A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking?

ABSTRACT: The search for understanding immunotherapy response has sparked interest in diverse areas of oncology, with artificial intelligence (AI) and radiomics emerging as promising tools, capable of gathering large amounts of information to identify suitable patients for treatment. The application of AI in radiology has grown, driven by the hypothesis that radiology images capture tumor phenotypes and thus could provide valuable insights into immunotherapy response likelihood. However, despite the rapid growth of studies, no algorithms in the field have reached clinical implementation, mainly due to the lack of standardized methods, hampering study comparisons and reproducibility across different datasets. In this review, we performed a comprehensive assessment of published data to identify source

SUBMITTER: Ligero M 

PROVIDER: S-EPMC10881558 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature