TY - JOUR
T1 - Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer
AU - Roisman, Laila C.
AU - Kian, Waleed
AU - Anoze, Alaa
AU - Fuchs, Vered
AU - Spector, Maria
AU - Steiner, Roee
AU - Kassel, Levi
AU - Rechnitzer, Gilad
AU - Fried, Iris
AU - Peled, Nir
AU - Bogot, Naama R.
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023/12/1
Y1 - 2023/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85177637031&partnerID=8YFLogxK
U2 - 10.1038/s41698-023-00473-x
DO - 10.1038/s41698-023-00473-x
M3 - Review article
C2 - 37990050
AN - SCOPUS:85177637031
SN - 2397-768X
VL - 7
JO - npj Precision Oncology
JF - npj Precision Oncology
IS - 1
M1 - 125
ER -