Effect of biopsy on the MRI radiomics classification of benign lesions and luminal A cancers

J Med Imaging (Bellingham). 2019 Jul;6(3):031408. doi: 10.1117/1.JMI.6.3.031408. Epub 2019 Feb 18.


Radiomic features extracted from magnetic resonance (MR) images have potential for diagnosis and prognosis of breast cancer. However, presentation of lesions on images may be affected by biopsy. Thirty-four nonsize features were extracted from 338 dynamic contrast-enhanced MR images of benign lesions and luminal A cancers (80 benign/34 luminal A prebiopsy; 46 benign/178 luminal A postbiopsy). Feature value distributions were compared by biopsy condition using the Kolmogorov-Smirnov test. Classification performance was assessed by biopsy condition in the task of distinguishing between lesion types using the area under the receiver operating characteristic curve (AUCROC) as performance metric. Superiority and equivalence testing of differences in AUCROC between biopsy conditions were conducted using Bonferroni-Holm-adjusted significance levels. Distributions for most nonsize features for each lesion type failed to show a statistically significant difference between biopsy conditions. Fourteen features outperformed random guessing in classification. Their differences in AUCROC by biopsy condition failed to reach statistical significance, but we were unable to prove equivalence using a margin of Δ AUCROC = ± 0.10 . However, classification performance for lesions imaged either prebiopsy or postbiopsy appears to be similar when taking into account biopsy condition.

Keywords: biopsy; breast cancer; computer-aided diagnosis; magnetic resonance imaging; radiomics.