Background Brain invasion by meningioma is a stand-alone criterion for tumor atypia in the 2016 World Health Organization classification, but no imaging parameter has yet been shown to be sufficient for predicting it. The aim of this study was to develop and validate an MRI-based radiomics model from the brain-to-tumor interface to predict brain invasion by meningioma. Methods Preoperative T2-weighted and contrast-enhanced T1-weighted imaging data were obtained from 454 patients (88 patients with brain invasion) between 2015 and 2017. Feature selection was performed from 3236 radiomics features obtained in the 1cm-thickness tumor-to-brain interface region using least absolute shrinkage and selection operator. Peritumoral edema volume, age, gender, and selected radiomics features were used to construct a random forest classifier-based diagnostic model. The performance was evaluated using the area under the receiver-operating-characteristic curves (AUCs) in an independent cohort of 150 patients (29 patients with brain invasion) between 2018 and 2019. Results Volume of peritumoral edema was an independent predictor of brain invasion (P<.001). The top six interface radiomics features plus the volume of peritumoral edema were selected for model construction. The combined model showed the highest performance for prediction of brain invasion in the training (AUC 0.97,95%CI:0.95–0.98) and validation sets (AUC 0.91,95%CI:0.84–0.98), and improved diagnostic performance over volume of peritumoral edema only (AUC 0.76,95% CI: 0.66–0.86). Conclusion An imaging-based model combining interface radiomics and peritumoral edema can help to predict brain invasion by meningioma and improve the diagnostic performance of known clinical and imaging parameters.

From May 2015 to June 2016, data on 296 patients undergoing 1.5-Tesla MRI for symptoms of acute ischemic stroke were retrospectively collected. Conventional, echo-planar imaging (EPI) and echo train length (ETL)-T2-FLAIR were simultaneously obtained in 118 patients (first group), and conventional, ETL-, and repetition time (TR)-T2-FLAIR were simultaneously obtained in 178 patients (second group). A total of 595 radiomics features were extracted from one region-of-interest (ROI) reflecting the acute and chronic ischemic hyperintensity, and concordance correlation coefficients (CCC) of the radiomics features were calculated between the fast scanned and conventional T2-FLAIR for paired patients (1st group and 2nd group). Stabilities of the radiomics features were compared with the proportions of features with a CCC higher than 0.85, which were considered to be stable in the fast scanned T2-FLAIR. EPI-T2-FLAIR showed higher proportions of stable features than ETL-T2-FLAIR, and TR-T2-FLAIR also showed higher proportions of stable features than ETL-T2-FLAIR, both in acute and chronic ischemic hyperintensities of whole- and intersection masks (p < .002). Radiomics features in fast scanned T2-FLAIR showed variable stabilities according to the sequences compared with conventional T2-FLAIR. Therefore, radiomics features may be used cautiously in applications for feature analysis as their stability and robustness can be variable.