Radiomics approach for prediction of recurrence in skull base meningiomas

• Published in: Neuroradiology 2019-12, Vol.61 (12), p.1355-1364
• Main Authors: Zhang, Yang, Chen, Jeon-Hor, Chen, Tai-Yuan, Lim, Sher-Wei, Wu, Te-Chang, Kuo, Yu-Ting, Ko, Ching-Chung, Su, Min-Ying
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Brain cancer; Contrast Media; Decision Trees; Diagnostic Neuroradiology; Disease Progression; Feature extraction; Female; Histograms; Humans; Image Interpretation, Computer-Assisted; Imaging; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Mathematical models; Medical imaging; Medicine; Medicine & Public Health; Meningioma - diagnostic imaging; Meningioma - pathology; Meningioma - surgery; Middle Aged; Model accuracy; Neoplasm Grading; Neoplasm Recurrence, Local - diagnostic imaging; Neoplasm Recurrence, Local - pathology; Neoplasm Recurrence, Local - surgery; Neurology; Neuroradiology; Neurosciences; Neurosurgery; Parameters; Patients; Predictive Value of Tests; Radiology; Radiomics; Retrospective Studies; Skull; Skull Base Neoplasms - diagnostic imaging; Skull Base Neoplasms - pathology; Skull Base Neoplasms - surgery; Tumors
• ISSN: 0028-3940; 1432-1920
• DOI: 10.1007/s00234-019-02259-0

Purpose A subset of skull base meningiomas (SBM) may show early progression/recurrence (P/R) as a result of incomplete resection. The purpose of this study is the implementation of MR radiomics to predict P/R in SBM. Methods From October 2006 to December 2017, 60 patients diagnosed with pathologically confirmed SBM (WHO grade I, 56; grade II, 3; grade III, 1) were included in this study. Preoperative MRI including T2WI, diffusion-weighted imaging (DWI), and contrast-enhanced T1WI were analyzed. On each imaging modality, 13 histogram parameters and 20 textural gray level co-occurrence matrix (GLCM) features were extracted. Random forest algorithms were utilized to evaluate the importance of these parameters, and the most significant three parameters were selected to build a decision tree for prediction of P/R in SBM. Furthermore, ADC values obtained from manually placed ROI in tumor were also used to predict P/R in SBM for comparison. Results Gross-total resection (Simpson Grades I–III) was performed in 33 (33/60, 55%) patients, and 27 patients received subtotal resection. Twenty-one patients had P/R (21/60, 35%) after a postoperative follow-up period of at least 12 months. The three most significant parameters included in the final radiomics model were T1 max probability, T1 cluster shade, and ADC correlation. In the radiomics model, the accuracy for prediction of P/R was 90%; by comparison, the accuracy was 83% using ADC values measured from manually placed tumor ROI. Conclusions The results show that the radiomics approach in preoperative MRI offer objective and valuable clinical information for treatment planning in SBM.