Forecasting Molecular Features in IDH-Wildtype Gliomas: The State of the Art of Radiomics Applied to Neurosurgery

Gerardi, Rosa Maria; Cannella, Roberto; Bonosi, Lapo; Vernuccio, Federica; Ferini, Gianluca; Viola, Anna; Zagardo, Valentina; Buscemi, Felice; Costanzo, Roberta; Porzio, Massimiliano; Giovannini, Evier Andrea; Paolini, Federica; Brunasso, Lara; Giammalva, Giuseppe Roberto; Umana, Giuseppe Emmanuele; Scarpitta, Antonino; Iacopino, Domenico Gerardo; Maugeri, Rosario

doi:10.3390/cancers15030940

Cited by 4 publications

(1 citation statement)

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“…To the best of our knowledge, two studies have been conducted on the radiomics analysis of TERT classification in GBM (Gerardi et al., 2023 ; Zhang et al., 2023 ); however, these researchers do not encompass the selection of optimal algorithms and feature selectors for predicting TERT classification. Despite the valuable insights gained from these two studies, there are still several limitations that need to be addressed.…”

Section: Introductionmentioning

confidence: 99%

MRI radiomics model for predicting TERT promoter mutation status in glioblastoma

Chen,

et al. 2023

Brain and Behavior

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Background and purposeThe presence of TERT promoter mutations has been associated with worse prognosis and resistance to therapy for patients with glioblastoma (GBM). This study aimed to determine whether the combination model of different feature selections and classification algorithms based on multiparameter MRI can be used to predict TERT subtype in GBM patients.MethodsA total of 143 patients were included in our retrospective study, and 2553 features were obtained. The datasets were randomly divided into training and test sets in a ratio of 7:3. The synthetic minority oversampling technique was used to achieve data balance. The Pearson correlation coefficients were used for dimension reduction. Three feature selections and five classification algorithms were used to model the selected features. Finally, 10‐fold cross validation was applied to the training dataset.ResultsA model with eight features generated by recursive feature elimination (RFE) and linear discriminant analysis (LDA) showed the greatest diagnostic performance (area under the curve values for the training, validation, and testing sets: 0.983, 0.964, and 0.926, respectively), followed by relief and random forest (RF), analysis of variance and RF. Furthermore, the relief was the optimal feature selection for separately evaluating those five classification algorithms, and RF was the most preferable algorithm for separately assessing the three feature selectors. ADC entropy was the parameter that made the greatest contribution to the discrimination of TERT mutations.ConclusionsRadiomics model generated by RFE and LDA mainly based on ADC entropy showed good performance in predicting TERT promoter mutations in GBM.

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Section: Introductionmentioning

confidence: 99%