English  |  正體中文  |  简体中文  |  Items with full text/Total items : 17776/20117 (88%)
Visitors : 12136839      Online Users : 227
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://ir.cnu.edu.tw/handle/310902800/32614


    標題: Radiomics approach for prediction of recurrence in skull base meningiomas
    作者: 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
    貢獻者: 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
    關鍵字: Meningioma
    Skull base
    Recurrence
    Radiomics
    MRI
    日期: 2019-12
    上傳時間: 2020-07-29 13:52:10 (UTC+8)
    出版者: SPRINGER
    摘要: 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.
    關聯: Neuroradiology, v.61, n.12, pp.1355-1364
    Appears in Collections:[通識教育中心] 期刊論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML222View/Open


    All items in CNU IR are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback