Cardiovascular

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 overlook interpatient variability in intrinsic radiosensitivity. Over the past decade, transcriptome-based biomarkers such as the Radiosensitivity Index (RSI) and its radiobiological extension, the Genomic-Adjusted Radiation Dose (GARD), have emerged as pro...

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 associations of scores from a commercial AI system for mammographic breast cancer detection and diagnosis with development of second breast cancers after DCIS treatment and to compare AI predictive performance with existing clinical risk models. Metho...

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...

A machine learning model for accurate prediction of CMV end-organ disease.

BACKGROUND: Cytomegalovirus (CMV) End-Organ Disease (EOD) remains a significant complication in immunocompromised individuals, particularly transplant...

Enhancing therapeutic outcomes with artificial intelligence for HR-positive, HER2-negative metastatic breast cancer.

Cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) are an integral first-line treatment for hormone receptor-positive metastatic breast cancer, though r...

Deep Learning Reconstruction Combined with Contrast-Enhancement Boost Technique in "Quadruple-low" CCTA Protocol: Evaluation of Image Quality and Diagnostic Accuracy.

RATIONALE AND OBJECTIVES: To evaluate the impact of a deep learning reconstruction (DLR) algorithm combined with contrast-enhancement boost (CE-boost)...

NIAID Biodosimetry Strategic Plan.

In the face of emerging threats from natural disasters, nuclear accidents, and potential malicious use of radiation, the National Institute of Allergy...

Spectral CT imaging in colorectal cancer: current applications, limitations, and future perspectives.

Colorectal cancer (CRC) is the third most common malignancy worldwide, and early detection is vital to prevent metastasis and postoperative recurrence...

Deep learning for synthetic PET imaging: a systematic mapping review of techniques, metrics, and clinical relevance.

BACKGROUND: Synthetic positron emission tomography (PET) imaging, enabled by deep learning, represents a promising approach to minimize radiation expo...

A Multimodal Fusion Model of Radiomics and Deep Learning Integrating the Tumor Microenvironment Accurately Predicts Pathological Complete Response in Breast Cancer.

BACKGROUND: Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) is a critical prognostic marker in breast cancer, yet its predictio...

Halide perovskite scintillators for X-ray detection: from structure to engineering.

Halide perovskites (HPs) and their derivatives are emerging as a prominent class of materials for ionizing radiation detection. A unique combination o...

STAR (stroma-tumor AI risk) assessment: association of AI-derived tumor-stroma proportion with patient survival provides added prognostic value beyond KELIM in epithelial ovarian cancer.

BACKGROUND: There remains a critical need for prognostic biomarkers of treatment response in epithelial ovarian cancer (EOC). The KELIM score, derived...

AI software as a third reader in breast cancer screening-a prospective diagnostic observational study.

OBJECTIVE: Despite advances in mammography screening, some cancers remain undetected, prompting the evaluation of artificial intelligence (AI) as an i...

Evaluation of deliverable dose-mimicking automated volumetric arc radiation therapy planning for stage III non-small cell lung cancer patients: comparison with a commercial DVH-predicted automated planning system.

This study aimed to evaluate the clinical validity of a dose-mimicking automated planning for volumetric-modulated arc therapy (VMAT) in patients with...

Validation of a Deep-Learning Coregistration Framework for Long-Axial-Field-of-View PET/CT Using Low-Radiation-Exposure Protocols Across Various Tracers.

The advent of long-axial-field-of-view (LAFOV) PET/CT systems has significantly improved whole-body imaging by providing higher sensitivity and extend...