This study explores potential multi-parametric magnetic resonance imaging (mpMRI) biomarkers for improved non-invasive prostate cancer (PCa) management, particularly for clinically significant PCa lesions that are missed by the current standard of care mpMRI exams. T2-weighted MRI and diffusion weighted imaging scans were acquired for subjects with Gleason Score 6 to 9 PCa lesions prior to radical prostatectomy. Lesions were examined by experienced clinicians and categorized as those visible on routine mpMRI radiological assessment, viz. mpMRI-detectable (mpMRI-d) and those that could not be satisfactorily diagnosed using routine mpMRI, viz. mpMRI-undetectable (mpMRI-u). A dense set of voxel-wise mpMRI features quantifying local spatial distribution, texture and statistical properties in the prostate were computed and analyzed for their potential to differentiate disease from healthy tissue. Feature selection and training were performed to build classifiers to produce voxel-wise probability-of-malignancy maps over the entire gland to characterize the extent of PCa. Receiver operating characteristic analysis on a cohort of 48 mpMRI exams showed that our classifier improves the sensitivity of the detection of prostate lesions by over 38% for the whole cohort and 124% for the mpMRI-u lesions with only a modest loss in specificity when compared to baseline routine radiological mpMRI assessment.
['Xia Li', 'Asha Singanamalli', 'Dattesh Shanbhag', 'Andreas M. Hötker', 'Omer Aras', 'Oguz Akin', 'Roshni Bhagalia']
Improved noninvasive prostate cancer assessment using multiparametric magnetic resonance imaging