
Artificial Intelligence in Neuro-Oncologic Imaging: A Brief Review for Clinical Use Cases and Future Perspectives
Author(s) -
Ji Eun Park
Publication year - 2022
Publication title -
brain tumor research and treatment
Language(s) - English
Resource type - Journals
eISSN - 2288-2413
pISSN - 2288-2405
DOI - 10.14791/btrt.2021.0031
Subject(s) - radiogenomics , medicine , deep learning , artificial intelligence , radiomics , neuroimaging , medical imaging , machine learning , medical physics , computer science , radiology , psychiatry
The artificial intelligence (AI) techniques, both deep learning end-to-end approaches and radiomics with machine learning, have been developed for various imaging-based tasks in neuro-oncology. In this brief review, use cases of AI in neuro-oncologic imaging are summarized: image quality improvement, metastasis detection, radiogenomics, and treatment response monitoring. We then give a brief overview of generative adversarial network and potential utility of synthetic images for various deep learning algorithms of imaging-based tasks and image translation tasks as becoming new data input. Lastly, we highlight the importance of cohorts and clinical trial as a true validation for clinical utility of AI in neuro-oncologic imaging.