Risk stratification of patients with prostate cancer: promising results with high b-value DWI radiomic features

Purpose: To investigate the potential role of radiomic features computed on high b-value Diffusion Weighted Imaging (DWI) to perform risk stratification of patients with a clinical suspicion of prostate cancer (PCa). Materials and Methods: 42 patients of our institution, representing 7 risk levels, were retrospectively enrolled in the study and grouped into 4 classes of risk: (a) clinically significant (CS) PCa, split over 4 levels (ISUP=2÷5), (b) non-clinically significant (NCS) PCa (ISUP=1), patients with a negative biopsy and (c) positive mpMRI (NP) or (d) negative mpMRI (NN). After computing radiomic features on DWI b=2000s/mm2, the correlation between radiomic features and risk level was investigated through two steps: (i) Spearman index (ρ), (ii) Kruskal-Wallis and Wilcoxon tests (p<0.05) for multi- and pairwise- comparison of the 4 classes, respectively. Results: The mean of local coefficient of variation (CVL-m), a measure of local dispersion of DWI values, resulted in the most discriminant radiomic features among the four classes (p~10-6), able to rank the four increasing risk classes with ρ=0.81, with a high pairwise separability (p≤0.026). ρ=0.81 is also achieved when correlating the CVL-m with all the 7 increasing risk level groups. Conclusions: This study allows performing an early stratification of all 7 PCa risk levels. Increasing values of CVL-m in DWI images describes a higher degree of local heterogeneity, in accordance with tissue over-proliferation and, consequently, increasing level of tumour aggressiveness. Limitations: The number of patients could be low for a proper stratification of the cohort in 7 classes. However, the excellent results achieved when using CVL-m values to correctly rank all risk levels give CVL-m the most promising role in depicting PCa risk progression. Ethics committee approval: IRB approval, written informed consent was waived. Funding: No funding was received for this work.