Table 2 Radiomic feature families extracted from the intratumoral and peritumoral regions

Laws energy measures

25

Response to 5-pixel × 5-pixel filter targeting combination of specific textural enhancement patterns in the x and y directions. Descriptors include all combinations of five 1D filters: level (L), edge (E), spot (S), wave (W), and ripple (R).

May possibly detect patterns of heterogeneous enhancement and abnormal structure; have previously been shown to enable quantification of TILs by lung CT [57].

Gabor features

48

Detection of edges through response to Gabor wavelet features. Each descriptor quantifies response to a given Gabor filter at a specific frequency (f = 0, 2, 4, 8, 16, or 32) and orientation (θ = 0 degrees, 22.5 degrees, 45 degrees, 67.5 degrees, 90 degrees, 112.5 degrees, 135 degrees, 167.5 degrees).

May possibly capture changes in tumor microarchitecture on account of glandular morphology or detect the presence of TILs [57]. TILs have been shown to be prognostic of better survival and NAC response [54].

Haralick features

13

Quantify heterogeneity and entropy of local intensity texture as represented by the gray-level co-occurrence matrix within a 5-pixel × 5-pixel window.

Regional changes in Haralick features following treatment have been shown to predict pCR in breast cancer [19].

Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) features

13

Apply Haralick metrics to dominant intensity gradient orientations within a 5-pixel × 5-pixel window, quantifying patterns of local gradient alignment [59, 60]. Some descriptors quantify homogeneity of gradient orientations (e.g., CoLlAGe information measure of correlation), whereas others compute their disorder (e.g., CoLlAGe entropy).

CoLlAGe entropy has previously been demonstrated to be effective in distinguishing breast cancer subtypes [59, 60].