Breast cancer recurrence risk prediction using whole-lesion histogram analysis with diffusion kurtosis imaging. Issue 3 (March 2020) - Wu, J.; Yan, F.; Chai, W.; Fu, C.; Yan, X.; Zhan, Y.; Sun, K.
Abstract : AIM: To explore the role of whole-lesion histogram analysis on diffusion kurtosis imaging (DKI) for predicting breast cancer 21-gene expression profiles and recurrence scores (RSs). MATERIALS AND METHODS: This retrospective study was approved by the institutional review board, and informed consent was waived. Seventy-two patients with breast cancer, who underwent genomic testing and DKI (b values: 0–2, 800 s/mm 2 ) were enrolled. Patients were divided into low-, intermediate-, and high-RS groups based on their genomic testing results. Diffusivity (D), kurtosis (K), total apparent diffusion coefficient (Total ADC), and ADC0–700 histogram parameters were calculated. Student's t -test, Wilcoxon signed-rank test, Jonckheere–Terpstra test, receiver operating characteristic curves, and Spearman's correlation were used for the statistical analysis. RESULTS: Total ADC mean/30%/50%/70%, D mean/50%, K mean/30%/50%/70% showed significant differences among the low-, intermediate-, and high-RS groups ( p ≤ 0.001, respectively). K50% had the strongest correlation with RSs (correlation coefficient, CC: 0.55). Furthermore, K50% was also correlated with the expression of gene PR, BCL2 and CEGP1 (CC: 0.45, –0.41, –0.41). CONCLUSIONS: Whole-lesion histogram analysis of DKI parameters can be a useful tool for RS prediction of breast cancer. K50% was found to be the most promising parameter for RS prediction. Highlights: DKI is a promising method to predict recurrence risks. DKI is a noninvasive tool to reflect estrogen-related gene expression. K50% based on histogram analysis could be a specific biomarker to predict recurrence.