AIMC Topic: Breast Neoplasms

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Translational and real-world evidence of trastuzumab biosimilar CT-P6 plus pertuzumab in neoadjuvant HER2-positive early breast cancer.

Breast cancer research and treatment
BACKGROUND: Data on neoadjuvant treatment with trastuzumab biosimilars, particularly CT-P6, in combination with pertuzumab, are limited. This study evaluates the efficacy, tolerability, and immunogenicity of CT-P6 plus pertuzumab and chemotherapy, in...

Establishment of an amino acid metabolism related signature for prognostic and therapeutic sensitivity prediction in breast cancer by machine learning.

PloS one
Amino acid metabolism plays a critical role in tumor growth and immune regulation, yet its comprehensive function in breast cancer remains underexplored. We developed an amino acid metabolism-related gene signature (AAMRGS) to predict prognosis and t...

Morphological and textural descriptors analysis of digital mammograms with radiological findings to support breast cancer detection using artificial neural networks.

Biomedical physics & engineering express
. To classify digital mammograms based on radiological findings using morphology and texture descriptors with artificial neural networks (ANN) for breast cancer detection.The mammography dataset from High Specialty Regional Hospital of Oaxaca (HRAEO)...

Artificial Intelligence in Breast Cancer Diagnosis and Management.

British journal of hospital medicine (London, England : 2005)
Artificial intelligence (AI) holds significant promise in the fields of diagnostics and therapeutics, particularly in cancer management. AI has been extensively applied in various aspects of breast cancer care. Numerous studies and reviews have been ...

Deciphering the biological underpinnings behind prognostic MRI-based imaging signatures in breast cancer: a systematic review.

Journal of translational medicine
OBJECTIVE: To explore the biological foundations of MRI-based prognostic imaging signatures (including radiomics and deep learning signatures) in breast cancer, and to assess the methodological quality of existing studies.

The role of gut microbiota in breast cancer: biomarker identification and therapeutic applications.

Antonie van Leeuwenhoek
Recent studies have established the gut microbiome as a crucial player in breast cancer diagnosis, progression, and treatment. Distinct microbial patterns have shown promise as non-invasive diagnostic and prognostic biomarkers, supporting patient str...

Development and validation of a machine learning model to predict moderate-to-severe cancer-related fatigue in breast cancer.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
PURPOSE: This study aimed to establish and validate a machine learning model for predicting moderate-to-severe cancer-related fatigue (CRF) 2 years after completion of anti-tumor therapy in breast cancer patients.

[The Development of Algorithm of Intellectual System of Supporting Decision-Making in Mammographic Diagnostics of Breast Cancer Based on Convolutional Neuronic Network].

Problemy sotsial'noi gigieny, zdravookhraneniia i istorii meditsiny
The article considers issues of training models of convolutional neuronic network (CNN) for automated identification of point functions of visualization to discern mammography pictures belonging to negative, false benign and malignant cases, targetin...

An Interpretable Hybrid AI Model for Breast Fine Needle Aspiration Cytology Image Classification.

Journal of medical systems
While Fine needle aspiration cytology (FNAC) and mammography are both used to diagnose breast lesions, FNAC is generally more accurate than mammograms for predicting breast cancer. It is also gaining popularity as an early detection tool due to its r...