[18F]FDG PET-Based Radiomics and Machine Learning for the Assessment of Gliomas and Glioblastomas: A Systematic Review
Background: Some evidence of the value of 18F-fluorodesoxyglucose ([18F]FDG) positron emission tomography (PET) imaging for the assessment of gliomas and glioblastomas (GBMs) is emerging. The aim of this systematic review was to assess the role of [18F]FDG PET-based radiomics and machine learning (M...
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2025-01-01
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| author | Francesco Dondi Roberto Gatta Maria Gazzilli Pietro Bellini Gian Luca Viganò Cristina Ferrari Antonio Rosario Pisani Giuseppe Rubini Francesco Bertagna |
| author_facet | Francesco Dondi Roberto Gatta Maria Gazzilli Pietro Bellini Gian Luca Viganò Cristina Ferrari Antonio Rosario Pisani Giuseppe Rubini Francesco Bertagna |
| author_sort | Francesco Dondi |
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| description | Background: Some evidence of the value of 18F-fluorodesoxyglucose ([18F]FDG) positron emission tomography (PET) imaging for the assessment of gliomas and glioblastomas (GBMs) is emerging. The aim of this systematic review was to assess the role of [18F]FDG PET-based radiomics and machine learning (ML) in the evaluation of these neoplasms. Methods: A wide literature search of the PubMed/MEDLINE, Scopus, and Cochrane Library databases was made to find relevant published articles on the role of [18F]FDG PET-based radiomics and ML for the assessment of gliomas and GBMs. Results: Eight studies were included in the systematic review. Signatures, including radiomics analysis and ML, generally demonstrated a possible diagnostic value to assess different characteristics of gliomas and GBMs, such as the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter, the isocitrate dehydrogenase (IDH) genotype, alpha thalassemia/mental retardation X-linked (ATRX) mutation status, proliferative activity, differential diagnosis with solitary brain metastases or primary central nervous system lymphoma, and prognosis of these patients. Conclusion: Despite some intrinsic limitations of radiomics and ML affecting the studies included in the review, some initial insights on the promising role of these technologies for the assessment of gliomas and GBMs are emerging. Validation of these preliminary findings in multicentric studies is needed to translate radiomics and ML approaches in the clinical setting. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-0a056b83ca1c4e2d8a0c81e4af469b5a2025-01-24T13:35:18ZengMDPI AGInformation2078-24892025-01-011615810.3390/info16010058[18F]FDG PET-Based Radiomics and Machine Learning for the Assessment of Gliomas and Glioblastomas: A Systematic ReviewFrancesco Dondi0Roberto Gatta1Maria Gazzilli2Pietro Bellini3Gian Luca Viganò4Cristina Ferrari5Antonio Rosario Pisani6Giuseppe Rubini7Francesco Bertagna8Nuclear Medicine, Università Degli Studi di Brescia and ASST Spedali Civili di Brescia, 25123 Brescia, ItalyDipartimento di Scienze Cliniche e Sperimentali, Università degli Studi di Brescia, 25123 Brescia, ItalyNuclear Medicine, ASL Bari—P.O. Di Venere, 70012 Bari, ItalyNuclear Medicine, ASST Spedali Civili di Brescia, 25123 Brescia, ItalyClinical Engineering, ASST Spedali Civili di Brescia, 25123 Brescia, ItalySection of Nuclear Medicine, Interdisciplinary Department of Medicine, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, ItalySection of Nuclear Medicine, Interdisciplinary Department of Medicine, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, ItalySection of Nuclear Medicine, Interdisciplinary Department of Medicine, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, ItalyNuclear Medicine, Università Degli Studi di Brescia and ASST Spedali Civili di Brescia, 25123 Brescia, ItalyBackground: Some evidence of the value of 18F-fluorodesoxyglucose ([18F]FDG) positron emission tomography (PET) imaging for the assessment of gliomas and glioblastomas (GBMs) is emerging. The aim of this systematic review was to assess the role of [18F]FDG PET-based radiomics and machine learning (ML) in the evaluation of these neoplasms. Methods: A wide literature search of the PubMed/MEDLINE, Scopus, and Cochrane Library databases was made to find relevant published articles on the role of [18F]FDG PET-based radiomics and ML for the assessment of gliomas and GBMs. Results: Eight studies were included in the systematic review. Signatures, including radiomics analysis and ML, generally demonstrated a possible diagnostic value to assess different characteristics of gliomas and GBMs, such as the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter, the isocitrate dehydrogenase (IDH) genotype, alpha thalassemia/mental retardation X-linked (ATRX) mutation status, proliferative activity, differential diagnosis with solitary brain metastases or primary central nervous system lymphoma, and prognosis of these patients. Conclusion: Despite some intrinsic limitations of radiomics and ML affecting the studies included in the review, some initial insights on the promising role of these technologies for the assessment of gliomas and GBMs are emerging. Validation of these preliminary findings in multicentric studies is needed to translate radiomics and ML approaches in the clinical setting.https://www.mdpi.com/2078-2489/16/1/58PETPET/CTpositron emission tomography[18F]FDGgliomaglioblastoma |
| spellingShingle | Francesco Dondi Roberto Gatta Maria Gazzilli Pietro Bellini Gian Luca Viganò Cristina Ferrari Antonio Rosario Pisani Giuseppe Rubini Francesco Bertagna [18F]FDG PET-Based Radiomics and Machine Learning for the Assessment of Gliomas and Glioblastomas: A Systematic Review Information PET PET/CT positron emission tomography [18F]FDG glioma glioblastoma |
| title | [18F]FDG PET-Based Radiomics and Machine Learning for the Assessment of Gliomas and Glioblastomas: A Systematic Review |
| title_full | [18F]FDG PET-Based Radiomics and Machine Learning for the Assessment of Gliomas and Glioblastomas: A Systematic Review |
| title_fullStr | [18F]FDG PET-Based Radiomics and Machine Learning for the Assessment of Gliomas and Glioblastomas: A Systematic Review |
| title_full_unstemmed | [18F]FDG PET-Based Radiomics and Machine Learning for the Assessment of Gliomas and Glioblastomas: A Systematic Review |
| title_short | [18F]FDG PET-Based Radiomics and Machine Learning for the Assessment of Gliomas and Glioblastomas: A Systematic Review |
| title_sort | 18f fdg pet based radiomics and machine learning for the assessment of gliomas and glioblastomas a systematic review |
| topic | PET PET/CT positron emission tomography [18F]FDG glioma glioblastoma |
| url | https://www.mdpi.com/2078-2489/16/1/58 |
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