Application of an unsupervised multi-characteristic framework for intermediate-high risk prostate cancer localization using diffusion-weighted MRI

PurposeThe aim of this proof-of-concept work is to propose an unsupervised framework that combines multiple parameters, in “positive-if-all-positive” manner, from different models to localize tumors.MethodsA voxel-by-voxel analysis of the DW-MRI images of whole prostate was performed to obtain parametric maps for D*, D, f, and K using the IVIM and kurtosis models. Ten patients with moderate or high-risk prostate cancer were included in study. The mean age and serum PSA for these 10 patients were 65 years (range 54–78) and 21.9 ng/mL (range 4.84–44.81), respectively. These patients were scanned using a DW spin-echo sequence with echo-planar readout with 16 equidistantly spaced b-values in the range of 0–2000 s/mm2 (TE = 58 ms; TR = 3990 ms; spatial resolution 2.19 × 2.19 × 2.73 mm3, slices =26, FOV = 140 × 140mm, slice gap =0.27 mm, NSA = 2).ResultsThe proposed framework detected 24 lesions of which 14 were true positive with 58% tumor detection rate on lesion-based analysis with sensitivity of 100%. The mpMRI evaluation (PIRADSv2) identified 12 of 14 true positive lesions with sensitivity of 86%; positive predictive value of mpMRI was 92%. The index lesions were visible on all framework maps and were coded as the most suspicious in 9 of 10 patients.ConclusionPreliminary results of the proposed framework indicate high patient-based sensitivity with 100% detection rate for identifying moderate-high risk aggressive index lesions.