Artificial intelligence in the management of glioma: Era of personalized medicine
- Purpose: Artificial intelligence (AI) has accelerated novel discoveries across multiple disciplines including medicine. Clinical medicine suffers from a lack of AI-based applications, potentially due to lack of awareness of AI methodology. Future collaboration between computer scientists and clinicians is critical to maximize the benefits of transformative technology in this field for patients. To illustrate, we describe AI-based advances in the diagnosis and management of gliomas, the most common primary central nervous system (CNS) malignancy. Methods: Presented is a succinct description of foundational concepts of AI approaches and their relevance to clinical medicine, geared toward clinicians without computer science backgrounds. We also review novel AI approaches in the diagnosis and management of glioma. Results: Novel AI approaches in gliomas have been developed to predict the grading and genomics from imaging, automate the diagnosis from histopathology, and provide insight into prognosis. Conclusion: Novel AI approaches offer acceptable performance in gliomas. Further investigation is necessary to improve the methodology and determine the full clinical utility of these novel approaches.
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| Author: | Houman Sotoudeh, Omid Shafaat, Joshua D. BernstockORCiD, David Brooks, Galal Elsayed, Jason A. Chen, Paul Szerip, Gustavo Chagoya, Florian GeßlerORCiDGND, Ehsan Sotoudeh, Amir Shafaat, Gregory K. Friedman |
|---|---|
| URN: | urn:nbn:de:hebis:30:3-518678 |
| DOI: | https://doi.org/10.3389/fonc.2019.00768 |
| ISSN: | 2234-943X |
| Pubmed Id: | https://pubmed.ncbi.nlm.nih.gov/31475111 |
| Parent Title (English): | Frontiers in oncology |
| Publisher: | Frontiers Media |
| Place of publication: | Lausanne |
| Contributor(s): | Sandro Krieg |
| Document Type: | Article |
| Language: | English |
| Year of Completion: | 2019 |
| Date of first Publication: | 2019/08/14 |
| Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
| Release Date: | 2020/02/17 |
| Tag: | artificial intelligence; convolution neural network; deep neural network; glioma; neural network; support vector machines |
| Volume: | 9 |
| Issue: | Art. 768 |
| Page Number: | 11 |
| First Page: | 1 |
| Last Page: | 11 |
| Note: | Copyright © 2019 Sotoudeh, Shafaat, Bernstock, Brooks, Elsayed, Chen, Szerip, Chagoya, Gessler, Sotoudeh, Shafaat and Friedman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| HeBIS-PPN: | 460958143 |
| Institutes: | Medizin / Medizin |
| Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
| Sammlungen: | Universitätspublikationen |
| Licence (German): |  Creative Commons - Namensnennung 4.0 |
