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

Magn Reson Imaging. 2016 Nov;34(9):1227-1234. doi: 10.1016/j.mri.2016.06.004. Epub 2016 Jul 20.


Purpose: The 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.

Methods: A 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 65years (range 54-78) and 21.9ng/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 0-2000s/mm2 (TE=58ms; TR=3990ms; spatial resolution 2.19×2.19×2.73mm3, slices =26, FOV=140×140mm, slice gap =0.27mm, NSA=2).

Results: The 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.

Conclusion: Preliminary results of the proposed framework indicate high patient-based sensitivity with 100% detection rate for identifying moderate-high risk aggressive index lesions.

Keywords: DWI; Diffusion-weighted MRI; Prostate cancer; Tumor localization.

MeSH terms

  • Aged
  • Diffusion Magnetic Resonance Imaging / methods*
  • Humans
  • Male
  • Middle Aged
  • Prostate / diagnostic imaging
  • Prostate / pathology
  • Prostatic Neoplasms / diagnostic imaging*
  • Prostatic Neoplasms / pathology
  • Reproducibility of Results
  • Sensitivity and Specificity