AIMC Topic: Triple Negative Breast Neoplasms
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Artificial intelligence-driven discovery of YH395A: A novel TGFβR1 inhibitor with potent anti-tumor activity against triple-negative breast cancer.
Cell communication and signaling : CCS
Jul 8, 2025
Characterized by high malignancy and limited treatment efficacy, triple-negative breast cancer (TNBC) remains a clinically challenging subtype within breast cancer classifications, marked by rapid progression and high mortality. Abnormal activation o...
Cohesive data analysis for the identification of prognostic hub genes and significant pathways associated with HER2 + and TN breast cancer types.
Scientific reports
Jul 2, 2025
Breast cancer is the most prevalent and lethal form of cancer being the utmost common medical concern of women. Breast cancer etiology implicates numerous cellular protein receptors such as estrogen receptors (ER), progesterone receptors (PR), and hu...
Prioritizing perturbation-responsive gene patterns using interpretable deep learning.
Nature communications
Jul 2, 2025
Spatially resolved transcriptomics enables mapping of multiplexed gene expression within tissue contexts. While existing methods prioritize spatially variable genes within a single slice, few address identifying genes with differential spatial expres...
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.
A genotype-to-drug diffusion model for generation of tailored anti-cancer small molecules.
Nature communications
Jul 1, 2025
Despite advances in precision oncology, developing effective cancer therapeutics remains a significant challenge due to tumor heterogeneity and the limited availability of well-defined drug targets. Recent progress in generative artificial intelligen...
Prediction of axillary lymph node metastasis in triple negative breast cancer using MRI radiomics and clinical features.
Scientific reports
Jul 1, 2025
To develop and validate a machine learning-based prediction model to predict axillary lymph node (ALN) metastasis in triple negative breast cancer (TNBC) patients using magnetic resonance imaging (MRI) and clinical characteristics. This retrospective...
PFHxA and PFHxS promote breast cancer progression in 3D culture: MEX3C-associated immune infiltration revealed by bioinformatics and machine learning.
Journal of hazardous materials
May 2, 2025
Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with widespread use and bioaccumulative potential. Short-chain PFAS such as perfluorohexanoic acid (PFHxA) and perfluorohexane sulfonate (PFHxS) have been introduced...
Image normalization techniques and their effect on the robustness and predictive power of breast MRI radiomics.
European journal of radiology
Mar 30, 2025
BACKGROUND AND PURPOSE: Radiomics analysis has emerged as a promising approach to aid in cancer diagnosis and treatment. However, radiomics research currently lacks standardization, and radiomics features can be highly dependent on acquisition and pr...
Harnessing Artificial Intelligence for Precision Diagnosis and Treatment of Triple Negative Breast Cancer.
Clinical breast cancer
Mar 8, 2025
Triple-Negative Breast Cancer (TNBC) is a highly aggressive subtype of breast cancer (BC) characterized by the absence of estrogen, progesterone, and HER2 receptors, resulting in limited therapeutic options. This article critically examines the role ...
Integrating ultrasound radiomics and clinicopathological features for machine learning-based survival prediction in patients with nonmetastatic triple-negative breast cancer.
BMC cancer
Feb 18, 2025
OBJECTIVE: This study aimed to evaluate the predictive value of implementing machine learning models based on ultrasound radiomics and clinicopathological features in the survival analysis of triple-negative breast cancer (TNBC) patients.
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