Predicting Histologic Grade of Meningiomas Using a Combined Model of Radiomic and Clinical Imaging Features from Preoperative MRI.
grading
machine learning
magnetic resonance imaging
meningioma
radiomic features
Journal
Biomedicines
ISSN: 2227-9059
Titre abrégé: Biomedicines
Pays: Switzerland
ID NLM: 101691304
Informations de publication
Date de publication:
10 Dec 2023
10 Dec 2023
Historique:
received:
20
11
2023
revised:
04
12
2023
accepted:
09
12
2023
medline:
23
12
2023
pubmed:
23
12
2023
entrez:
23
12
2023
Statut:
epublish
Résumé
Meningiomas are common primary brain tumors, and their accurate preoperative grading is crucial for treatment planning. This study aimed to evaluate the value of radiomics and clinical imaging features in predicting the histologic grade of meningiomas from preoperative MRI. We retrospectively reviewed patients with intracranial meningiomas from two hospitals. Preoperative MRIs were analyzed for tumor and edema volumes, enhancement patterns, margins, and tumor-brain interfaces. Radiomics features were extracted, and machine learning models were employed to predict meningioma grades. A total of 212 patients were included. In the training group (Hospital 1), significant differences were observed between low-grade and high-grade meningiomas in terms of tumor volume (
Identifiants
pubmed: 38137489
pii: biomedicines11123268
doi: 10.3390/biomedicines11123268
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development
ID : IITP-2023-RS-2023-00256629
Organisme : Korea government (MSIT) and grants from the Ministry of Education, Republic of Korea
ID : NRF-2022R1I1A3072856
Organisme : Chonnam National University Hospital Biomedical Research Institute
ID : BCRI22037
Organisme : the Ministry of Science and ICT through the National Research Foundation of Korea
ID : 2021R1A2C1005765, 2021H1D3A2A02037997, 2022R1A2C1003266