ArticleJournal of neuro-oncology2023
Volumetric measurement of intracranial meningiomas: a comparison between linear, planimetric, and machine learning with multiparametric voxel-based morphometry methods.
Article in Journal of neuro-oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Discordance in tumour response assessment for gastric cancer after neoadjuvant chemotherapy using different methods.Abdominal radiology (New York) · 2026Article
- Deficiency of SMARCB1 drives an immunosuppressive microenvironment in meningioma.Acta neuropathologica communications · 2026Article
- A scoping review of artificial intelligence applications in meningioma from image analysis to prognostic prediction.Discover oncology · 2026Review
- Relationship Between Radiological Features of Primary Empty or Primary Partial Empty Sella and Pituitary Hormone Levels.Biomedicines · 2025Article
- Performance Test of a Well-Trained Model for Meningioma Segmentation in Health Care Centers: Secondary Analysis Based on Four Retrospective Multicenter Data Sets.Journal of medical Internet research · 2023Article
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo compare the accuracy of three volumetric methods in the radiological assessment of meningiomas: linear (ABC/2), planimetric, and multiparametric machine learning-based semiautomated voxel-based morphometry (VBM), and to investigate the relevance of tumor shape in volumetric error.
methodsRetrospective imaging database analysis at the authors' institutions. We included patients with a confirmed diagnosis of meningioma and preoperative cranial magnetic resonance imaging eligible for volumetric analyses. After tumor segmentation, images underwent automated computation of shape properties such as sphericity, roundness, flatness, and elongation.
resultsSixty-nine patients (85 tumors) were included. Tumor volumes were significantly different using linear (13.82 cm
conclusionSemiautomated VBM volumetry for meningiomas is not influenced by tumor shape properties, whereas planimetry and linear methods tend to overestimate tumor volume. Furthermore, it is necessary to consider tumor roundness prior to linear measurement so as to choose the most appropriate method for each patient on an individual basis.
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