Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer.
Journal
NPJ precision oncology
ISSN: 2397-768X
Titre abrégé: NPJ Precis Oncol
Pays: England
ID NLM: 101708166
Informations de publication
Date de publication:
21 Nov 2023
21 Nov 2023
Historique:
received:
17
07
2023
accepted:
24
10
2023
medline:
22
11
2023
pubmed:
22
11
2023
entrez:
22
11
2023
Statut:
epublish
Résumé
Personalized medicine has revolutionized approaches to treatment in the field of lung cancer by enabling therapies to be specific to each patient. However, physicians encounter an immense number of challenges in providing the optimal treatment regimen for the individual given the sheer complexity of clinical aspects such as tumor molecular profile, tumor microenvironment, expected adverse events, acquired or inherent resistance mechanisms, the development of brain metastases, the limited availability of biomarkers and the choice of combination therapy. The integration of innovative next-generation technologies such as deep learning-a subset of machine learning-and radiomics has the potential to transform the field by supporting clinical decision making in cancer treatment and the delivery of precision therapies while integrating numerous clinical considerations. In this review, we present a brief explanation of the available technologies, the benefits of using these technologies in predicting immunotherapy response in lung cancer, and the expected future challenges in the context of precision medicine.
Identifiants
pubmed: 37990050
doi: 10.1038/s41698-023-00473-x
pii: 10.1038/s41698-023-00473-x
pmc: PMC10663598
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
125Subventions
Organisme : EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)
ID : 101057695
Organisme : EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)
ID : 101057695
Organisme : EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)
ID : 101057695
Informations de copyright
© 2023. The Author(s).
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