KLI

Extensive peritumoral edema and brain-to-tumor interface MRI features enable prediction of brain invasion in meningioma: Development and validation

Metadata Downloads
Abstract
Background: Brain invasion by meningioma is a stand-alone criterion for tumor atypia in the 2016 World Health Organization classification, but no imaging parameter has yet been shown to be sufficient for predicting it. The aim of this study was to develop and validate an MRI-based radiomics model from the brain-to-tumor interface to predict brain invasion by meningioma. Methods: Preoperative T2-weighted and contrast-enhanced T1-weighted imaging data were obtained from 454 patients (88 patients with brain invasion) between 2012 and 2017. Feature selection was performed from 3222 radiomics features obtained in the 1 cm thickness tumor-to-brain interface region using least absolute shrinkage and selection operator. Peritumoral edema volume, age, sex, and selected radiomics features were used to construct a random forest classifier-based diagnostic model. The performance was evaluated using the areas under the curves (AUCs) of the receiver operating characteristic in an independent cohort of 150 patients (29 patients with brain invasion) between 2018 and 2019. Results: Volume of peritumoral edema was an independent predictor of brain invasion (P < 0.001). The top 6 interface radiomics features plus the volume of peritumoral edema were selected for model construction. The combined model showed the highest performance for prediction of brain invasion in the training (AUC 0.97; 95% CI: 0.95-0.98) and validation sets (AUC 0.91; 95% CI: 0.84-0.98), and improved diagnostic performance over volume of peritumoral edema only (AUC 0.76; 95% CI: 0.66-0.86). Conclusion: An imaging-based model combining interface radiomics and peritumoral edema can help to predict brain invasion by meningioma and improve the diagnostic performance of known clinical and imaging parameters.
Author(s)
주리히박지은박서영남수정김영훈김정훈김호성
Issued Date
2021
Type
Article
Keyword
MRIbrain invasionmachine learningmeningiomaradiomics.
DOI
10.1093/neuonc/noaa190
URI
https://oak.ulsan.ac.kr/handle/2021.oak/7805
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_proquest_miscellaneous_2434061094&amp;context=PC&amp;vid=ULSAN&amp;lang=ko_KR&amp;search_scope=default_scope&amp;adaptor=primo_central_multiple_fe&amp;tab=default_tab&amp;query=any,contains,Extensive%20peritumoral%20edema%20and%20brain-to-tumor%20interface%20MRI%20features%20enable%20prediction%20of%20brain%20invasion%20in%20meningioma:%20Development%20and%20validation&amp;offset=0&amp;pcAvailability=true
Publisher
NEURO-ONCOLOGY
Location
영국
Language
영어
ISSN
1522-8517
Citation Volume
23
Citation Number
2
Citation Start Page
324
Citation End Page
333
Appears in Collections:
Medicine > Medicine
Authorize & License
  • Authorize공개
Files in This Item:
  • There are no files associated with this item.

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.