Radiomics approach for prediction of recurrence in skull base meningiomas.
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 pathologic...
| Publicado en: | Neuroradiology Vol. 61; no. 12; pp. 1355 - 1365 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=139600370&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139600370 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Dec2019 vid: 61 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 139600370 139600370 144156077 139600370 10.1007/s00234-019-02259-0 139600370 ppf: 1355 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Radiomics approach for prediction of recurrence in skull base meningiomas. aug: au: Zhang, Yang Chen, Jeon-Hor Chen, Tai-Yuan Lim, Sher-Wei Wu, Te-Chang Kuo, Yu-Ting Ko, Ching-Chung Su, Min-Ying affil: Department of Radiological Sciences, University of California, Irvine, CA, USA sug: subj: Skull Neoplasms Diagnosis Meningioma Diagnosis Magnetic Resonance Imaging Methods Neoplasm Recurrence, Local Diagnosis Disease Progression Diagnosis Human Neoplasm Grading Preoperative Period Algorithms Decision Trees Meningioma Surgery Skull Neoplasms Surgery Postoperative Period Probability Decision Making, Clinical ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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