Brain Tumor Res Treat.  2022 Apr;10(2):69-75. 10.14791/btrt.2021.0031.

Artificial Intelligence in Neuro-Oncologic Imaging: A Brief Review for Clinical Use Cases and Future Perspectives

Affiliations
  • 1Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

Abstract

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.

Keyword

Artificial intelligence; Brain tumor; Deep learning; Imaging genomics.

Figure

  • Fig. 1 The hierarchy of artificial intelligence, machine learning, and deep learning.

  • Fig. 2 Diagram demonstrating artificial intelligence (AI), machine learning (ML), and deep learning in the clinical workflow of neuro-oncology patients. Following image acquisition, deep learning-based reconstruction can be applied to reduce noise and improve image quality. Then, AI-assisted image-based tasks are performed, which include deep learning-based detection and segmentation. After segmentation, the quantitative analysis of radiomics can be applied, and further analyses are performed using ML. AI-assisted image-based tasks help to provide quantitative and standardized reporting. Importantly, deep learning-based image generation can be applied during the data input stage and may improve prediction performance during every process of AI in neuro-oncologic imaging.

  • Fig. 3 Representative cases of deep learning detection for brain metastasis on black blood and white blood imaging, respectively. The red colored dots are AI prediction for brain metastasis. The enhancing lesion is a vascular structure (yellow arrow, pseudo-lesion) that leads to a false-positive artificial intelligence (AI) prediction.

  • Fig. 4 Representative cases of deep learning reconstruction (DLR) for pituitary adenoma. The DLR image provides better image quality with lesion conspicuity. Note that the residual mass in the left cavernous sinus (yellow arrow) can be clearly visualized on the 1 mm DLR image.


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