ADC histogram analysis for prediciting highly aggressive breast carcinomas

Introduction Diffusion Weighted Imaging (DWI) has demonstrated increased potential in identifying imaging-based biomarkers for cancer diagnosis, prognosis and monitoring response to therapy. Purpose This study investigates the feasibility of apparent diffusion coefficient (ADC) histogram statistics in identifying highly aggressive breast carcinomas, by investigating correlation to established prognostic indices. Materials and methods The dataset consists of 43 histologically verified invasive ductal carcinomas (IDC) of patients undergoing breast DW-MRI at 3 T ( b -values 0, 900 s/mm2). ADC maps were generated for a slice representative of lesion largest diameter. An expert radiologist delineated lesion contour on ADC map, defining the lesion region of interest to be subjected to histogram analysis. Feature extraction considered mean, standard deviation, skewness, kurtosis, entropy, maximum, minimum and range of ADC. The ability of ADC histogram features in identifying tumor grade, estrogen receptor (ER) and progesterone receptor (PR) status was investigated. Results Median value of feature ADC mean was 1.034 × 10 −3  mm 2 /s, 0.869 × 10 −3  mm 2 /s and 0.971 × 10 −3  mm 2 /s in grade I, grade II and grade III lesions, respectively. Median value of ADC kurtosis was 0.176, 1.165 and 1.289 in grade I, grade II and grade III lesions, respectively. ER positive lesions demonstrated increased median value of ADC entropy (5.828), reflecting increased ADC heterogeneity, as compared to ER negative ones (5.480). Median value of ADC entropy was 5.842 and 5.486 in PR positive and PR negative lesions, respectively. Conclusion ADC histogram analysis may contribute in breast cancer prognosis by identifying high grade tumors and predicting ER and PR status.