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To 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 con rmed diagnosis of meningioma and a volumetric acquired cranial magnetic resonance imaging.After tumor segmentation, images underwent automated computation of shape properties such as sphericity, roundness, atness, and elongation. ResultsSixty-nine patients (85 tumors) were included. Tumor volumes were signi cantly different using linear (13.82 cm³ [range: 0.13-163.74 cm³]), planimetric (11.66 cm³ [range: 0.17-196.2 cm³]) and VBM methods (10.24 cm³ [range: 0.17-190.32 cm³]) (p < 0.001). Median volume and percentage errors between the planimetric and linear methods and the VBM method were 1.08 cm³ and 11.61%, and 0.23 cm³ and 5.5%, respectively. Planimetry and linear methods overestimated the actual volume in 79% and 63% of the patients, respectively. Correlation studies showed excellent reliability and volumetric agreement between manual-and computer-based methods. Larger and atter tumors had greater accuracy on planimetry, whereas less rounded tumors contributed negatively to the accuracy of the linear method. ConclusionSemiautomated VBM volumetry for meningiomas is not in uenced 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. methods. VBM could replace manually-based volumetric assessment in the future, especially for research purposes, and could have a complementary role for clinical purposes.

Purpose To 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. Methods Retrospective imaging database analysis at the authors’ institutions. We included patients with a confirmed diagnosis of meningioma and a volumetric acquired cranial magnetic resonance imaging. After tumor segmentation, images underwent automated computation of shape properties such as sphericity, roundness, flatness, and elongation. Results Sixty-nine patients (85 tumors) were included. Tumor volumes were significantly different using linear (13.82 cm³ [range: 0.13–163.74 cm³]), planimetric (11.66 cm³ [range: 0.17–196.2 cm³]) and VBM methods (10.24 cm³ [range: 0.17–190.32 cm³]) (p < 0.001). Median volume and percentage errors between the planimetric and linear methods and the VBM method were 1.08 cm³ and 11.61%, and 0.23 cm³ and 5.5%, respectively. Planimetry and linear methods overestimated the actual volume in 79% and 63% of the patients, respectively. Correlation studies showed excellent reliability and volumetric agreement between manual- and computer-based methods. Larger and flatter tumors had greater accuracy on planimetry, whereas less rounded tumors contributed negatively to the accuracy of the linear method. Conclusion Semiautomated 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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