Voxel‐based clustered imaging by multiparameter diffusion tensor images for predicting the grade and proliferative activity of meningioma

Abramovich, C. M., & Prayson, R. A. (1998). MIB-1 labeling indices in benign,

aggressive, and malignant meningiomas: A study of 90 tumors. Human

Pathology, 29, 1420–1427. https://doi.org/10.1016/S0046-8177(98)

90010-7

Abramovich, C. M., & Prayson, R. A. (1999). Histopathologic features and

MIB-1 labeling indices in recurrent and nonrecurrent meningiomas.

Archives of Pathology & Laboratory Medicine, 123, 793–800. https://doi.

org/10.5858/1999-123-0793-HFAMLI

Abry, E., Thomassen, I. Ø., Salvesen, Ø. O., & Torp, S. H. (2010). The significance of Ki-67/MIB-1 labeling index in human meningiomas: A literature

study. Pathology, Research and Practice, 206, 810–815. https://doi.org/10.

1016/j.prp.2010.09.002

Alexiou, G. A., Zikou, A., Tsiouris, S., Goussia, A., Kosta, P., Papadopoulos,

A., Voulgaris, S., Kyritsis, A. P., Fotopoulos, A. D., & Argyropoulou, M. I.

(2014). Correlation of diffusion tensor, dynamic susceptibility contrast

MRI and (99m)Tc-Tetrofosmin brain SPECT with tumour grade and Ki67 immunohistochemistry in glioma. Clinical Neurology and Neurosurgery,

116, 41–45. https://doi.org/10.1016/j.clineuro.2013.11.003

Andersson, J. L. R., Skare, S., & Ashburner, J. (2003). How to correct susceptibility distortions in spin-echo echo-planar images: Application to

diffusion tensor imaging. Neuroimage, 20, 870–888. https://doi.org/10.

1016/S1053-8119(03)00336-7

Basser, P. J., Mattiello, J., & Lebihan, D. (1994). Estimation of the effective self-diffusion tensor from the NMR spin echo. Journal of Magnetic

Resonance Series B, 103, 247–254. https://doi.org/10.1006/jmrb.1994.

1037

Bastin, M. E., Sinha, S., Whittle, I. R., & Wardlaw, J. M. (2002). Measurements of water diffusion and T1 values in peritumoural oedematous

brain. Neuroreport, 13, 1335–1340. https://doi.org/10.1097/00001756200207190-00024

Bohra, H., Rathi, K. R., Dudani, S., Bohra, A., Vishwakarma, S., & Sahai, K.

(2016). The study of MIB-1 LI and CD 34 as a marker of proliferative

activity and angiogenesis in different grades of meningioma. Journal of

Clinical Diagnostic Research, 10, EC14–EC17.

Bulakbasi, N., Guvenc, I., Onguru, O., Erdogan, E., Tayfun, C., & Ucoz, T.

(2004). The added value of the apparent diffusion coefficient calculation to magnetic resonance imaging in the differentiation and grading

of malignant brain tumors. Journal of Computer Assisted Tomography, 28,

735–746. https://doi.org/10.1097/00004728-200411000-00003

De Belder, F. E., Oot, A. R., Van Hecke, W., Venstermans, C., Menovsky, T.,

Van Marck, V., Van Goethem, J., Van Den Hauwe, L., Vandekerckhove,

M., & Parizel, P. M. (2012). Diffusion tensor imaging provides an insight

into the microstructure of meningiomas, high-grade gliomas, and peritumoral edema. Journal of Computer Assisted Tomography, 36, 577–582.

https://doi.org/10.1097/RCT.0b013e318261e913

Demaerel, P., Wilms, G., Lammens, M., Marchal, G., Plets, C., Goffin, J.,

& Baert, A. L. (1991). Intracranial meningiomas: Correlation between

MR imaging and histology in fifty patients. Journal of Computer

Assisted Tomography, 15, 45–51. https://doi.org/10.1097/00004728199101000-00005

Duan, C., Li, N., Liu, X., Cui, J., Wang, G., & Xu, W. (2023). Performance

comparison of 2D and 3D MRI radiomics features in meningioma grade

prediction: A preliminary study. Frontiers in oncology, 13, 1157379.

https://doi.org/10.3389/fonc.2023.1157379

Durand, A., Labrousse, F., Jouvet, A., Bauchet, L., Kalamaridès, M., Menei,

P., Deruty, R., Moreau, J. J., Fèvre-Montange, M., & Guyotat, J. (2009).

WHO grade II and III meningiomas: A study of prognostic factors. Journal

of Neuro-Oncology, 95, 367–375. https://doi.org/10.1007/s11060-0099934-0

Filippi, C. G., Edgar, M. A., Ulug, A. M., Prowda, J. C., Heier, L. A., &

Zimmerman, R. D. (2001). Appearance of meningiomas on diffusionweighted images: Correlating diffusion constants with histopathologic

findings. Ajnr American Journal of Neuroradiology, 22, 65–72.

Gupta, A., Prabhu, S. M., Sureka, J., & Chacko, G. (2013). Role of diffusion

weighted MRI in differentiating typical from atypical meningiomas at 1.5

and 3T MRI. The Egyptian Journal of Radiology and Nuclear Medicine, 44,

635–640. https://doi.org/10.1016/j.ejrnm.2013.04.005

Hakyemez, B., Yıldırım, N., Gokalp, G., Erdogan, C., & Parlak, M. (2006). The

contribution of diffusion-weighted MR imaging to distinguishing typical from atypical meningiomas. Neuroradiology, 48, 513–520. https://doi.

org/10.1007/s00234-006-0094-z

Hanft, S., Canoll, P., & Bruce, J. N. (2010). A review of malignant meningiomas: Diagnosis, characteristics, and treatment. Journal of NeuroOncology, 99, 433–443. https://doi.org/10.1007/s11060-010-0348-9

Hsu, C.-C., Pai, C.-Y., Kao, H.-W., Hsueh, C.-J., Hsu, W.-L., & Lo, C.-P. (2010).

Do aggressive imaging features correlate with advanced histopathological grade in meningiomas? Journal of Clinical Neuroscience, 17, 584–587.

https://doi.org/10.1016/j.jocn.2009.09.018

Inano, R., Oishi, N., Kunieda, T., Arakawa, Y., Kikuchi, T., Fukuyama, H., &

Miyamoto, S. (2016). Visualization of heterogeneity and regional grading of gliomas by multiple features using magnetic resonance-based

clustered images. Scientific Reports, 6, 30344. https://doi.org/10.1038/

srep30344

Inano, R., Oishi, N., Kunieda, T., Arakawa, Y., Yamao, Y., Shibata, S., Kikuchi, T.,

Fukuyama, H., & Miyamoto, S. (2014). Voxel-based clustered imaging by

multiparameter diffusion tensor images for glioma grading. NeuroImage

Clinical, 5, 396–407. https://doi.org/10.1016/j.nicl.2014.08.001

Iuchi, T., Saeki, N., Osato, K., & Yamaura, A. (1999). Topographical analysis of

proliferating cells in meningiomas. Regional heterogeneity of the ability

of tumors to proliferate. Neurological Research, 21, 721–726.

Jezzard, P., & Balaban, R. S. (1995). Correction for geometric distortion

in echo planar images from B0 field variations. Magnetic Resonance in

Medicine, 34, 65–73. https://doi.org/10.1002/mrm.1910340111

Jolapara, M., Kesavadas, C., Radhakrishnan, V. V., Thomas, B., Gupta, A. K.,

Bodhey, N., Patro, S., Saini, J., George, U., & Sarma, P. S. (2010). Role

of diffusion tensor imaging in differentiating subtypes of meningiomas.

21579032, 2023, 10, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/brb3.3201 by Cochrane Japan, Wiley Online Library on [10/03/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

13 of 15

Journal of Neuroradiology Journal De Neuroradiologie, 37, 277–283. https://

doi.org/10.1016/j.neurad.2010.03.001

Kim, B.-W., Kim, M.-S., Kim, S.-W., Chang, C.-H., & Kim, O.-L. (2011). Peritumoral brain edema in meningiomas : Correlation of radiologic and

pathologic features. Journal of Korean Neurosurgical Society, 49, 26–30.

https://doi.org/10.3340/jkns.2011.49.1.26

Kim, J. W., Kim, D. G., Paek, S. H., Chung, H.-T., Myung, J. K., Park, S.-H., Kim, Y.

H., Han, J. H., Yang, S.-Y., Park, C.-K., & Jung, H.-W. (2012). Radiosurgery

for atypical and anaplastic meningiomas: Histopathological predictors

of local tumor control. Stereotactic and Functional Neurosurgery, 90,

316–324. https://doi.org/10.1159/000338253

Kono, K., Inoue, Y., Nakayama, K., Shakudo, M., Morino, M., Ohata, K.,

Wakasa, K., & Yamada, R. (2001). The role of diffusion-weighted imaging

in patients with brain tumors. Ajnr American Journal of Neuroradiology, 22,

1081–1088.

Krabbe, K., Gideon, P., Wagn, P., Hansen, U., Thomsen, C., & Madsen, F.

(1997). MR diffusion imaging of human intracranial tumours. Neuroradiology, 39, 483–489. https://doi.org/10.1007/s002340050450

Louis, D. N., Ohgaki, H., Wiestler, O. D., Cavenee, W. K., Burger, P. C., Jouvet,

A., Scheithauer, B. W., & Kleihues, P. (2007). The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathologica,

114, 97–109. https://doi.org/10.1007/s00401-007-0243-4

Louis, D. N., Perry, A., Reifenberger, G., Von Deimling, A., Figarella-Branger,

D., Cavenee, W. K., Ohgaki, H., Wiestler, O. D., Kleihues, P., & Ellison, D.

W. (2016). The 2016 World Health Organization classification of tumors

of the central nervous system: A summary. Acta Neuropathologica, 131,

803–820. https://doi.org/10.1007/s00401-016-1545-1

Matsuno, A., Fujimaki, T., Sasaki, T., Nagashima, T., Ide, T., Asai, A., Matsuura,

R., Utsunomiya, H., & Kirino, T. (1996). Clinical and histopathological analysis of proliferative potentials of recurrent and non-recurrent

meningiomas. Acta Neuropathologica, 91, 504–510. https://doi.org/10.

1007/s004010050458

Nagar, V. A., Ye, J. R., Ng, W. H., Chan, Y. H., Hui, F., Lee, C. K., & Lim, C. C.

T. (2008). Diffusion-weighted MR imaging: Diagnosing atypical or malignant meningiomas and detecting tumor dedifferentiation. Ajnr American

Journal of Neuroradiology, 29, 1147–1152. https://doi.org/10.3174/ajnr.

A0996

Nakasu, S., Li, D. H., Okabe, H., Nakajima, M., & Matsuda, M. (2001). Significance of MIB-1 staining indices in meningiomas: Comparison of two

counting methods. American Journal of Surgical Pathology, 25, 472–478.

https://doi.org/10.1097/00000478-200104000-00006

Nakaya, K., Chernov, M., Kasuya, H., Izawa, M., Hayashi, M., Kato, K., Kubo,

O., Muragaki, Y., Iseki, H., Hori, T., Okada, Y., & Takakura, K. (2009).

Risk factors for regrowth of intracranial meningiomas after gamma

knife radiosurgery: Importance of the histopathological grade and MIB1 index. Minimally Invasive Neurosurgery, 52, 216–221. https://doi.org/10.

1055/s-0029-1243244

Ohta, M., Iwaki, T., Kitamoto, T., Takeshita, I., Tateishi, J., & Fukui,

M. (1994). MIB1 staining index and scoring of histologic features in meningioma. Indicators for the prediction of biologic

potential and postoperative management. Cancer, 74, 3176–3189.

https://doi.org/10.1002/1097-0142(19941215)74:12%3c3176::AIDCNCR2820741217%3e3.0.CO;2-N

Ostrom, Q. T., Gittleman, H., Farah, P., Ondracek, A., Chen, Y., Wolinsky,

Y., Stroup, N. E., Kruchko, C., & Barnholtz-Sloan, J. S. (2013). CBTRUS

statistical report: Primary brain and central nervous system tumors diagnosed in the United States in 2006–2010. Neuro-oncol, 15, (Suppl 2),

ii1–i56.

Park, Y. W., Oh, J., You, S. C., Han, K., Ahn, S. S., Choi, Y. S., Chang, J. H., Kim,

S. H., & Lee, S.-K. (2019). Radiomics and machine learning may accurately

predict the grade and histological subtype in meningiomas using conven-

14 of 15

tional and diffusion tensor imaging. European Radiology, 29, 4068–4076.

https://doi.org/10.1007/s00330-018-5830-3

Radeesri, K., & Lekhavat, V. (2023). The role of pre-operative MRI for prediction of high-grade intracranial meningioma: A retrospective study.

Asian Pacific Journal of Cancer Prevention, 24, 819–825. https://doi.org/

10.31557/APJCP.2023.24.3.819

Rezanko, T., Akkalp, A. K., Tunakan, M., & Sari, A. A. (2008). MIB-1 counting

methods in meningiomas and agreement among pathologists. Analytical

and Quantitative Cytology and Histology, 30, 47–52.

Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., &

Müller, M. (2011). pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics [Electronic Resource],

12, 77. https://doi.org/10.1186/1471-2105-12-77

Santelli, L., Ramondo, G., Della Puppa, A., Ermani, M., Scienza, R., D’avella,

D., & Manara, R. (2010). Diffusion-weighted imaging does not predict histological grading in meningiomas. Acta Neurochirurgica, 152, 1315–1319.

discussion 1319. https://doi.org/10.1007/s00701-010-0657-y

Sanverdi, S. E., Ozgen, B., Oguz, K. K., Mut, M., Dolgun, A., Soylemezoglu, F., &

Cila, A. (2012). Is diffusion-weighted imaging useful in grading and differentiating histopathological subtypes of meningiomas? European Journal

of Radiology, 81, 2389–2395. https://doi.org/10.1016/j.ejrad.2011.06.

031

She, D., Huang, H., Guo, W., Jiang, D., Zhao, X., Kang, Y., & Cao, D. (2023).

Grading meningiomas with diffusion metrics: A comparison between

diffusion kurtosis, mean apparent propagator, neurite orientation dispersion and density, and diffusion tensor imaging. European Radiology, 33,

3671–3681. https://doi.org/10.1007/s00330-023-09505-3

Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E.

J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D.

E., Niazy, R. K., Saunders, J., Vickers, J., Zhang, Y., De Stefano, N., Brady,

J. M., & Matthews, P. M. (2004). Advances in functional and structural

MR image analysis and implementation as FSL. Neuroimage, 23, (Suppl 1),

S208–S219. https://doi.org/10.1016/j.neuroimage.2004.07.051

Stadnik, T. W., Chaskis, C., Michotte, A., Shabana, W. M., van Rompaey, K.,

Luypaert, R., Budinsky, L., Jellus, V., & Osteaux, M. (2001). Diffusionweighted MR imaging of intracerebral masses: Comparison with conventional MR imaging and histologic findings. Ajnr American Journal of

Neuroradiology, 22, 969–976.

Tang, Y., Dundamadappa, S. K., Thangasamy, S., Flood, T., Moser, R., Smith, T.,

Cauley, K., & Takhtani, D. (2014). Correlation of apparent diffusion coefficient with Ki-67 proliferation index in grading meningioma. Ajr American

Journal of Roentgenology, 202, 1303–1308. https://doi.org/10.2214/AJR.

13.11637

Thomsen, H. S. (2003). Guidelines for contrast media from the European

Society of Urogenital Radiology. Ajr American Journal of Roentgenology,

181, 1463–1471. https://doi.org/10.2214/ajr.181.6.1811463

Toh, C.-H., Castillo, M., Wong, A. M.-C., Wei, K.-C., Wong, H.-F., Ng, S.-H., &

Wan, Y.-L. (2008). Differentiation between classic and atypical meningiomas with use of diffusion tensor imaging. Ajnr American Journal of

Neuroradiology, 29, 1630–1635. https://doi.org/10.3174/ajnr.A1170

Tyagi, A., Chakrabarty, A., & Franks, A. (2004). MIB1 proliferation index in

meningiomas: Does it predict recurrence? A clinicopathological study.

British Journal of Neurosurgery, 18, 357–361. https://doi.org/10.1080/

02688690400005008

Velmurugan, T., & Santhanam, T. (2010). Computational complexity between

K-means and K-medoids clustering algorithms for normal and uniform distributions of data points. Journal of Computer Science, 6(3),

363–368.

Zhu, H., Xie, Q., Zhou, Y., Chen, H., Mao, Y., Zhong, P., Zheng, K., Wang,

Y., Wang, Y., Xie, L., Zheng, M., Tang, H., Wang, D., Chen, X., Zhou,

L., & Gong, Y. (2015). Analysis of prognostic factors and treatment of

21579032, 2023, 10, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/brb3.3201 by Cochrane Japan, Wiley Online Library on [10/03/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

TAKAHASHI ET AL .

anaplastic meningioma in China. Journal of Clinical Neuroscience, 22,

690–695. https://doi.org/10.1016/j.jocn.2014.10.023

Zikou, A., Alexiou, G. A., Goussia, A., Kosta, P., Xydis, V., Voulgaris, S.,

Kyritsis, A. P., & Argyropoulou, M. I. (2016). The role of diffusion tensor

imaging and dynamic susceptibility perfusion MRI in the evaluation of

meningioma grade and subtype. Clinical Neurology and Neurosurgery, 146,

109–115. https://doi.org/10.1016/j.clineuro.2016.05.005

TAKAHASHI ET AL .

How to cite this article: Takahashi, Y., Oishi, N., Yamao, Y.,

Kunieda, T., Kikuchi, T., Fukuyama, H., Miyamoto, S., &

Arakawa, Y. (2023). Voxel-based clustered imaging by

multiparameter diffusion tensor images for predicting the

grade and proliferative activity of meningioma. Brain and

Behavior, 13, e3201. https://doi.org/10.1002/brb3.3201

21579032, 2023, 10, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/brb3.3201 by Cochrane Japan, Wiley Online Library on [10/03/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

15 of 15

...