Radiomic features of magnetic resonance images as novel preoperative predictive factors of bone invasion in meningiomas. Issue 132 (November 2020)

• Record Type: Journal Article
• Authors: Zhang, Jing; Sun, Jianqing; Han, Tao; Zhao, Zhiyong; Cao, Yuntai; Zhang, Guojin; Zhou, Junlin
• Is Part Of: European journal of radiology. Issue 132(2020)
• Publication Date: 2020-11
• Subjects: Meningioma -- Bone invasion -- Radiomics -- Magnetic resonance images; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2020.109287
• Languages: English
• ISSNs: 0720-048X

Abstract

Abstract: Purpose: Bone invasion in meningiomas is a prognostic determinant, and a priori knowledge may alter surgical techniques. Here, we aim to predict bone invasion in meningiomas using radiomic signatures based on preoperative, contrast-enhanced T1-weighted (T1C) and T2-weighted (T2) magnetic resonance imaging (MRI). Methods: In this retrospective study, 490 patients diagnosed with meningiomas, including WHO grade I (448cases), grade II (38cases), and grade III (4cases), were enrolled and 213 out of 490 cases (43.5 %) had bone invasion. The patients were randomly divided into training (n = 343) and test (n = 147) datasets at a 7:3 ratio. For each patient, 1227 radiomic features were extracted from T1C and T2, respectively. Spearman's correlation and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to select the most informative features. Subsequently, a 5-fold cross-validation was used to compare the performance of different classification algorithms, and logistic regression was chosen to predict the risk of bone invasion. Results: Eight radiomic features were selected from T1C and T2 respectively, and three models were built using radiomic features. The radiomic models derived from T1C alone or a combination of T1C and T2 had the best performance in predicting risk of bone invasion, with areas under the curve in the training dataset of 0.714 [95 % CI, 0.660−0.768] and 0.722 [95 % CI, 0.668−0.776] and in the test datasets of 0.715 [95 % CI, 0.632−0.798] and 0.713 [95 % CI, 0.628−0.798], respectively. Conclusions: The radiomic model may aid clinicians with preoperative prediction of bone invasion by meningiomas, which can help in predicting prognosis and devising surgical strategies.