====== Intracranial meningioma volume ======

[[Tumor volume]] plays a pivotal role in this decision-making process. For instance, large tumors can increase the intracranial pressure substantially, making surgery an optimal treatment choice. Besides, tumor volume is reported to correlate with histological aggressiveness; meningiomas with larger volumes are more often diagnosed as high-grade
((S. T. Magill, J. S. Young, R. Chae, M. K. Aghi, P. V. Theodosopoulos, and M. W. McDermott, “Relationship between tumor location, size, and WHO grade in meningioma,” Neurosurgical Focus, vol. 44, no. 4, p. E4, 2018.))
((A. Ressel, S. Fichte, M. Brodhun, S. K. Rosahl, and R. Gerlach, “WHO grade of intracranial meningiomas differs with respect to patient’s age, location, tumor size and peritumoral edema,” Journal of Neuro-Oncology, vol. 145, no. 2, pp. 277–286, 2019.)).
Considering the fact that more aggressive treatment should be considered for patients with high-grade meningiomas, tumor volume might serve as a good indicator in this respect. In addition, tumor volume is closely related to stereotactic radiosurgery (SRS)
((O. Bloch, G. Kaur, B. J. Jian, A. T. Parsa, and I. J. Barani, “Stereotactic radiosurgery for benign meningiomas,” Journal of neuro-oncology, vol. 107, no. 1, pp. 13–20, 2012.))
((A. Mansouri, D. Guha, G. Klironomos, S. Larjani, G. Zadeh, and D. Kondziolka, “Stereotactic radiosurgery for intracranial meningiomas,” Neurosurgery, vol. 76, no. 4, pp. 362–371, 2015.))
((M.-S. Han, W.-Y. Jang, K.-S. Moon et al., “Is fractionated gamma knife radiosurgery a safe and effective treatment approach for large-volume (>10 cm3) intracranial meningiomas?” World neurosurgery, vol. 99, pp. 477–483, 2017.))
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Dos Santos Silva Jet al. compared the [[accuracy]] of three volumetric methods in the radiological assessment of [[meningioma]]s: linear ([[ABC/2]]), [[planimetry]], and multiparametric machine learning-based semiautomated [[voxel-based morphometry]] (VBM), and to investigate the relevance of tumor shape in volumetric error.

They 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.

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.

Semiautomated [[voxel-based morphometry]] (VBM) 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
((Dos Santos Silva J, Schreiner CA, de Lima L, Brigido CEPL, Wilson CD, McVeigh L, Acchiardo J, Landeiro JA, Acioly MA, Cohen-Gadol A. Volumetric measurement of intracranial meningiomas: a comparison between linear, planimetric, and machine learning with multiparametric voxel-based morphometry methods. J Neurooncol. 2022 Sep 5. doi: 10.1007/s11060-022-04127-z. Epub ahead of print. PMID: 36058985.))