MRI radiomics in the prediction of the volumetric response in meningiomas after gamma knife radiosurgery.
Gamma knife
Machine learning
Meningioma
Outcome prediction
Radiomics
Radiosurgery
Journal
Journal of neuro-oncology
ISSN: 1573-7373
Titre abrégé: J Neurooncol
Pays: United States
ID NLM: 8309335
Informations de publication
Date de publication:
Sep 2022
Sep 2022
Historique:
received:
08
05
2022
accepted:
07
06
2022
pubmed:
18
6
2022
medline:
31
8
2022
entrez:
17
6
2022
Statut:
ppublish
Résumé
This report presents the first investigation of the radiomics value in predicting the meningioma volumetric response to gamma knife radiosurgery (GKRS). The retrospective study included 93 meningioma patients imaged by three Tesla MRI. Tumor morphology was quantified by calculating 337 shape, first- and second-order radiomic features from MRI obtained before GKRS. Analysis was performed on original 3D MR images and after their laplacian of gaussian (LoG), logarithm and exponential filtering. The prediction performance was evaluated by Pearson correlation, linear regression and ROC analysis, with meningioma volume change per month as the outcome. Sixty calculated features significantly correlated with the outcome. The feature selection based on LASSO and multivariate regression started from all available 337 radiomic and 12 non-radiomic features. It selected LoG-sigma-1-0-mm-3D_firstorder_InterquartileRange and logarithm_ngtdm_Busyness as the predictively most robust and non-redundant features. The radiomic score based on these two features produced an AUC = 0.81. Adding the non-radiomic karnofsky performance status (KPS) to the score has increased the AUC to 0.88. Low values of the radiomic score defined a homogeneous subgroup of 50 patients with consistent absence (0%) of tumor progression. This is the first report of a strong association between MRI radiomic features and volumetric meningioma response to radiosurgery. The clinical importance of the early and reliable prediction of meningioma responsiveness to radiosurgery is based on its potential to aid individualized therapy decision making.
Identifiants
pubmed: 35715668
doi: 10.1007/s11060-022-04063-y
pii: 10.1007/s11060-022-04063-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
281-291Subventions
Organisme : This work was supported by the University Instituto Tecnologico de Santo Domingo (INTEC), Dominican Republic
ID : CBA-221024-2020-P-1.
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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