Cancer immunotherapy efficacy and machine learning.
Expert Rev Anticancer Ther
; 24(1-2): 21-28, 2024.
Article
en En
| MEDLINE
| ID: mdl-38288663
ABSTRACT
INTRODUCTION:
Immunotherapy is one of the major breakthroughs in the treatment of cancer, and it has become a powerful clinical strategy, however, not all patients respond to immune checkpoint blockade and other immunotherapy strategies. Applying machine learning (ML) techniques to predict the efficacy of cancer immunotherapy is useful for clinical decision-making. AREAS COVERED Applying ML including deep learning (DL) in radiomics, pathomics, tumor microenvironment (TME) and immune-related genes analysis to predict immunotherapy efficacy. The studies in this review were searched from PubMed and ClinicalTrials.gov (January 2023). EXPERT OPINION An increasing number of studies indicate that ML has been applied to various aspects of oncology research, with the potential to provide more effective individualized immunotherapy strategies and enhance treatment decisions. With advances in ML technology, more efficient methods of predicting the efficacy of immunotherapy may become available in the future.Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Inmunoterapia
/
Neoplasias
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Expert Rev Anticancer Ther
Asunto de la revista:
NEOPLASIAS
/
TERAPEUTICA
Año:
2024
Tipo del documento:
Article