Background The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised
treatment of multiple cancer types. However, selecting patients who may benefit from ICI …

The high-throughput extraction of quantitative imaging features from medical images for the
purpose of radiomic analysis, ie, radiomics in a broad sense, is a rapidly developing and …

Background Artificial intelligence (AI) is influencing our society on many levels and has
broad implications for the future practice of hematology and oncology. However, for many …

Summary Background Only around 20–30% of patients with non-small-cell lung cancer
(NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based …

Immunotherapy by immune checkpoint inhibitors has become a standard treatment strategy
for many types of solid tumors. However, the majority of patients with cancer will not …

With the increasing use of immune checkpoint inhibitors (ICIs), there is an urgent need to
identify biomarkers to stratify responders and non-responders using programmed death …

Artificial intelligence (AI) refers to computer algorithms used to complete tasks that usually
require human intelligence. Typical examples include complex decision-making and-image …

The use of radiomics and artificial intelligence applied for the diagnosis and monitoring of
Alzheimer's disease has developed in recent years. However, this approach is not yet …

Deep learning (DL) is a breakthrough technology for medical imaging with high sample size
requirements and interpretability issues. Using a pretrained DL model through a radiomics …

Original research: A Machine learning model trained on dual-energy CT radiomics significantly
improves immunotherapy response prediction for patients with stage IV melanoma - PMC Back …