Radiomics for Differentiation of Pediatric Posterior Fossa Tumors: A Meta-Analysis and Systematic Review of the Literature.

Autor: Garaba A; Department of Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25121 Brescia, Italy.; Unit of Neurosurgery, Spedali Civili Hospital, Largo Spedali Civili 1, 25123 Brescia, Italy., Ponzio F; Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, 10129 Torino, Italy., Grasso EA; Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA 19146, USA., Brinjikji W; Department of Neurosurgery and Interventional Neuroradiology, Mayo Clinic, Rochester, MN 55905, USA., Fontanella MM; Department of Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25121 Brescia, Italy., De Maria L; Department of Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25121 Brescia, Italy.; Department of Clinical Neuroscience, Geneva University Hospitals (HUG), 1205 Geneva, Switzerland.
Jazyk: angličtina
Zdroj: Cancers [Cancers (Basel)] 2023 Dec 18; Vol. 15 (24). Date of Electronic Publication: 2023 Dec 18.
DOI: 10.3390/cancers15245891
Abstrakt: 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 2 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.
Databáze: MEDLINE
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