Analysis of morphological characteristics of IDH-mutant/wildtype brain tumors using whole-lesion phenotype analysis. Issue 1 (24th June 2021)

• Record Type: Journal Article
• Authors: Snyder, James M; Huang, Raymond Y; Bai, Harrison; Rao, Vikram R; Cornes, Susannah; Barnholtz-Sloan, Jill S; Gutman, David; Fasano, Rebecca; Van Meir, Erwin G; Brat, Daniel; Eschbacher, Jennifer; Quackenbush, John; Wen, Patrick Y; Lee, Jong Woo
• Is Part Of: Neuro-oncology advances. Volume 3:Issue 1(2021)
• Publication Date: 2021-06-24
• Subjects: brain tumor -- IDH mutation -- statistical mapping -- TCGA -- TCIA; 616.99481
• DOI: 10.1093/noajnl/vdab088
• Languages: English
• ISSNs: 2632-2498

Abstract

Abstract: Background: Although IDH-mutant tumors aggregate to the frontotemporal regions, the clustering pattern of IDH-wildtype tumors is less clear. As voxel-based lesion-symptom mapping (VLSM) has several limitations for solid lesion mapping, a new technique, whole-lesion phenotype analysis (WLPA), is developed. We utilize WLPA to assess spatial clustering of tumors with IDH mutation from The Cancer Genome Atlas and The Cancer Imaging Archive. Methods: The degree of tumor clustering segmented from T1 weighted images is measured to every other tumor by a function of lesion similarity to each other via the Hausdorff distance. Each tumor is ranked according to the degree to which its neighboring tumors show identical phenotypes, and through a permutation technique, significant tumors are determined. VLSM was applied through a previously described method. Results: A total of 244 patients of mixed-grade gliomas (WHO II–IV) are analyzed, of which 150 were IDH-wildtype and 139 were glioblastomas. VLSM identifies frontal lobe regions that are more likely associated with the presence of IDH mutation but no regions where IDH-wildtype was more likely to be present. WLPA identifies both IDH-mutant and -wildtype tumors exhibit statistically significant spatial clustering. Conclusion: WLPA may provide additional statistical power when compared with VLSM without making several potentially erroneous assumptions. WLPA identifies tumors most likely to exhibit particular phenotypes, rather than producing anatomical maps, and may be used in conjunction with VLSM to understand the relationship between tumor morphology and biologically relevant tumor phenotypes.