Diagnosis of Invasive Meningioma Based on Brain-Tumor Interface Radiomics Features on Brain MR Images: A Multicenter Study - Xiao, Dongdong; Zhao, Zhen; Liu, Jun; Wang, Xuan; Fu, Peng; Le Grange, Jehane Michael; Wang, Jihua; Guo, Xuebing; Zhao, Hongyang; Shi, Jiawei; Yan, Pengfei; Jiang, Xiaobing (Frontiers in Oncology, 2021)
Background: Meningioma invasion can be pre-operatively recognized by radiomic features, which significantly contributes to treatment decision-making. Here, we aimed to evaluate the comparative performance of radiomic signatures derived from varying regions of interests(ROIs) in predicting BI and ascertaining the optimal width of the peritumoral regions needed for accurate analysis. Methods: 505 patients from Wuhan Union Hospital (internal cohort) and 214 cases from Taihe Hospital (external validation cohort) that pathologically diagnosed as meningioma were included in our study. Feature selection was performed from 1015 radiomic features respectively obtained from nine different ROIs [brain-tumor interface(BTI)2-5mm; whole tumor; the amalgamation of the two regions] on contrast-enhancement T1-weighted imaging using least absolute shrinkage and selection operator and random forest. Principal component analysis with varimax-rotation was employed for feature reduction. Receiver operator curve was utilized for assessing discrimination of the classifier. Furthermore, clinical index was used to detect the predictive power. Results: Model obtained from BTI4mm ROI has the maximum AUC in the training set (0.891 (0.85, 0.932)), internal validation set (0.851 (0.743, 0.96)), and external validation set (0.881 (0.833, 0.928)) and displayed statistically significant results between nine radiomics models. The most predictive radiomic features are almost entirely generated from GLCM and GLDM statistics. The addition of PEV to radiomic features (BTI4mm) enhanced model discrimination of invasive meningiomas. Conclusions: The combined model (radiomic classifier with BTI4mm ROI + PEV) had greater diagnostic performance than other models, and may contribute to clinical application for meningioma patients.