Prostate cancer identification and assessment of clinical significance continues to be a challenge. Routine multiparametric magnetic resonance imaging has shown to be useful in assessing disease progression. Although dynamic contrast-enhanced imaging (DCE) has the ability to characterize perfusion across time and has shown enormous utility, radiological assessment (Prostate Imaging-Reporting and Data System or PIRADS version 2) has limited its use owing to lack of consistency and nonquantitative nature. In our work, we propose a systematic methodology to quantify perfusion dynamics for the DCE imaging. Using these metrics, 7 different subregions or perfusion habitats of the targeted lesions are localized and related to clinical significance. We found that quantitative features describing the habitat based on the late area under the DCE time-activity curve was a good predictor of clinical significance disease. The best predictive feature in the habitat had an AUC of 0.82, CI [0.81-0.83].
Keywords: DCE; MRI; habitats; machine learning; prostate cancer; radiomics.