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One Step Forward—The Current Role of Artificial Intelligence in Glioblastoma Imaging
Artificial intelligence (AI) is rapidly integrating into diagnostic methods across many branches of medicine. Significant progress has been made in tumor assessment using AI algorithms, and research is underway on how image manipulation can provide information with diagnostic, prognostic and treatme...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10381280/ https://www.ncbi.nlm.nih.gov/pubmed/37511936 http://dx.doi.org/10.3390/life13071561 |
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author | Chirica, Costin Haba, Danisia Cojocaru, Elena Mazga, Andreea Isabela Eva, Lucian Dobrovat, Bogdan Ionut Chirica, Sabina Ioana Stirban, Ioana Rotundu, Andreea Leon, Maria Magdalena |
author_facet | Chirica, Costin Haba, Danisia Cojocaru, Elena Mazga, Andreea Isabela Eva, Lucian Dobrovat, Bogdan Ionut Chirica, Sabina Ioana Stirban, Ioana Rotundu, Andreea Leon, Maria Magdalena |
author_sort | Chirica, Costin |
collection | PubMed |
description | Artificial intelligence (AI) is rapidly integrating into diagnostic methods across many branches of medicine. Significant progress has been made in tumor assessment using AI algorithms, and research is underway on how image manipulation can provide information with diagnostic, prognostic and treatment impacts. Glioblastoma (GB) remains the most common primary malignant brain tumor, with a median survival of 15 months. This paper presents literature data on GB imaging and the contribution of AI to the characterization and tracking of GB, as well as recurrence. Furthermore, from an imaging point of view, the differential diagnosis of these tumors can be problematic. How can an AI algorithm help with differential diagnosis? The integration of clinical, radiomics and molecular markers via AI holds great potential as a tool for enhancing patient outcomes by distinguishing brain tumors from mimicking lesions, classifying and grading tumors, and evaluating them before and after treatment. Additionally, AI can aid in differentiating between tumor recurrence and post-treatment alterations, which can be challenging with conventional imaging methods. Overall, the integration of AI into GB imaging has the potential to significantly improve patient outcomes by enabling more accurate diagnosis, precise treatment planning and better monitoring of treatment response. |
format | Online Article Text |
id | pubmed-10381280 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103812802023-07-29 One Step Forward—The Current Role of Artificial Intelligence in Glioblastoma Imaging Chirica, Costin Haba, Danisia Cojocaru, Elena Mazga, Andreea Isabela Eva, Lucian Dobrovat, Bogdan Ionut Chirica, Sabina Ioana Stirban, Ioana Rotundu, Andreea Leon, Maria Magdalena Life (Basel) Review Artificial intelligence (AI) is rapidly integrating into diagnostic methods across many branches of medicine. Significant progress has been made in tumor assessment using AI algorithms, and research is underway on how image manipulation can provide information with diagnostic, prognostic and treatment impacts. Glioblastoma (GB) remains the most common primary malignant brain tumor, with a median survival of 15 months. This paper presents literature data on GB imaging and the contribution of AI to the characterization and tracking of GB, as well as recurrence. Furthermore, from an imaging point of view, the differential diagnosis of these tumors can be problematic. How can an AI algorithm help with differential diagnosis? The integration of clinical, radiomics and molecular markers via AI holds great potential as a tool for enhancing patient outcomes by distinguishing brain tumors from mimicking lesions, classifying and grading tumors, and evaluating them before and after treatment. Additionally, AI can aid in differentiating between tumor recurrence and post-treatment alterations, which can be challenging with conventional imaging methods. Overall, the integration of AI into GB imaging has the potential to significantly improve patient outcomes by enabling more accurate diagnosis, precise treatment planning and better monitoring of treatment response. MDPI 2023-07-14 /pmc/articles/PMC10381280/ /pubmed/37511936 http://dx.doi.org/10.3390/life13071561 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Chirica, Costin Haba, Danisia Cojocaru, Elena Mazga, Andreea Isabela Eva, Lucian Dobrovat, Bogdan Ionut Chirica, Sabina Ioana Stirban, Ioana Rotundu, Andreea Leon, Maria Magdalena One Step Forward—The Current Role of Artificial Intelligence in Glioblastoma Imaging |
title | One Step Forward—The Current Role of Artificial Intelligence in Glioblastoma Imaging |
title_full | One Step Forward—The Current Role of Artificial Intelligence in Glioblastoma Imaging |
title_fullStr | One Step Forward—The Current Role of Artificial Intelligence in Glioblastoma Imaging |
title_full_unstemmed | One Step Forward—The Current Role of Artificial Intelligence in Glioblastoma Imaging |
title_short | One Step Forward—The Current Role of Artificial Intelligence in Glioblastoma Imaging |
title_sort | one step forward—the current role of artificial intelligence in glioblastoma imaging |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10381280/ https://www.ncbi.nlm.nih.gov/pubmed/37511936 http://dx.doi.org/10.3390/life13071561 |
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