Cargando…

MRI radiomic features of peritumoral edema may predict the recurrence sites of glioblastoma multiforme

BACKGROUND AND PURPOSE: As one of the most aggressive malignant tumor in the central nervous system, the main cause of poor outcome of glioblastoma (GBM) is recurrence, a non-invasive method which can predict the area of recurrence pre-operation is necessary.To investigate whether there is radiologi...

Descripción completa

Detalles Bibliográficos
Autores principales: Long, Hao, Zhang, Ping, Bi, Yuewei, Yang, Chen, Wu, Manfeng, He, Dian, Huang, Shaozhuo, Yang, Kaijun, Qi, Songtao, Wang, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9845721/
https://www.ncbi.nlm.nih.gov/pubmed/36686829
http://dx.doi.org/10.3389/fonc.2022.1042498
_version_ 1784870973635297280
author Long, Hao
Zhang, Ping
Bi, Yuewei
Yang, Chen
Wu, Manfeng
He, Dian
Huang, Shaozhuo
Yang, Kaijun
Qi, Songtao
Wang, Jun
author_facet Long, Hao
Zhang, Ping
Bi, Yuewei
Yang, Chen
Wu, Manfeng
He, Dian
Huang, Shaozhuo
Yang, Kaijun
Qi, Songtao
Wang, Jun
author_sort Long, Hao
collection PubMed
description BACKGROUND AND PURPOSE: As one of the most aggressive malignant tumor in the central nervous system, the main cause of poor outcome of glioblastoma (GBM) is recurrence, a non-invasive method which can predict the area of recurrence pre-operation is necessary.To investigate whether there is radiological heterogeneity within peritumoral edema and identify the reproducible radiomic features predictive of the sites of recurrence of glioblastoma(GBM), which may be of value to optimize patients’ management. MATERIALS AND METHODS: The clinical information and MR images (contrast-enhanced T1 weighted and FLAIR sequences) of 22 patients who have been histologically proven glioblastoma, were retrospectively evaluated. Kaplan-Meier methods was used for survival analysis. Oedematous regions were manually segmented by an expert into recurrence region, non-recurrence region. A set of 94 radiomic features were obtained from each region using the function of analyzing MR image of 3D slicer. Paired t test was performed to identify the features existing significant difference. Subsequently, the data of two patients from TCGA database was used to evaluate whether these features have clinical value. RESULTS: Ten features with significant differences between the recurrence and non-recurrence subregions were identified and verified on two individual patients from the TCGA database with pathologically confirmed diagnosis of GBM. CONCLUSIONS: Our results suggested that heterogeneity does exist in peritumoral edema, indicating that the radiomic features of peritumoral edema from routine MR images can be utilized to predict the sites of GBM recurrence. Our findings may further guide the surgical treatment strategy for GBM.
format Online
Article
Text
id pubmed-9845721
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-98457212023-01-19 MRI radiomic features of peritumoral edema may predict the recurrence sites of glioblastoma multiforme Long, Hao Zhang, Ping Bi, Yuewei Yang, Chen Wu, Manfeng He, Dian Huang, Shaozhuo Yang, Kaijun Qi, Songtao Wang, Jun Front Oncol Oncology BACKGROUND AND PURPOSE: As one of the most aggressive malignant tumor in the central nervous system, the main cause of poor outcome of glioblastoma (GBM) is recurrence, a non-invasive method which can predict the area of recurrence pre-operation is necessary.To investigate whether there is radiological heterogeneity within peritumoral edema and identify the reproducible radiomic features predictive of the sites of recurrence of glioblastoma(GBM), which may be of value to optimize patients’ management. MATERIALS AND METHODS: The clinical information and MR images (contrast-enhanced T1 weighted and FLAIR sequences) of 22 patients who have been histologically proven glioblastoma, were retrospectively evaluated. Kaplan-Meier methods was used for survival analysis. Oedematous regions were manually segmented by an expert into recurrence region, non-recurrence region. A set of 94 radiomic features were obtained from each region using the function of analyzing MR image of 3D slicer. Paired t test was performed to identify the features existing significant difference. Subsequently, the data of two patients from TCGA database was used to evaluate whether these features have clinical value. RESULTS: Ten features with significant differences between the recurrence and non-recurrence subregions were identified and verified on two individual patients from the TCGA database with pathologically confirmed diagnosis of GBM. CONCLUSIONS: Our results suggested that heterogeneity does exist in peritumoral edema, indicating that the radiomic features of peritumoral edema from routine MR images can be utilized to predict the sites of GBM recurrence. Our findings may further guide the surgical treatment strategy for GBM. Frontiers Media S.A. 2023-01-04 /pmc/articles/PMC9845721/ /pubmed/36686829 http://dx.doi.org/10.3389/fonc.2022.1042498 Text en Copyright © 2023 Long, Zhang, Bi, Yang, Wu, He, Huang, Yang, Qi and Wang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Long, Hao
Zhang, Ping
Bi, Yuewei
Yang, Chen
Wu, Manfeng
He, Dian
Huang, Shaozhuo
Yang, Kaijun
Qi, Songtao
Wang, Jun
MRI radiomic features of peritumoral edema may predict the recurrence sites of glioblastoma multiforme
title MRI radiomic features of peritumoral edema may predict the recurrence sites of glioblastoma multiforme
title_full MRI radiomic features of peritumoral edema may predict the recurrence sites of glioblastoma multiforme
title_fullStr MRI radiomic features of peritumoral edema may predict the recurrence sites of glioblastoma multiforme
title_full_unstemmed MRI radiomic features of peritumoral edema may predict the recurrence sites of glioblastoma multiforme
title_short MRI radiomic features of peritumoral edema may predict the recurrence sites of glioblastoma multiforme
title_sort mri radiomic features of peritumoral edema may predict the recurrence sites of glioblastoma multiforme
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9845721/
https://www.ncbi.nlm.nih.gov/pubmed/36686829
http://dx.doi.org/10.3389/fonc.2022.1042498
work_keys_str_mv AT longhao mriradiomicfeaturesofperitumoraledemamaypredicttherecurrencesitesofglioblastomamultiforme
AT zhangping mriradiomicfeaturesofperitumoraledemamaypredicttherecurrencesitesofglioblastomamultiforme
AT biyuewei mriradiomicfeaturesofperitumoraledemamaypredicttherecurrencesitesofglioblastomamultiforme
AT yangchen mriradiomicfeaturesofperitumoraledemamaypredicttherecurrencesitesofglioblastomamultiforme
AT wumanfeng mriradiomicfeaturesofperitumoraledemamaypredicttherecurrencesitesofglioblastomamultiforme
AT hedian mriradiomicfeaturesofperitumoraledemamaypredicttherecurrencesitesofglioblastomamultiforme
AT huangshaozhuo mriradiomicfeaturesofperitumoraledemamaypredicttherecurrencesitesofglioblastomamultiforme
AT yangkaijun mriradiomicfeaturesofperitumoraledemamaypredicttherecurrencesitesofglioblastomamultiforme
AT qisongtao mriradiomicfeaturesofperitumoraledemamaypredicttherecurrencesitesofglioblastomamultiforme
AT wangjun mriradiomicfeaturesofperitumoraledemamaypredicttherecurrencesitesofglioblastomamultiforme