Glioma grading, molecular feature classification, and microstructural characterization using MR diffusional variance decomposition (DIVIDE) imaging.
Sirui LiYuan ZhengWenbo SunSamo LasičFilip SzczepankiewiczQing WeiShihong HanShuheng ZhangXiaoli ZhongLiang WangHuan LiYuxiang CaiDan XuZhiqiang LiQiang HeDanielle van WestenKarin BryskheDaniel TopgaardHai-Bo XuPublished in: European radiology (2021)
• DIVIDE metrics MKI is related to cell density heterogeneity while MKA and μFA are related to cell eccentricity. • DIVIDE metrics can effectively differentiate LGG from HGG and IDH mutation from wild-type tumor, and showed significant correlation with the Ki-67 labeling index. • MKI was larger than MKA which indicates predominant cell density heterogeneity in gliomas. • MKA and MKI increased with grade or degree of malignancy, however with a relatively larger increase in the cell eccentricity metric MKA in relation to the cell density heterogeneity metric MKI.