Compactness index: a radiosurgery outcome predictor for patients with unruptured brain arteriovenous malformations

Po-Wei Huang; Syu-Jyun Peng; David Hung-Chi Pan; Huai-Che Yang; Jo-Ting Tsai; Cheng-Ying Shiau; I-Chang Su; Ching-Jen Chen; Hsiu-Mei Wu; Chung Jung Lin; Wen-Yuh Chung; Wan-Yuo Guo; Wei-Lun Lo; Shao-Wen Lai; Cheng-Chia Lee

doi:10.3171/2022.4.JNS212369

Compactness index: a radiosurgery outcome predictor for patients with unruptured brain arteriovenous malformations

Po-Wei Huang, Syu-Jyun Peng, David Hung-Chi Pan, Huai-Che Yang, Jo-Ting Tsai, Cheng-Ying Shiau, I-Chang Su, Ching-Jen Chen, Hsiu-Mei Wu, Chung Jung Lin, Wen-Yuh Chung, Wan-Yuo Guo, Wei-Lun Lo, Shao-Wen Lai, Cheng-Chia Lee

Research output: Contribution to journal › Article › peer-review

Abstract

OBJECTIVE
The goal of the study was to define and quantify brain arteriovenous malformation (bAVM) compactness and to assess its effect on outcomes after Gamma Knife radiosurgery (GKRS) for unruptured bAVMs.

METHODS
Unsupervised machine learning with fuzzy c-means clustering was used to differentiate the tissue constituents of bAVMs on T2-weighted MR images. The percentages of vessel, brain, and CSF were quantified. The proposed compactness index, defined as the ratio of vasculature tissue to brain tissue, categorized bAVM morphology into compact, intermediate, and diffuse types according to the tertiles of this index. The outcomes of interest were complete obliteration and radiation-induced changes (RICs).

Original language	English
Pages (from-to)	1-10
Number of pages	10
Journal	Journal of Neurosurgery
DOIs	https://doi.org/10.3171/2022.4.JNS212369
Publication status	Published - 2022

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Huang, P-W., Peng, S-J., Pan, D. H-C., Yang, H-C., Tsai, J-T., Shiau, C-Y., Su, I-C., Chen, C-J., Wu, H-M., Lin, C. J., Chung, W-Y., Guo, W-Y., Lo, W-L., Lai, S-W., & Lee, C-C. (2022). Compactness index: a radiosurgery outcome predictor for patients with unruptured brain arteriovenous malformations. Journal of Neurosurgery, 1-10. https://doi.org/10.3171/2022.4.JNS212369

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title = "Compactness index: a radiosurgery outcome predictor for patients with unruptured brain arteriovenous malformations",

abstract = "OBJECTIVEThe goal of the study was to define and quantify brain arteriovenous malformation (bAVM) compactness and to assess its effect on outcomes after Gamma Knife radiosurgery (GKRS) for unruptured bAVMs.METHODSUnsupervised machine learning with fuzzy c-means clustering was used to differentiate the tissue constituents of bAVMs on T2-weighted MR images. The percentages of vessel, brain, and CSF were quantified. The proposed compactness index, defined as the ratio of vasculature tissue to brain tissue, categorized bAVM morphology into compact, intermediate, and diffuse types according to the tertiles of this index. The outcomes of interest were complete obliteration and radiation-induced changes (RICs).",

author = "Po-Wei Huang and Syu-Jyun Peng and Pan, {David Hung-Chi} and Huai-Che Yang and Jo-Ting Tsai and Cheng-Ying Shiau and I-Chang Su and Ching-Jen Chen and Hsiu-Mei Wu and Lin, {Chung Jung} and Wen-Yuh Chung and Wan-Yuo Guo and Wei-Lun Lo and Shao-Wen Lai and Cheng-Chia Lee",

year = "2022",

doi = "10.3171/2022.4.JNS212369",

language = "English",

pages = "1--10",

journal = "Journal of Neurosurgery",

issn = "0022-3085",

publisher = "American Association of Neurological Surgeons",

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TY - JOUR

T1 - Compactness index: a radiosurgery outcome predictor for patients with unruptured brain arteriovenous malformations

AU - Huang, Po-Wei

AU - Peng, Syu-Jyun

AU - Pan, David Hung-Chi

AU - Yang, Huai-Che

AU - Tsai, Jo-Ting

AU - Shiau, Cheng-Ying

AU - Su, I-Chang

AU - Chen, Ching-Jen

AU - Wu, Hsiu-Mei

AU - Lin, Chung Jung

AU - Chung, Wen-Yuh

AU - Guo, Wan-Yuo

AU - Lo, Wei-Lun

AU - Lai, Shao-Wen

AU - Lee, Cheng-Chia

PY - 2022

Y1 - 2022

N2 - OBJECTIVEThe goal of the study was to define and quantify brain arteriovenous malformation (bAVM) compactness and to assess its effect on outcomes after Gamma Knife radiosurgery (GKRS) for unruptured bAVMs.METHODSUnsupervised machine learning with fuzzy c-means clustering was used to differentiate the tissue constituents of bAVMs on T2-weighted MR images. The percentages of vessel, brain, and CSF were quantified. The proposed compactness index, defined as the ratio of vasculature tissue to brain tissue, categorized bAVM morphology into compact, intermediate, and diffuse types according to the tertiles of this index. The outcomes of interest were complete obliteration and radiation-induced changes (RICs).

AB - OBJECTIVEThe goal of the study was to define and quantify brain arteriovenous malformation (bAVM) compactness and to assess its effect on outcomes after Gamma Knife radiosurgery (GKRS) for unruptured bAVMs.METHODSUnsupervised machine learning with fuzzy c-means clustering was used to differentiate the tissue constituents of bAVMs on T2-weighted MR images. The percentages of vessel, brain, and CSF were quantified. The proposed compactness index, defined as the ratio of vasculature tissue to brain tissue, categorized bAVM morphology into compact, intermediate, and diffuse types according to the tertiles of this index. The outcomes of interest were complete obliteration and radiation-induced changes (RICs).

U2 - 10.3171/2022.4.JNS212369

DO - 10.3171/2022.4.JNS212369

M3 - Article

SN - 0022-3085

SP - 1

EP - 10

JO - Journal of Neurosurgery

JF - Journal of Neurosurgery

ER -

Compactness index: a radiosurgery outcome predictor for patients with unruptured brain arteriovenous malformations

Abstract

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