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Multiparameter MRI-based radiomics nomogram for preoperative prediction of brain invasion in atypical meningioma:a multicentre study.
- Source :
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BMC medical imaging [BMC Med Imaging] 2024 Jun 05; Vol. 24 (1), pp. 134. Date of Electronic Publication: 2024 Jun 05. - Publication Year :
- 2024
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Abstract
- Objective: To develop a nomogram based on tumor and peritumoral edema (PE) radiomics features extracted from preoperative multiparameter MRI for predicting brain invasion (BI) in atypical meningioma (AM).<br />Methods: In this retrospective study, according to the 2021 WHO classification criteria, a total of 469 patients with pathologically confirmed AM from three medical centres were enrolled and divided into training (n = 273), internal validation (n = 117) and external validation (n = 79) cohorts. BI was diagnosed based on the histopathological examination. Preoperative contrast-enhanced T1-weighted MR images (T1C) and T2-weighted MR images (T2) for extracting meningioma features and T2-fluid attenuated inversion recovery (FLAIR) sequences for extracting meningioma and PE features were obtained. The multiple logistic regression was applied to develop separate multiparameter radiomics models for comparison. A nomogram was developed by combining radiomics features and clinical risk factors, and the clinical usefulness of the nomogram was verified using decision curve analysis.<br />Results: Among the clinical factors, PE volume and PE/tumor volume ratio are the risk of BI in AM. The combined nomogram based on multiparameter MRI radiomics features of meningioma and PE and clinical indicators achieved the best performance in predicting BI in AM, with area under the curve values of 0.862 (95% CI, 0.819-0.905) in the training cohort, 0.834 (95% CI, 0.780-0.908) in the internal validation cohort and 0.867 (95% CI, 0.785-0.950) in the external validation cohort, respectively.<br />Conclusions: The nomogram based on tumor and PE radiomics features extracted from preoperative multiparameter MRI and clinical factors can predict the risk of BI in patients with AM.<br /> (© 2024. The Author(s).)
- Subjects :
- Humans
Female
Male
Middle Aged
Retrospective Studies
Neoplasm Invasiveness
Adult
Aged
Multiparametric Magnetic Resonance Imaging methods
Brain Neoplasms diagnostic imaging
Brain Neoplasms pathology
Brain Neoplasms surgery
Magnetic Resonance Imaging methods
Radiomics
Meningioma diagnostic imaging
Meningioma pathology
Meningioma surgery
Nomograms
Meningeal Neoplasms diagnostic imaging
Meningeal Neoplasms pathology
Meningeal Neoplasms surgery
Subjects
Details
- Language :
- English
- ISSN :
- 1471-2342
- Volume :
- 24
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- BMC medical imaging
- Publication Type :
- Academic Journal
- Accession number :
- 38840054
- Full Text :
- https://doi.org/10.1186/s12880-024-01294-5