Applications of artificial intelligence and advanced imaging in pediatric diffuse midline glioma.
Authors
Affiliations (7)
Affiliations (7)
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), San Francisco, California, USA.
- Medical Scientist Training Program (MSTP), University of California San Diego (UCSD), San Diego, California, USA.
- Department of Radiation Oncology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
- Artificial Intelligence in Medicine (AIM) Program; Mass General Brigham, Harvard Medical School, Boston, Massachusetts, USA.
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Boston Children's Hospital, Boston, Massachusetts, USA.
- Department of Neurology, Department of Neurosurgery and Department of Pediatrics, UCSF, San Francisco, California, USA.
- The University of Zurich, Department of Pediatrics, Zurich, Switzerland.
Abstract
Diffuse midline glioma (DMG) is a rare, aggressive, and fatal tumor that largely occurs in the pediatric population. To improve outcomes, it is important to characterize DMGs, which can be performed via magnetic resonance imaging (MRI) assessment. Recently, artificial intelligence (AI) and advanced imaging have demonstrated their potential to improve the evaluation of various brain tumors, gleaning more information from imaging data than is possible without these methods. This narrative review compiles the existing literature on the intersection of MRI-based AI use and DMG tumors. The applications of AI in DMG revolve around classification and diagnosis, segmentation, radiogenomics, and prognosis/survival prediction. Currently published articles have utilized a wide spectrum of AI algorithms, from traditional machine learning and radiomics to neural networks. Challenges include the lack of cohorts of DMG patients with publicly available, multi-institutional, multimodal imaging and genomics datasets as well as the overall rarity of the disease. As an adjunct to AI, advanced MRI techniques, including diffusion-weighted imaging, perfusion-weighted imaging, and Magnetic Resonance Spectroscopy (MRS), as well as positron emission tomography (PET), provide additional insights into DMGs. Establishing AI models in conjunction with advanced imaging modalities has the potential to push clinical practice toward precision medicine.