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.
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).