry (IHC) in breast cancer (BC) is debated. We explored the tumor immune microenvironment and gene-expression profile of patients with early-stage HER2-negative ER-low (ER 1%-9%) BC, comparing them to ER-negative (ER <1%) and ER-intermediate (ER 10%-50%) tumors.
Methods: Among 921 patients with early-stage I-III, ER ≤50%, HER2-negative BCs, tumors were classified as ER-negative (n = 712), ER-low (n = 128), or ER-intermediate (n = 81).