Radiomics is a quantitative tool for digital image analysis. This systematic review
aims to investigate the scientific articles to evaluate the potential implications
of Radiomics analysis in Dentomaxillofacial Radiology (DMFR). Studies regarding Radiomics
applications in DMFR and human samples, in vivo study, a case reports/series if >=
5 samples were included, while case reports/series if < 5 samples, articles other
than in English, abstracts without full text, and studies published before 2015 were
excluded. Fifty-one articles were selected from 3789 literatures. The QUADAS-2 tool
was used for risk of bias assessment. The accuracy of predicting dentomaxillofacial
pathologies was considered as the primary outcome, and the modeling type of Radiomics
was considered as the secondary outcome. A meta-analysis could not be performed due
to the lack of information and standardization among the reported accuracies. The
reported accuracies were found between 0.66 and 99.65%. Logistic regression (n = 6)
was found to be the most common Radiomics modeling type, followed by Support Vector
Machine and Decision Tree (n = 5). Second-order statistics (n = 38) was the most common
type of Radiomics application, followed by first-order (n = 26), higher-order (n =
20), and shape-based (n = 15) statistics. Further work is needed to increase standardization
in the Radiomics workflow. Quantitative image analysis is an alternative tool for
conventional visual radiographic evaluation. Radiomics systems depend on elements
such as imaging modality, feature type, data mining, or statistical method. Radiomics
applications do not justify digital transformation on their own, but the potential
of its integration into the digital workflow is considerable.
