Breast Cancer

Different BI-RADS breast cancer diagnosis using MobileNetV1 and vision transformer based on explainable artificial intelligence (XAI).

Breast cancer (BC) remains one of the leading causes of death among women in the world, depending on the requirement for precise, effective, and interpretable computer-aided diagnosis systems (CADs). In this work, a hybrid deep learning (DL) framework is presented for multi-class BI-RADS BC classification using mammographic images. This framework fuses MobileNetV1, a lightweight convolutional neur...

Deep-Learning Tool ScVital Enables Species-Agnostic Integration of Cancer Cell States.

UNLABELLED: Genetically engineered mouse models (GEMM) of cancer are useful for exploring the development and biological composition of human tumors. Single-cell RNA sequencing (scRNA-seq) provides a transcriptomic snapshot of cancer to explore the heterogeneity of cell states in an immunocompetent context. However, cross-species comparison often suffers from biological batch effect, and inherent ...

Emerging Roles of Multiparametric Biodosimetry and Artificial Intelligence in Advancing Radiation Dose Assessment for Triage.

Biodosimetry plays a crucial role in radiation emergency preparedness and response by enabling efficient allocation of medical resources through prior...

A Flexible Hybrid Quantum-classical Training Framework of Organ-at-Risk and Tumor Segmentation Models for Radiation Therapy Planning.

Deep learning-based Organ-at-Risk (OAR) and tumor segmentation is vital for radiation therapy planning but often suffers from over-parameterization, r...

Essential Updates 2024-2025: Surgical Strategy for Esophageal Cancer Toward a New Paradigm in the Era of Immunotherapy and Personalization.

Esophageal cancer surgery is evolving from technical standardization to a paradigm of personalized, strategy-oriented care. Robotic-assisted technique...

Deep-learning based adaptive fusion of CC and MLO views for improved mammographic cancer diagnosis.

Breast cancer remains the most prevalent malignancy among women worldwide. The timely detection of this cancer type is critical for improving survival...

Interpretable prognostic modeling of glioblastoma using cross-cohort transcriptome integration and machine learning approaches.

Glioblastoma (GBM) is an aggressive brain tumor with highly variable patient outcomes due to pronounced molecular heterogeneity. Prognosis remains dis...

Late distant recurrence prediction model in premenopausal women with ER-positive/HER 2-negative breast cancer: A multicenter retrospective study.

BACKGROUND: Late distant recurrence (DR) remains a significant challenge in estrogen receptor (ER)-positive/Human Epidermal Growth Factor Receptor 2 (...

Interpreting cancer genetics through a two-step "evolutionary cascade hypothesis": bridging neutral and selective perspectives.

BACKGROUND: DNA mutations are the fundamental engines of cancer, driving its initiation and progression. The forces that fuel malignancy are also the ...

Early detection of female-specific cancers using longitudinal healthcare records with a multichannel convolutional neural network.

OBJECTIVES: Female-specific cancers, including breast, ovarian, cervical and uterine malignancies, lack comprehensive early detection approaches, part...

Predicting cardiovascular events from routine mammograms using machine learning.

BACKGROUND: Cardiovascular risk is underassessed in women. Many women undergo screening mammography in midlife when the risk of cardiovascular disease...

Reinforcement learning-guided segment anything model for MRI prostate and dominant intraprostatic lesions auto-segmentation.

Objective. Accurate segmentation of the prostate and dominant intraprostatic lesions (DILs) on magnetic resonance imaging (MRI) is important for prost...

Toward genomic personalization of breast cancer radiotherapy: foundations, challenges, and a roadmap for clinical integration.

Personalizing radiotherapy dose in breast cancer remains a major unmet need, as current treatment paradigms rely on uniform prescriptions that overloo...

Commercially Available Artificial Intelligence Score on Preoperative Mammography for Prediction of Future Breast Cancer After DCIS Treatment.

Background: Mammographic artificial intelligence (AI) systems have been explored for future breast cancer risk prediction. Objective: To investigate a...

Development and Validation of a Computational Histology Artificial Intelligence-Powered Predictive Biomarker for Selection of Chemotherapy in Advanced Pancreatic Cancer.

PURPOSE: Predictive biomarkers to guide selection of first-line chemotherapy for advanced pancreatic ductal adenocarcinoma (PDAC) are an unmet clinica...

Selective multimodal deep learning for reliable breast cancer subtype classification from histopathology and genomic data.

Accurate classification of breast cancer subtypes is critical for personalized treatment planning and prognostic assessment. While histopathology ...

A systems approach identifies MERTK as a therapeutic vulnerability in ZFTA-RELA-driven ependymomas.

Ependymomas (EPN) are rare central nervous system tumors that account for approximately 10% of intracranial tumors in children and 4% in adults. Despi...

Development of a Real-time Radiation Detection System that Uses IoT to Provide Real-time Monitoring in Custom-built Website.

Effective radiation monitoring is crucial for ensuring public security and safety, particularly in the event of nuclear (e.g., nuclear accident, fallo...

Clinical feasibility test of 60 kVp double-low-dose coronary CT angiography with a deep learning reconstruction algorithm.

OBJECTIVES: To test the feasibility of 60 kVp double-low-dose coronary CT angiography (CCTA) with a deep learning reconstruction (DLR) algorithm. MATE...

Development and validation of a prognosis model for low-grade gliomas based on metabolic gene risk scoring and immune microenvironment interaction.

PURPOSE: Low-grade gliomas(LGGs) show significant clinical and molecular heterogeneity, complicating progression prediction with conventional indicato...