AIMC Topic: Triple Negative Breast Neoplasms
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Contrast-enhanced mammography-based interpretable machine learning model for the prediction of the molecular subtype breast cancers.
BMC medical imaging
Jul 1, 2025
OBJECTIVE: This study aims to establish a machine learning prediction model to explore the correlation between contrast-enhanced mammography (CEM) imaging features and molecular subtypes of mass-type breast cancer.
Machine learning identifies SRD5A3 as a propionate-related prognostic biomarker in triple-negative breast cancer.
Scientific reports
May 29, 2025
The increased risk of recurrence and metastasis are obstacles to treating TNBC. Propionate-related genes play an important role in tumor development and immune cell infiltration. The study was to identify the association between propionate-related ge...
Machine learning and single-cell analysis uncover distinctive characteristics of CD300LG within the TNBC immune microenvironment: experimental validation.
Clinical and experimental medicine
May 17, 2025
Investigating the essential function of CD300LG within the tumor microenvironment in triple-negative breast cancer (TNBC). Transcriptomic and single-cell data from TNBC were systematically collected and integrated. Four machine learning algorithms we...
Construction of a stromal cell-related prognostic signature based on a 101-combination machine learning framework for predicting prognosis and immunotherapy response in triple-negative breast cancer.
Frontiers in immunology
Jan 1, 2025
BACKGROUND: Triple-negative breast cancer (TNBC) is a highly aggressive subtype with limited therapeutic targets and poor immunotherapy outcomes. The tumor microenvironment (TME) plays a key role in cancer progression. Advances in single-cell transcr...
Artificial Intelligence Decision Support for Triple-Negative Breast Cancers on Ultrasound.
Journal of breast imaging
Jan 19, 2024
OBJECTIVE: To assess performance of an artificial intelligence (AI) decision support software in assessing and recommending biopsy of triple-negative breast cancers (TNBCs) on US.
Predicting pathological complete response to neoadjuvant systemic therapy for triple-negative breast cancers using deep learning on multiparametric MRIs.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2023
We trained and validated a deep learning model that can predict the treatment response to neoadjuvant systemic therapy (NAST) for patients with triple negative breast cancer (TNBC). Dynamic contrast enhanced (DCE) MRI and diffusion-weighted imaging (...
Machine learning analysis of breast ultrasound to classify triple negative and HER2+ breast cancer subtypes.
Breast disease
Jan 1, 2023
OBJECTIVES: Early diagnosis of triple-negative (TN) and human epidermal growth factor receptor 2 positive (HER2+) breast cancer is important due to its increased risk of micrometastatic spread necessitating early treatment and for guiding targeted th...
A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images.
Bioinformatics (Oxford, England)
Sep 30, 2022
MOTIVATION: Predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) patients accurately is direly needed for clinical decision making. pCR is also regarded as a strong predictor of ove...
Classification and gene selection of triple-negative breast cancer subtype embedding gene connectivity matrix in deep neural network.
Briefings in bioinformatics
Sep 2, 2021
Triple-negative breast cancer (TNBC) has been a challenging breast cancer subtype for oncological therapy. Normally, it can be classified into different molecular subtypes. Accurate and stable classification of the six subtypes is essential for perso...
A Reliable Multi-classifier Multi-objective Model for Predicting Recurrence in Triple Negative Breast Cancer.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2019
Recurrence is a significant prognostic factor in patients with triple negative breast cancer, and the ability to accurately predict it is essential for treatment optimization. Machine learning is a preferred strategy for recurrence prediction. Most c...
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