Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer.

Autor: Roisman LC; The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel. dr.roisman@gmail.com.; Ben-Gurion University of the Negev, Be'er Sheva, Israel. dr.roisman@gmail.com., Kian W; The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel.; The Institute of Oncology, Assuta Ashdod, Ashdod, Israel., Anoze A; The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel., Fuchs V; Ben-Gurion University of the Negev, Be'er Sheva, Israel., Spector M; The Department of Radiology, Shaare Zedek Medical Center, Jerusalem, Israel., Steiner R; The Institute for Nuclear Medicine, Shaare Zedek Medical Center, Jerusalem, Israel., Kassel L; The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel., Rechnitzer G; The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel., Fried I; The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel., Peled N; The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel. nirp@szmc.org.il., Bogot NR; The Department of Radiology, Shaare Zedek Medical Center, Jerusalem, Israel.
Jazyk: angličtina
Zdroj: NPJ precision oncology [NPJ Precis Oncol] 2023 Nov 21; Vol. 7 (1), pp. 125. Date of Electronic Publication: 2023 Nov 21.
DOI: 10.1038/s41698-023-00473-x
Abstrakt: 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.
(© 2023. The Author(s).)
Databáze: MEDLINE
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