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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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Hauptverfasser: Ligero, Marta (VerfasserIn) , El Nahhas, Omar S. M. (VerfasserIn) , Aldea, Mihaela (VerfasserIn) , Kather, Jakob Nikolas (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: March 2025
In: Trends in cancer
Year: 2025, Jahrgang: 11, Heft: 3, Pages: 232-244
ISSN:2405-8025
DOI:10.1016/j.trecan.2024.12.001
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.trecan.2024.12.001
Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S2405803324002802
Volltext
Verfasserangaben:Marta Ligero, Omar S.M. El Nahhas, Mihaela Aldea, and Jakob Nikolas Kather

MARC

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520 |a 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. 
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