Breast Cancer

Machine learning-based analysis of average glandular dose in mammography: The role of breast density.

INTRODUCTION: Average glandular dose (Dg) is the primary metric for assessing radiation risk in screening mammography. Although Dg analysis is commonly interpreted according to compressed breast thickness (CBT), breast density (BD) may significantly influence Dg. This study evaluated the influence of BD on Dg variability using the TG282-based dosimetry model and a machine learning approach. METHOD...

A deep learning-derived risk score model from digital pathology to forecast nivolumab outcomes in gastric carcinoma.

BACKGROUND: The PD-L1 combined positive score (CPS) is a biomarker predicting responses in gastric cancer (GC) immunotherapy. OBJECTIVES: We aimed to develop a deep learning-based model to predict responses to nivolumab in GC using PD-L1 28-8 immunohistochemistry. METHODS: A cell-detection network was trained on 1927 patches from 88 whole-slide images to generate a computational positive cell rati...

Integrative systems biology identifies PSMB2 as a core oxidative stress-associated target in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) represents one of the most aggressive and therapeutically challenging subtypes of breast cancer, characterized by...

Two-stage deep learning framework for magnetic resonance imaging-based detection of temporomandibular joint osteoarthritis.

OBJECTIVES: Cone-beam computed tomography (CBCT) is the reference standard for detecting osseous changes in temporomandibular joint osteoarthritis (TM...

Intelligent Organ Recognition Technology Combined With an Artificial Intelligence Iterative Reconstruction Algorithm in Contrast-enhanced CT of the Chest, Abdomen, and Pelvis.

RATIONALE AND OBJECTIVES: To evaluate the application value of intelligent organ recognition technology combined with the artificial intelligence iter...

Computational methods, clinical evidence, and translational readiness for artificial intelligence for HER2 assessment using silver in situ hybridization (SISH).

Accurate determination of human epidermal growth factor receptor 2 (HER2) gene amplification is a cornerstone of precision oncology in breast cancer a...

[Paeoniflorin alleviates cancer-related fatigue during chemotherapy for breast cancer by targeting EZH2/CCNE1 to regulate cell cycle and inflammatory microenvironment].

OBJECTIVES: To explore the molecular mechanisms and key pharmacodynamic basis of Baishao for alleviating cancer-related fatigue (CRF) during chemother...

[Reflections on surgical management strategies for locally advanced gastric cancer after neoadjuvant immunotherapy].

Neoadjuvant immunotherapy is reshaping the treatment landscape of locally advanced gastric cancer (GC). The central clinical challenge has expanded fr...

Artificial intelligence-driven prediction of neoadjuvant chemotherapy response in adult patients with gastric cancer: a systematic review and meta-analysis.

OBJECTIVES: A substantial proportion of adults with locally advanced gastric cancer derive limited benefit from neoadjuvant chemotherapy (NAC), with a...

Radiomics using generative adversarial network enhanced non-contrast computed tomography for gastric cancer diagnosis.

Objective.Non-contrast-enhanced computed tomography (NCCT) images have limited tissue resolution for gastric cancer diagnosis, while contrast-enhanced...

Global trends and emerging frontiers in robot-assisted vascular intervention: a comprehensive bibliometric analysis.

Robot-assisted vascular intervention may reduce occupational radiation exposure, improve procedural stability, and expand access to specialized endova...

Nanotechnology-based immunotherapy: integrating Artificial Intelligence (AI) with current strategies in combating brain cancer disease.

Brain cancer is one of the most challenging malignancies and a major contributor to worldwide morbidity and mortality. Glioblastoma, the most aggressi...

Prediction of Side Effects from Breast Radiation Therapy - Integration of Clinical and Genomic Data.

OBJECTIVES: To compare the ability of different machine learning models to predict the risk of side effects in patients with breast cancer undergoing ...

Multimodal Artificial Intelligence System for Risk-Adapted Cancer Survivorship Surveillance: A Multicenter Target Trial Emulation.

BACKGROUND: The growing population of cancer survivors faces immense monitoring burdens due to rigid follow-up guidelines, such as the intensive surve...

Deep learning applications in osteosarcoma MRI: A systematic review of recent advances in AI-based osteosarcoma diagnosis.

Primary malignant bone tumours of the skeleton have a great diversity in their biological behaviour, and the most common in adolescence is osteosarcom...

Integrating Cardiovascular Screening in Lung Cancer Screening.

Cardiovascular risk assessment is a natural extension of lung cancer screening (LCS) because individuals eligible for low-dose computed tomography oft...

Next generation graphene based MXene metamaterial multicylinder resonator solar absorber optimized with machine learning for conceptual solar dryer mining applications.

In the field of industry and mining sectors, energy demand is rapidly increasing, and it is very necessary to develop a durable, scalable, and broadba...

Transcriptomic Profiling of Mouse Mammary Tumors Enables Prognostic and Predictive Biomarker Discovery for Human Breast Cancer.

The development and validation of prognostic and predictive biomarkers in breast cancer is limited by the availability of well-annotated datasets link...

From optimization of radiation dose distributions to optimal cancer treatments.

The history of optimization in radiation oncology is closely intertwined with the history of Physics in Medicine & Biology (PMB). Over the past four d...

Modeling microbiome modulation of tumor metabolic networks to predict synergistic therapies.

Differences in microbiome composition profoundly influence drug response, yet methods to model the metabolic impact of microbes on host cells and ther...