Skip to main content

Table 3 Texture features that showed significantly different mean values comparing malignant versus benign solid lesions and corresponding area under the curve (AUC)

From: Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study

Feature Malignant lesions (mean ± standard deviation) Benign lesions (mean ± standard deviation) p value AUC (95% confidence interval)
Entropy 5.28 ± 0.38 5.67 ± 0.16 < 0.00001 for all 0.86 (0.82–0.89)
Skewness 0.74 ± 0.33 0.54 ± 0.41 0.66 (0.61–0.70)
Kurtosis 0.53 ± 0.67 0.31 ± 0.66 0.61 (0.56–0.65)
Contrast 24.45 ± 10.61 28.40 ± 13.66 0.58 (0.53–0.63)
GLN (GLCM) 96.33 ± 87.47 60.58 ± 61.73 0.67 (0.62–0.71)
RLN (GLCM) 2,218.97 ± 1,834.77 1,523.25 ± 1,681.38 0.66 (0.62–0.71)
HGRE (GLCM) 1,163.54 ± 20.49 1,171.63 ± 19.35 0.62 (0.57–0.67)
SRHGE (GLCM) 1,067.79 ± 32.58 1,080.79 ± 29.69 0.63 (0.58–0.67)
GLN (GLRLM) 96.43 ± 87.45 60.70 ± 61.72 0.67 (0.62–0.71)
RLN (GLRLM) 2,218.89 ± 1,828.26 1,525.31 ± 1,677.17 0.67 (0.62–0.71)
HGRE (GLRLM) 1,165.00 ± 20.59 1,173.06 ± 19.57 0.62 (0.57–0.67)
SRHGE (GLRLM) 1,069.20 ± 33.13 1,082.28 ± 29.75 0.62 (0.58–0.67)
  1. GLN Grey-level non-uniformity, GLCM Grey-level co-occurrence matrix, RLN Run length non-uniformity, HGRE High grey-level run emphasis, SRHGE Short-run high grey-level emphasis, GLN Grey-level non-uniformity, GLRLM Grey-level run length matrix