Latest AI and machine learning research in oncology/hematology for healthcare professionals.
PURPOSE: Cancer treatments such as chemotherapy, targeted therapy, and immunotherapy can effectively combat malignant cells but frequently cause serious side effects by damaging healthy tissues. This underscores the need for robust Adverse Drug Event (ADE) reporting and summarization frameworks to improve patient safety, support clinical decision-making, and optimize treatment outcomes. METHODS: I...
Acute lymphoblastic leukemia (ALL) is a hematological malignancy characterized by the rapid proliferation of immature white blood cells in the bone marrow. Early and accurate diagnosis is essential for improving clinical outcomes; however, distinguishing between lymphocytes and lymphoblasts poses significant challenges owing to their subtle morphological similarities. Traditional manual diagnostic...
Cancer remains a leading cause of global mortality, with early diagnosis being pivotal for improving treatment outcomes. Traditional tissue biopsy is ...
Large language models (LLMs) have emerged in recent years as innovative artificial intelligence systems with early potential in clinical decision-maki...
INTRODUCTION: Accurate preoperative imaging is essential for improving the treatment of small lung cancers. Precise identification of non-invasive ade...
The interaction between autophagy and ferroptosis has resulted in the identification of novel approaches for the treatment of lung cancer (LC). The tw...
Pulmonary fibrosis (PF) is a progressive and fatal interstitial lung disease characterized by irreversible lung scarring and frequently associated wit...
Nitenpyram (NIT) is an insecticide used primarily for flea control in pets, especially cats and dogs. Some studies suggest that NIT is associated with...
Advanced differentiated thyroid cancer (DTC) is characterized by limited therapeutic options and unfavorable prognosis. To address this, we conduct pr...
Accurate distinguishing the phenotype of glioblastoma (GBM) cell lines remains challenging in clinical diagnostics, particularly for rapid intraoperat...
BACKGROUND: Women with a history of breast cancer face an elevated risk of developing contralateral breast cancer (CBC). Although annual mammographic ...
BACKGROUND: Early detection of cancer and precise recurrence monitoring remain major unmet needs in oncology. Conventional screening is limited to a f...
Traditional spatial transcriptomics methods typically rely on the direct relationship between spatial location and gene expression data, but they ofte...
The aim of the study was to evaluate the concordance between radiological imaging modalities and pathological findings and to test whether neoadjuvant...
MicroRNAs (miRNAs) serve as crucial biomarkers in disease diagnosis. Although silicon-based electronic machine learning models provide efficient means...
PURPOSE: Personalized immunotherapy strategies are urgently needed for patients with locoregionally advanced nasopharyngeal carcinoma (NPC). We aim to...
OBJECTIVE: To develop machine-learning models that incorporate clinical information and radiomics features extracted from ultrasound images to disting...
Breast cancer remains a leading global health concern in women, while screening is still limited by imaging accessibility and reduced sensitivity in d...