Use of Radiomics Models in Preoperative Grading of Cerebral Gliomas and Comparison with Three-dimensional Arterial Spin Labelling.
Clin Oncol (R Coll Radiol)
; 35(11): 726-735, 2023 Nov.
Article
em En
| MEDLINE
| ID: mdl-37598093
ABSTRACT
AIMS:
To build machine learning-based radiomics models to discriminate between high- (HGGs) and low-grade gliomas (LGGs) and to compare the effectiveness of three-dimensional arterial spin labelling (3D-ASL) to evaluate which is a better method. MATERIALS ANDMETHODS:
We retrospectively analysed the magnetic resonance imaging T1WI-enhanced images of 105 patients with gliomas that were pathologically confirmed in our hospital. We divided the patients into a training group and a verification group at a ratio of 82; 200 patients from the Brain Tumour Segmentation Challenge 2020 were selected as the test group for image segmentation, feature extraction and screening. We constructed models using multilayer perceptron (MLP), support vector machine, random forest and logistic regression and evaluated their predictive performance. We obtained the mean maximum relative cerebral blood flow (rCBFmax) value from 3D-ASL of 105 patients from the hospital to evaluate its efficacy in discriminating between HGGs and LGGs.RESULTS:
In machine learning, the MLP classifier model exhibited the best performance in discriminating between HGGs and LGGs; the areas under the curve obtained by MLP and rCBFmax were 0.968 versus 0.815 (verification group) and 0.981 versus 0.815 (test group), respectively. The machine learning-based MLP classifier model performed better in discriminating between HGGs and LGGs than 3D-ASL.CONCLUSION:
In our study, we found that machine learning-based radiomics models and 3D-ASL were valuable in discriminating between HGGs and LGGs and between them, the machine learning-based MLP model had better diagnostic performance.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Clin Oncol (R Coll Radiol)
Assunto da revista:
NEOPLASIAS
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China