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. 2019 Nov 19;5:43. doi: 10.1038/s41523-019-0134-6

Table 3.

Summary of top 10 ranked histologic features identified in the random forest model for the prediction of invasive cancer status among women with high and low % fibroglandular volume

Feature Name High global FGV (%) (> median) Low global FGV (%) (≤ median) High localized FGV (%) (> median) Low localized FGV (%) (≤ median)
Global tissue amount
 Fat amount (µm2) 8 5
 Fat amount normalized (%) 3 7
 Stroma amount (µm2) 9
 Stroma amount normalized (%) 4 10 3
 Epithelium amount (µm2) 4 4 1
 Epithelium amount normalized (%) 6 7 8
Morphology
 Epithelial regions (IQ µm2) 1 4
 Epithelial regions (max µm2) 9
 Ecc epi regions (mean) 10 3 9
 Ecc epi regions (median) 2 2
 Ecc epi regions (IQ) 5 10
Spatial arrangement of the epithelial regions (Area-Voronoi diagram)
 Voronoi area (mean µm2) 5 7 6
 Voronoi area (median µm2) 3 5
 Voronoi area (SD µm2) 9
 Voronoi area (IQ µm2) 7 6
 Ratio epi to Voronoi (mean) 1 2 8
 Ratio epi to Voronoi (median) 2 1
 Ratio epi to Voronoi (IQ) 10
 Ratio epi to non-epi (median) 6
Spatial arrangement of the epithelial regions (Delaunay Triangulation)
 Neighbors (mean number) 8

Ecc eccentricity, Epi epithelial, IQ interquartile, FGV fibroglandular volume, SD standard deviation

Only histologic features ranked within the top 10 for prediction of each density measure are included in the table

Features are ranked numerically and sequentially from 1–10, with 1 representing the most important feature and 10 representing the 10th most important feature

The median cut points of breast density used in stratification were: global FGV (%) 34.4, localized FGV (%) 40.0