Extensive peritumoral edema and brain-to-tumor interface MRI features enable prediction of brain invasion in meningioma: development and validation. Issue 2 (13th August 2020)

• Record Type: Journal Article
• Authors: Joo, Leehi; Park, Ji Eun; Park, Seo Young; Nam, Soo Jung; Kim, Young-Hoon; Kim, Jeong Hoon; Kim, Ho Sung
• Is Part Of: Neuro-oncology. Volume 23:Issue 2(2021)
• Page Start: 324
• Page End: 333
• Publication Date: 2020-08-13
• Subjects: meningioma -- brain invasion -- MRI -- radiomics -- machine learning; Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noaa190
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: 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 2012 and 2017. Feature selection was performed from 3222 radiomics features obtained in the 1 cm thickness tumor-to-brain interface region using least absolute shrinkage and selection operator. Peritumoral edema volume, age, sex, and selected radiomics features were used to construct a random forest classifier–based diagnostic model. The performance was evaluated using the areas under the curves (AUCs) of the receiver operating characteristic 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 < 0.001). The top 6 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.