AIMC Topic: Breast Neoplasms
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Multiple instance learning-based prediction of programmed death-ligand 1 (PD-L1) expression from hematoxylin and eosin (H&E)-stained histopathological images in breast cancer.
PeerJ
Apr 15, 2025
Programmed death-ligand 1 (PD-L1) is an important biomarker increasingly used as a predictive marker in breast cancer immunotherapy. Immunohistochemical quantification remains the standard method for assessment. However, it presents challenges relate...
Sonopermeation combined with stroma normalization enables complete cure using nano-immunotherapy in murine breast tumors.
Journal of controlled release : official journal of the Controlled Release Society
Apr 14, 2025
Nano-immunotherapy shows great promise in improving patient outcomes, as seen in advanced triple-negative breast cancer, but it does not cure the disease, with median survival under two years. Therefore, understanding resistance mechanisms and develo...
Development and validation of a nomogram model of lung metastasis in breast cancer based on machine learning algorithm and cytokines.
BMC cancer
Apr 14, 2025
BACKGROUND: The relationship between cytokines and lung metastasis (LM) in breast cancer (BC) remains unclear and current clinical methods for identifying breast cancer lung metastasis (BCLM) lack precision, thus underscoring the need for an accurate...
Integrating Machine Learning and Bulk and Single-Cell RNA Sequencing to Decipher Diverse Cell Death Patterns for Predicting the Prognosis of Neoadjuvant Chemotherapy in Breast Cancer.
International journal of molecular sciences
Apr 13, 2025
Breast cancer (BRCA) continues to pose a serious risk to women's health worldwide. Neoadjuvant chemotherapy (NAC) is a critical treatment strategy. Nevertheless, the heterogeneity in treatment outcomes necessitates the identification of reliable biom...
Identification of M1 macrophage infiltration-related genes for immunotherapy in Her2-positive breast cancer based on bioinformatics analysis and machine learning.
Scientific reports
Apr 11, 2025
Over the past several decades, there has been a significant increase in the number of breast cancer patients. Among the four subtypes of breast cancer, Her2-positive breast cancer is one of the most aggressive breast cancers. In this study, we screen...
MISTIC: a novel approach for metastasis classification in Italian electronic health records using transformers.
BMC medical informatics and decision making
Apr 10, 2025
BACKGROUND: Analysis of Electronic Health Records (EHRs) is crucial in real-world evidence (RWE), especially in oncology, as it provides valuable insights into the complex nature of the disease. The implementation of advanced techniques for automated...
Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification.
Scientific reports
Apr 9, 2025
Breast cancer detection remains one of the most challenging problems in medical imaging. We propose a novel hybrid model that integrates Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LSTM) networks, and EfficientNet-B...
One-class support vector machines for detecting population drift in deployed machine learning medical diagnostics.
Scientific reports
Apr 9, 2025
Machine learning (ML) models are increasingly being applied to diagnose and predict disease, but face technical challenges such as population drift, where the training and real-world deployed data distributions differ. This phenomenon can degrade mod...
A Multitask CNN for Near-Infrared Probe: Enhanced Real-Time Breast Cancer Imaging.
Sensors (Basel, Switzerland)
Apr 8, 2025
The early detection of breast cancer, particularly in dense breast tissues, faces significant challenges with traditional imaging techniques such as mammography. This study utilizes a Near-infrared Scan (NIRscan) probe and an advanced convolutional n...
CNRein: an evolution-aware deep reinforcement learning algorithm for single-cell DNA copy number calling.
Genome biology
Apr 7, 2025
Low-pass single-cell DNA sequencing technologies and algorithmic advancements have enabled haplotype-specific copy number calling on thousands of cells within tumors. However, measurement uncertainty may result in spurious CNAs inconsistent with real...
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