Gastric cancer is a leading contributor to cancer incidence and mortality globally. Recently,
artificial intelligence approaches, particularly machine learning and deep learning, are …

Based on the advantages of revealing the functional status and molecular expression of
tumor cells, positron emission tomography (PET) imaging has been performed in numerous …

Background Gastric cancer is the most common malignant tumor worldwide and a leading
cause of cancer deaths. This neoplasm has a poor prognosis and heterogeneous outcomes …

Background Clinical prediction is integral to modern healthcare, leveraging current and
historical medical data to forecast health outcomes. The integration of Artificial Intelligence …

Background Current radiomics for treatment response assessment in gastric cancer (GC)
have focused solely on Computed tomography (CT). The importance of multiparametric …

Gastric cancer (GC), with a 5-year survival rate of less than 40%, is known as the fourth
principal reason of cancer-related mortality over the world. This study aims to develop …

Background: Gastric cancer remains one of the leading causes of worldwide cancer-specific
deaths. Accurately predicting the survival likelihood of gastric cancer patients can inform …

The objective of this review was to summarize published radiomics studies dealing with
infradiaphragmatic cancers, blood malignancies, melanoma, and musculoskeletal cancers …

Artificial intelligence applications in computed tomography in gastric cancer: a narrative review -
PMC Back to Top Skip to main content NIH NLM Logo Access keys NCBI Homepage MyNCBI …

Radiomics aims to develop novel biomarkers and provide relevant deeper subvisual
information about pathology, immunophenotype, and tumor microenvironment. It uses …