Oncology/Hematology

Integration of protein and metabolomic blood biomarkers enhances classification of radiation exposure in X-irradiated humanized mice.

PURPOSE: The general population is at risk of exposure to ionizing radiation due to nuclear warfare, terrorism, or radiological accidents. Such exposures can lead to acute radiation syndrome (ARS), a set of debilitating and often fatal health effects that start within the first week after irradiation. ARS is largely defined by injuries to the bone marrow and intestinal tract, two organ systems tha...

A Bilayer Feature Fusion Framework for Pan-Cancer Survival Prediction Based on Multihead Attention and Adaptive Differential Privacy: Model Development and Validation Study.

BACKGROUND: In the field of precision medicine, pan-cancer survival prediction is crucial for individualized oncology diagnosis and treatment. Although multimodal data fusion techniques have significantly improved prediction accuracy, existing studies generally overlook the sensitivity of medical data and the need for privacy protection. OBJECTIVE: To address the aforementioned problem, this study...

Inverse treatment planning using deep learning-based organs at risk in radiotherapy for head and neck cancer: a prospective planning study.

PURPOSE: Radiotherapy (RT) planning for head and neck squamous cell carcinoma (HNSCC) is known to be both challenging and time-consuming. Deep learnin...

Neoantigen Targeting as a Novel Approach for Therapy-Resistant Tumors.

Neoantigens have emerged as central targets in the development of individualized cancer vaccines, as they are recognized by the immune system as forei...

Cross-platform deep learning enables automated cytomorphologic subtyping of acute leukemia from bone marrow smears.

Accurate subtyping of acute leukemia is essential for guiding therapy and predicting patient outcomes. Morphological assessment remains challenging fo...

Machine learning identifies a 10-gene signature predicting hepatocellular carcinoma recurrence and immune-metabolic reprogramming.

BACKGROUND: Hepatocellular carcinoma (HCC) remains a major cause of cancer-related mortality, with frequent recurrence after curative treatment. Conve...

Cardioprotection in oncology: Mechanisms, risks and therapeutic strategies.

Objectives of this review is to conduct an analysis of current data of cardiotoxicity of anticancer drugs and radiation therapy, methods of cardiovasc...

IGSF9 drives malignant transformation and predicts early-stage lung adenocarcinoma in integrated transcriptomic analyses.

PURPOSE: Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer (NSCLC), often presents with mild or absent symptoms in its...

Reinforcement learning for real-time adaptive radiotherapy.

State-of-the-art radiotherapy machines with integrated magnetic resonance (MR) imaging, known as MR-Linacs, provide the capability to track tumors in ...

Hypothesis Generation via Interpretable Machine Learning: A Case Study on Risk Factors for Postradiation Therapy Lung Cancer Recurrence.

PURPOSE: Interpretability is highly desirable for oncologic outcome prediction, as it increases the level of transparency and trustworthiness of the m...

HOX code-based stratification reveals RUNX1T1-HDAC reprogramming as a targetable driver of lineage plasticity across cancers.

Cancer remains a leading cause of death worldwide, with lineage plasticity emerging as a hallmark that drives therapy resistance and tumor progression...

Extraction of distant recurrence sites for breast cancer patients from free-text clinical notes using large language models.

OBJECTIVES: Accurate documentation of distant recurrence sites in breast cancer is essential for evaluating treatment effectiveness and outcomes resea...

Nanoparticle-based immunotherapeutic strategies to overcome cancer drug resistance: From biological barriers to artificial intelligence-driven design.

Although cancer immunotherapy has revolutionized oncology, its clinical efficacy remains substantially limited by both primary and acquired resistance...

Evolution of Aptamer Materials Maps Exosomal Surface Proteins at the Nano-Bio Interface.

Exosomes are nanoscale extracellular vesicles whose membrane-embedded proteins encode phenotypic information but remain difficult to interrogate due t...

Fast 3D whole-body occupational dose estimation in interventional radiology using physics-informed deep learning.

Occupational radiation exposure in interventional radiology is spatially heterogeneous and inadequately captured by conventional point-based dosimetry...

PTBP1 knockdown reprograms glioma stem cells into neuronal-like cells and suppresses tumorigenesis via the DUSP5-ERK1/2 signaling pathway.

BACKGROUND: Glioblastoma (GBM), the most prevalent and aggressive primary brain tumor in adults, has a median survival of merely 14 months. Current th...

Machine learning-predicted chromatin organization landscape across pediatric tumors.

Structural variants (SVs) are increasingly recognized as important contributors to oncogenesis through their effects on 3D genome folding. Recent adva...

Machine learning classification of normal and malignant cells on the basis of their viscoelastic properties.

BACKGROUND AND OBJECTIVE: Cell mechanics, elasticity and viscoelasticity, are key markers of biological states like cancer. Atomic force microscopy (A...

Dosimetric comparison of intensity modulated proton therapy and knowledge-based volumetric arc therapy for left-sided breast cancer.

INTRODUCTION: There is increasing interest in the application of proton beam therapy to left-sided breast cancer, due itspotential to reduce heart dos...

Artificial intelligence-assisted three-dimensional imaging of breast microinvasive carcinoma reveals larger invasive focus size in a substantial proportion of cases.

Microinvasive carcinoma of breast is a unique type of malignancy characterized by the presence of small invasive foci (less than 1 mm in diameter) in ...