AIMC Topic: Breast Neoplasms
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An explainable predictive machine learning model for axillary lymph node metastasis in breast cancer based on multimodal data: A retrospective single-center study.
Journal of translational medicine
Aug 11, 2025
OBJECTIVE: To develop explainable machine learning models that integrate multimodal imaging and pathological biomarkers to predict axillary lymph node metastasis (ALNM) in breast cancer patients and assess their clinical utility.
18F-FDG PET/CT-based deep radiomic models for enhancing chemotherapy response prediction in breast cancer.
Medical oncology (Northwood, London, England)
Aug 11, 2025
Enhancing the accuracy of tumor response predictions enables the development of tailored therapeutic strategies for patients with breast cancer. In this study, we developed deep radiomic models to enhance the prediction of chemotherapy response after...
AI microscope facilitates accurate interpretation of HER2 immunohistochemical scores 0 and 1+ in invasive breast cancer.
Scientific reports
Aug 11, 2025
Accurate interpretation of human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) scores 0 and 1+ is crucial for treating HER2-low breast cancer patients with antibody-drug conjugates. To improve diagnostic precision, we developed...
Breast cancer is detectable from peripheral blood using machine learning over T cell receptor repertoires.
NPJ systems biology and applications
Aug 8, 2025
The immune system's defense abilities rely on the diversity of T and B lymphocytes. T Cell Receptors (TCRs) are generated through V(D)J recombination, where distinct genetic elements combine and undergo modifications, creating extensive variability. ...
BCDCNN: breast cancer deep convolutional neural network for breast cancer detection using MRI images.
Scientific reports
Aug 8, 2025
Breast cancer (BC) is a kind of cancer that is created from the cells in breast tissue. This is a primary cancer that occurs in women. Earlier identification of BC is significant in the treatment process. To lessen unwanted biopsies, Magnetic Resonan...
Machine learning and SHAP value interpretation for predicting the response to neoadjuvant chemotherapy and long-term clinical outcomes in Chinese female breast cancer.
Annals of medicine
Aug 3, 2025
BACKGROUND: Most models of neoadjuvant chemotherapy (NACT) for breast cancer (BC) suffer from insufficient data and lack interpretability. Additionally, there is a notable absence of reports from China in this field. This study is also the first to i...
A lipid metabolism-related gene signature predicts prognosis after tamoxifen treatment in ER + breast cancer and reflects tumor microenvironment heterogeneity through single-cell analysis.
BMC medical genomics
Jul 31, 2025
BACKGROUND: Although the clinical outcome of ER + breast cancer patients receiving tamoxifen after surgery is favorable, a proportion of patients experience recurrence or death due to disease progression.
Refined prognostication of pathological complete response in breast cancer using radiomic features and optimized InceptionV3 with DCE-MRI.
Scientific reports
Jul 30, 2025
BACKGROUND: Neoadjuvant therapy plays a pivotal role in breast cancer treatment, particularly for patients aiming to conserve their breast by reducing tumor size pre-surgery. The ultimate goal of this treatment is achieving a pathologic complete resp...
Ultrasound derived deep learning features for predicting axillary lymph node metastasis in breast cancer using graph convolutional networks in a multicenter study.
Scientific reports
Jul 30, 2025
The purpose of this study was to create and validate an ultrasound-based graph convolutional network (US-based GCN) model for the prediction of axillary lymph node metastasis (ALNM) in patients with breast cancer. A total of 820 eligible patients wit...
SpaSEG: unsupervised deep learning for multi-task analysis of spatially resolved transcriptomics.
Genome biology
Jul 29, 2025
Spatially resolved transcriptomics (SRT) for characterizing spatial cellular heterogeneities in tissue environments requires systematic analytical approaches to elucidate gene expression variations within their physiological context. Here, we introdu...
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