Breast Cancer

Predicting Bacterial Radiation Resistance Through Identifying Novel Genomic Features.

Radiation resistance in bacteria is a critical trait with implications for biotechnology, medicine and environmental science. Deinococcus species possess unique genomic features that enable their extraordinary survival in extreme environments. However, the diversity of radiation resistance mechanisms across bacteria remains poorly explored. Here, we analysed the genomic diversity and functional ge...

Explainable artificial intelligence in prostate cancer treatment recommendation: A decision support system for oncological expert panels.

BACKGROUND: Therapeutic decisions in clinical oncology are commonly established through interdisciplinary consensus in multidisciplinary cancer conferences (MCC). Artificial intelligence (AI) may support these processes by generating data-driven treatment recommendations (TR). We developed and evaluated an explainable AI system designed to reproduce MCC-based treatment decisions for metastatic and...

Assessing Image Quality and Diagnostic Performance of Quadruple-Low Coronary CT Angiography with Deep Learning in High-BMI Patients.

RATIONALE AND OBJECTIVES: To examine the feasibility of a quadruple-low protocol in coronary computed tomography angiography (CCTA) assisted by the de...

Integrating handcrafted and deep learning MRI signatures: an interpretable framework for predicting chemotherapy benefit in glioma.

The molecular and spatial heterogeneity of gliomas severely limits accurate prediction of postoperative adjuvant chemotherapy efficacy, representing a...

What's next for VI-RADS? Updates and future perspectives from the ACR VI-RADS steering committee.

Since the introduction of the Vesical Imaging-Reporting and Data System (VI-RADS), MRI has become an important imaging modality in the management of p...

A macrophage-related efferocytosis-based two-gene prognostic model for acute myeloid leukemia identified by multi-omics and machine learning.

Background Acute myeloid leukemia (AML) remains a lethal hematologic malignancy with high heterogeneity. Macrophage-mediated efferocytosis in the tumo...

Towards an accessible, centralised, searchable database for AI courses in Europe: the Artificial Intelligence in Medical Imaging and Radiation Oncology Education (AIMIROE) project.

OBJECTIVE: Artificial intelligence (AI) is transforming medical imaging and radiation oncology, yet limited understanding and access to education hind...

AI-MIRACLE: Artificial Intelligence and MultIpaRAmetric MRI Predict CLinical OutcomEs to Neoadjuvant Immunotherapy in Patients with Muscle-invasive Bladder Cancer Undergoing Radical Cystectomy.

BACKGROUND AND OBJECTIVE: Neoadjuvant immune-checkpoint inhibitors (ICIs) in muscle-invasive bladder cancer (MIBC) were tested in patient's ineligible...

Cyclosporine in hematological disorders: mechanisms, clinical practice and emerging advances.

Cyclosporine A (CsA) functions as a calcineurin inhibitor that perturbs T cell activation via calcineurin-nuclear factor of activated T cells (CaN-NFA...

Exercise-related genes predicts overall survival and tumor immune microenvironment, and identifies the biological role of SLC52A2 in breast cancer.

BACKGROUND: Breast cancer (BRCA) is the most common malignant tumor in women, with prognoses differing markedly by molecular subtype. Exercise is know...

A novel prognostic model based on MVIRGs identifies ANGPT2 as a key target driving the malignant progression of HCC.

BACKGROUND: Hepatocellular carcinoma (HCC) is a highly lethal malignancy with high recurrence and limited therapies. Microvascular invasion (MVI) is a...

DNA damage response signature-based prognostic genes for intrahepatic cholangiocarcinoma: a combined analysis of machine learning and biological experiments.

BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is a highly aggressive subtype of primary liver cancer with insidious onset, early metastasis, and p...

Improving Compton imaging of 478 keV prompt gamma-ray in boron neutron capture therapy with neural network-based event filtering: a simulation study.

Objective.Boron neutron capture therapy (BNCT) is a promising radiotherapy modality that effectively targets cancer cells through boron neutron captur...

Radiation dose has no significant impact on CT-based bone mineral density measurements in a large-animal model.

Bone mineral density (BMD) is a biomarker for frailty, and CT-derived radiodensity can be extracted fully automatically as a surrogate. Because these ...

Artificial intelligence and transforming cancer care.

Artificial Intelligence (AI) is reshaping oncology by addressing key limitations in traditional cancer care and enabling data-driven, personalized app...

Assessing biomarkers for patients with breast cancer who are suited for adjuvant therapy.

INTRODUCTION: Biomarker-guided stratification is essential for optimizing adjuvant systemic therapy in early-stage breast cancer, requiring a balance ...

Machine Learning Model Using Pre-Cancer Therapy Cardiac Magnetic Resonance Images to Predict Cancer Therapy-Related Cardiac Dysfunction.

BACKGROUND: Predicting risk of cancer therapy-related cardiac dysfunction (CTRCD) remains challenging. OBJECTIVES: The purpose of this study was to as...

Up-regulation of SLC26A3 enhances oxaliplatin sensitivity in colorectal cancer.

The role of SLC26A3 in the sensitivity to oxaliplatin, a widely used chemotherapy drug for colorectal cancer (CRC), remains unclear. This study aimed ...

Super-resolution deep learning reconstruction achieves low-dose high-resolution imaging in pediatric cardiac computed tomography.

BACKGROUND: Cardiac computed tomography (CT) is widely utilized in pediatric cardiology, but minimizing radiation exposure is essential. Recently, sup...

Review of systemic therapy in HCC with an approach to response assessment in clinical practice.

Systemic therapy for hepatocellular carcinoma (HCC) has undergone rapid transformation over the past decade, significantly expanding treatment options...