Radiomics approach for prediction of recurrence in skull base meningiomas

doi:10.1007/s00234-019-02259-0

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Please use this identifier to cite or link to this item: https://ir.cnu.edu.tw/handle/310902800/32614

Title:	Radiomics approach for prediction of recurrence in skull base meningiomas
Authors:	Zhang, Yang Chen, Jeon-Hor Chen, Tai-Yuan Lim, Sher-Wei Wu, Te-Chang Kuo, Yu-Ting Ching-Chung Ko(柯景中) Su, Min-Ying
Contributors:	Univ Calif Irvine, Dept Radiol Sci I Shou Univ, E DA Hosp, E DA Canc Hosp, Dept Radiol Chi Mei Med Ctr, Dept Med Imaging Chang Jung Christian Univ, Grad Inst Med Sci Chi Mei Med Ctr, Dept Neurosurg Min Hwei Coll Hlth Care Management, Dept Nursing Natl Yang Ming Univ, Dept Biomed Imaging & Radiol Sci Kaohsiung Med Univ Hosp, Dept Med Imaging Chia Nan Univ Pharm & Sci, Ctr Gen Educ
Keywords:	Meningioma Skull base Recurrence Radiomics MRI
Date:	2019-12
Issue Date:	2020-07-29 13:52:10 (UTC+8)
Publisher:	SPRINGER
Abstract:	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.
Relation:	Neuroradiology, v.61, n.12, pp.1355-1364
Appears in Collections:	[The Center For General Education] Periodical Articles

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