Unsupervised Machine Learning Using K-means Identifies Radiomic Subgroups of Pediatric Low-grade Gliomas that Correlate with Key Molecular Markers

Neoplasia

February 28, 2023

Haldar, D., Kazerooni, A. F., Arif, S., Familiar, A., Madhogarhia, R., Khalili, N., Bagheri, S., Anderson, H., Shaikh, I. S., Mahtabfar, A., Kim, M. C., Tu, W., Ware, J., Vossough, A., Davatzikos, C., Storm, P. B., Resnick, A. C., & Nabavizadeh, A.

Summary

Despite advancements in molecular and histopathologic characterization of pediatric low-grade gliomas (pLGGs), there remains significant phenotypic heterogeneity among tumors with similar categorizations. We hypothesized that an unsupervised machine learning approach based on radiomic features may reveal distinct pLGG imaging subtypes.

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