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

Author:

Huang Po-Wei1,Peng Syu-Jyun2,Pan David Hung-Chi345,Yang Huai-Che36,Tsai Jo-Ting178,Shiau Cheng-Ying96,Su I-Chang4105,Chen Ching-Jen11,Wu Hsiu-Mei126,Lin Chung-Jung126,Chung Wen-Yuh3136,Guo Wan-Yuo126,Lo Wei-Lun4105,Lai Shao-Wen14,Lee Cheng-Chia3615

Affiliation:

1. Department of Radiation Oncology, Shuang Ho Hospital, Taipei Medical University, New Taipei City;

2. Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei;

3. Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei;

4. Department of Neurosurgery, Shuang Ho Hospital, Taipei Medical University, New Taipei City;

5. Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan; and

6. School of Medicine, National Yang Ming Chiao Tung University, Taipei;

7. Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei;

8. Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei;

9. Cancer Center, Taipei Veterans General Hospital, Taipei;

10. Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei;

11. Department of Neurological Surgery, University of Virginia Health System, Charlottesville, Virginia;

12. Department of Radiology, Taipei Veterans General Hospital, Taipei;

13. Department of Neurosurgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan;

14. Product and Engineering, Zippin, San Carlos, California

15. Brain Research Center, National Yang Ming Chiao Tung University, Taipei;

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). RESULTS A total of 209 unruptured bAVMs treated with GKRS were retrospectively included. The median imaging and clinical follow-up periods were 49.2 and 72.3 months, respectively. One hundred seventy-three bAVMs (82.8%) achieved complete obliteration after a median latency period of 43.3 months. The rates of RIC and permanent RIC were 76.1% and 3.8%, respectively. Post-GKRS hemorrhage occurred in 14 patients (6.7%), resulting in an annual bleeding risk of 1.0%. Compact bAVM, smaller bAVM volume, and exclusively superficial venous drainage were independent predictors of complete obliteration. Diffuse bAVM morphology, larger bAVM volume, and higher margin dose were independently associated with RICs. CONCLUSIONS The compactness index quantitatively describes the compactness of unruptured bAVMs. Moreover, compact bAVMs may have a higher obliteration rate and a smaller risk of RICs than diffuse bAVMs. This finding could help guide decision-making regarding GKRS treatment for patients with unruptured bAVMs.

Publisher

Journal of Neurosurgery Publishing Group (JNSPG)

Subject

Genetics,Animal Science and Zoology

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