Breast Cancer

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 recipients and patients undergoing chemotherapy. Accurate prediction of CMV EOD is essential for timely intervention but remains challenging using traditional methods. OBJECTIVE: This study aimed to evaluate the diagnostic performance of machine lea...

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 resistance eventually develops. In this issue of Cancer Cell, Fan et al. report results from LINUX, a randomized phase 2 trial applying an artificial intelligence digital pathology classification system to guide therapy after CDK4/6i progression.

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...

Quantification of PET activation in adipose tissue from non-contrast CT scans.

Brown adipose tissue (BAT) plays a key role in energy metabolism and cardiometabolic health. Its detection typically relies on 18F-FDG PET, which is c...

CT Radiation Dose Reduction With Preserved Diagnostic Performance: How Far Have We Come Over 25 Years?

Concerns about the risk of radiation from CT have driven a spectrum of major advances in radiation dose reduction technology since the 2000s, includin...

Prognostic Significance of Lymph Node Ratio for Overall Survival in Patients with Lymph Node-Positive Bladder Cancer Post-Radical Cystectomy and Chemotherapy: A Multicenter Cohort Analysis.

BACKGROUND: The lymph node ratio (LNR) is gaining recognition as a prognostic biomarker for various malignant neoplasms. However, its prognostic role ...

Advances in multi-omics for esophageal squamous cell carcinoma: Diagnostic, prognostic, and therapeutic perspectives.

Esophageal squamous cell carcinoma (ESCC) remains a major health burden, particularly in Asia, with poor patient prognosis despite advancements in rad...

Enhancing diagnostic safety with low iodine, low radiation CTPA classification using deep learning.

Pulmonary embolism (PE) is a life-threatening condition for which computed tomography pulmonary angiography (CTPA) is the standard diagnostic modality...

Comparison of Image Quality Reconstructed Using Iterative Reconstruction and Deep Learning Algorithms Under Varying Dose Reductions in Dual-Energy Carotid CT Angiography.

Carotid CT angiography (CTA) is valuable for diagnosing carotid artery disease but involves radiation and contrast agent risks. Deep Learning Image Re...

Pilot clinical evaluation of artificial intelligence-driven guiding catheter simulation for optimizing percutaneous coronary intervention.

AIMS: In percutaneous coronary intervention (PCI), a suboptimal choice of guiding catheter may compromise coaxial alignment and backup support, prolon...

Foundations for AI-assisted Adverse Outcome Pathways (AOPs) in radiation research.

PURPOSE: Artificial Intelligence (AI) and Machine Learning (ML) are being explored to improve systematic evidence gathering and to identify patterns a...