Radiomics for Differentiation of Pediatric Posterior Fossa Tumors: A Meta-Analysis and Systematic Review of the Literature
Purpose: To better define the overall performance of the current radiomics-based models for the discrimination of pediatric posterior fossa tumors. Methods: A comprehensive literature search of the databases PubMed, Ovid MEDLINE, Ovid EMBASE, Web of Science, and Scopus was designed and conducted by...
| Main Authors: | , , , , , |
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| Format: | Article |
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MDPI AG
2023-12-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/15/24/5891 |
| _version_ | 1797381657834029056 |
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| author | Alexandru Garaba Francesco Ponzio Eleonora Agata Grasso Waleed Brinjikji Marco Maria Fontanella Lucio De Maria |
| author_facet | Alexandru Garaba Francesco Ponzio Eleonora Agata Grasso Waleed Brinjikji Marco Maria Fontanella Lucio De Maria |
| author_sort | Alexandru Garaba |
| collection | DOAJ |
| description | Purpose: To better define the overall performance of the current radiomics-based models for the discrimination of pediatric posterior fossa tumors. Methods: A comprehensive literature search of the databases PubMed, Ovid MEDLINE, Ovid EMBASE, Web of Science, and Scopus was designed and conducted by an experienced librarian. We estimated overall sensitivity (SEN) and specificity (SPE). Event rates were pooled across studies using a random-effects meta-analysis, and the χ<sup>2</sup> test was performed to assess the heterogeneity. Results: Overall SEN and SPE for differentiation between MB, PA, and EP were found to be promising, with SEN values of 93% (95% CI = 0.88–0.96), 83% (95% CI = 0.66–0.93), and 85% (95% CI = 0.71–0.93), and corresponding SPE values of 87% (95% CI = 0.82–0.90), 95% (95% CI = 0.90–0.98) and 90% (95% CI = 0.84–0.94), respectively. For MB, there is a better trend for LR classifiers, while textural features are the most used and the best performing (ACC 96%). As for PA and EP, a synergistic employment of LR and NN classifiers, accompanied by geometrical or morphological features, demonstrated superior performance (ACC 94% and 96%, respectively). Conclusions: The diagnostic performance is high, making radiomics a helpful method to discriminate these tumor types. In the forthcoming years, we expect even more precise models. |
| first_indexed | 2024-03-08T20:54:34Z |
| format | Article |
| id | doaj.art-40a0f2fdc5314587b0cdf9dd3738778f |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-08T20:54:34Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-40a0f2fdc5314587b0cdf9dd3738778f2023-12-22T13:59:11ZengMDPI AGCancers2072-66942023-12-011524589110.3390/cancers15245891Radiomics for Differentiation of Pediatric Posterior Fossa Tumors: A Meta-Analysis and Systematic Review of the LiteratureAlexandru Garaba0Francesco Ponzio1Eleonora Agata Grasso2Waleed Brinjikji3Marco Maria Fontanella4Lucio De Maria5Department of Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25121 Brescia, ItalyInteruniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, 10129 Torino, ItalyDepartment of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA 19146, USADepartment of Neurosurgery and Interventional Neuroradiology, Mayo Clinic, Rochester, MN 55905, USADepartment of Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25121 Brescia, ItalyDepartment of Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25121 Brescia, ItalyPurpose: To better define the overall performance of the current radiomics-based models for the discrimination of pediatric posterior fossa tumors. Methods: A comprehensive literature search of the databases PubMed, Ovid MEDLINE, Ovid EMBASE, Web of Science, and Scopus was designed and conducted by an experienced librarian. We estimated overall sensitivity (SEN) and specificity (SPE). Event rates were pooled across studies using a random-effects meta-analysis, and the χ<sup>2</sup> test was performed to assess the heterogeneity. Results: Overall SEN and SPE for differentiation between MB, PA, and EP were found to be promising, with SEN values of 93% (95% CI = 0.88–0.96), 83% (95% CI = 0.66–0.93), and 85% (95% CI = 0.71–0.93), and corresponding SPE values of 87% (95% CI = 0.82–0.90), 95% (95% CI = 0.90–0.98) and 90% (95% CI = 0.84–0.94), respectively. For MB, there is a better trend for LR classifiers, while textural features are the most used and the best performing (ACC 96%). As for PA and EP, a synergistic employment of LR and NN classifiers, accompanied by geometrical or morphological features, demonstrated superior performance (ACC 94% and 96%, respectively). Conclusions: The diagnostic performance is high, making radiomics a helpful method to discriminate these tumor types. In the forthcoming years, we expect even more precise models.https://www.mdpi.com/2072-6694/15/24/5891radiomicsmachine learningdeep learningpediatric tumorsposterior fossasystematic review |
| spellingShingle | Alexandru Garaba Francesco Ponzio Eleonora Agata Grasso Waleed Brinjikji Marco Maria Fontanella Lucio De Maria Radiomics for Differentiation of Pediatric Posterior Fossa Tumors: A Meta-Analysis and Systematic Review of the Literature Cancers radiomics machine learning deep learning pediatric tumors posterior fossa systematic review |
| title | Radiomics for Differentiation of Pediatric Posterior Fossa Tumors: A Meta-Analysis and Systematic Review of the Literature |
| title_full | Radiomics for Differentiation of Pediatric Posterior Fossa Tumors: A Meta-Analysis and Systematic Review of the Literature |
| title_fullStr | Radiomics for Differentiation of Pediatric Posterior Fossa Tumors: A Meta-Analysis and Systematic Review of the Literature |
| title_full_unstemmed | Radiomics for Differentiation of Pediatric Posterior Fossa Tumors: A Meta-Analysis and Systematic Review of the Literature |
| title_short | Radiomics for Differentiation of Pediatric Posterior Fossa Tumors: A Meta-Analysis and Systematic Review of the Literature |
| title_sort | radiomics for differentiation of pediatric posterior fossa tumors a meta analysis and systematic review of the literature |
| topic | radiomics machine learning deep learning pediatric tumors posterior fossa systematic review |
| url | https://www.mdpi.com/2072-6694/15/24/5891 |
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