Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative
information from digital histopathology images. AI is expected to reduce workload for human …

Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict
patient outcomes, and inform treatment planning. Here, we review recent applications of ML …

Medical image segmentation is a critical component in clinical practice, facilitating accurate
diagnosis, treatment planning, and disease monitoring. However, existing methods, often …

Emerging as a potent anticancer treatment, subcellular targeted cancer therapy has drawn
increasing attention, bringing great opportunities for clinical application. Here, two targeting …

Background Prediction of microvascular invasion (MVI) may help determine treatment
strategies for hepatocellular carcinoma (HCC). Purpose To develop a radiomics approach …

Objective The main aim of the present systematic review was a comprehensive overview of
the Radiomics Quality Score (RQS)–based systematic reviews to highlight common issues …

Background Macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is
an aggressive variant associated with angiogenesis and immunosuppressive tumor …

Lung cancer accounts for the main proportion of malignancy-related deaths and most
patients are diagnosed at an advanced stage. Immunotherapy and targeted therapy have …

Artificial Intelligence (AI) has made significant strides in revolutionizing cancer care,
encompassing various aspects from diagnosis to prevention and beyond. With its ability to …

Immune checkpoint inhibitors (ICIs) show prominent clinical activity across multiple
advanced tumors. However, less than half of patients respond even after molecule-based …