Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer

Laila C. Roisman, Waleed Kian, Alaa Anoze, Vered Fuchs, Maria Spector, Roee Steiner, Levi Kassel, Gilad Rechnitzer, Iris Fried, Nir Peled, Naama R. Bogot

Research output: Contribution to journalReview articlepeer-review

Abstract

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.

Original languageEnglish
Article number125
Journalnpj Precision Oncology
Volume7
Issue number1
DOIs
StatePublished - 1 Dec 2023

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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