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Artificial intelligence-based biomarkers for treatment decisions in oncology

The development of new therapeutic strategies such as immune checkpoint inhibitors (ICIs) and targeted therapies has increased the complexity of the treatment landscape for solid tumors. At the current rate of annual FDA approvals, the potential treatment options could increase by tenfold over the n...

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Bibliographic Details
Main Authors: Ligero, Marta (Author) , El Nahhas, Omar S. M. (Author) , Aldea, Mihaela (Author) , Kather, Jakob Nikolas (Author)
Format: Article (Journal)
Language:English
Published: March 2025
In: Trends in cancer
Year: 2025, Volume: 11, Issue: 3, Pages: 232-244
ISSN:2405-8025
DOI:10.1016/j.trecan.2024.12.001
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.trecan.2024.12.001
Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S2405803324002802
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Author Notes:Marta Ligero, Omar S.M. El Nahhas, Mihaela Aldea, and Jakob Nikolas Kather
Description
Summary:The development of new therapeutic strategies such as immune checkpoint inhibitors (ICIs) and targeted therapies has increased the complexity of the treatment landscape for solid tumors. At the current rate of annual FDA approvals, the potential treatment options could increase by tenfold over the next 5 years. The cost of personalized medicine technologies limits its accessibility, thus increasing socioeconomic disparities in the treated population. In this review we describe artificial intelligence (AI)-based solutions - including deep learning (DL) methods for routine medical imaging and large language models (LLMs) for electronic health records (EHRs) - to support cancer treatment decisions with cost-effective biomarkers. We address the current limitations of these technologies and propose the next steps towards their adoption in routine clinical practice.
Item Description:Online verfügbar: 14. Januar 2025, Artikelversion: 11. März 2025
Gesehen am 19.08.2025
Physical Description:Online Resource
ISSN:2405-8025
DOI:10.1016/j.trecan.2024.12.001

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