Cardiovascular

Predicting treatment response to neoadjuvant chemotherapy in locally advanced rectal cancer: A combined deep learning and machine learning approach utilizing longitudinal multi-sequence MRI.

PURPOSE: To develop and validate deep leaning-based machine learning models using longitudinal multi-sequence MRI for predicting treatment response of patients with locally advanced rectal cancer (LARC) to neoadjuvant chemotherapy (NCT). METHODS: This retrospective study included 169 LARC patients who received 2-4 cycles of CAPOX chemotherapy before surgery. Patients were randomly divided into a t...

Flow rate determination in a two-phase system using radioactive particle tracking and deep learning.

In the oil industry, accurate flow rate determination and control in pipelines are critical for ensuring operational efficiency. However, most conventional flowmeters require direct contact with the transported fluids, which necessitates periodic maintenance and may cause system shutdowns, thereby increasing operational costs. To address these limitations, this study proposes a minimally intrusive...

Non-invasive screening for ovarian cancer by combining serum SERS with interpretable machine learning models.

Early identification of malignant ovarian tumors is critical for informing treatment decisions and enhancing patients' quality of life. As the third m...

Neoadjuvant Radiation is Causally Linked to Increased Operative Time and Perioperative Blood Transfusion in Pancreatic Ductal Adenocarcinoma.

INTRODUCTION: Blood transfusion in patients undergoing surgical resection for pancreatic ductal adenocarcinoma (PDAC) is associated with worse outcome...

Deep Learning Integration of Endoscopic Ultrasound Features and Serum Data Reveals LTB4 as a Diagnostic and Therapeutic Target in ESCC.

BACKGROUND: Early diagnosis and accurate prediction of treatment response in esophageal squamous cell carcinoma (ESCC) remain major clinical challenge...

Prediction of Symptomatic Radiation Pneumonitis in Lung Cancer Patients: A Radiomics and Dosiomics Machine Learning Approach Using the Prospective Multicenter RTOG 0617 and REQUITE Trials.

PURPOSE: Radiation-induced pneumonitis (RP) is a side effect after thoracic radiation therapy (RT). The ability to predict RP would facilitate treatme...

Contemporary Management of Radiation Necrosis: Insights and Avenues.

Radiation necrosis (RN) remains a challenging complication of upfront radiation therapy for both brain metastases and primary tumors. Despite developm...

Artificial Intelligence for radiation dose reduction in computed tomography: a narrative synthesis of clinical evidence from 2020 to 2025.

Computed tomography (CT) is essential to modern clinical practice but contributes substantially to population radiation exposure, particularly in onco...

Systemic immunometabolic profiling classifies cisplatin sensitivity states using interpretable machine learning.

Cisplatin resistance limits the effectiveness of platinum-based chemotherapy for lung adenocarcinoma, yet practical systemic diagnostics for cisplatin...

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 inter...

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. ...

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...

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...