title: Time-Dependent Diffusion in Prostate Cancer.
abstract: Prior studies in prostate diffusion-weighted magnetic resonance imaging (MRI) have largely explored the impact of b-value and diffusion directions on estimated diffusion coefficient D. Here we suggest varying diffusion time, t, to study time-dependent D(t) in prostate cancer, thereby adding an extra dimension in the development of prostate cancer biomarkers.
  
  
  
  Thirty-eight patients with peripheral zone prostate cancer underwent 3-T MRI using an external-array coil and a diffusion-weighted image sequence acquired for b = 0, as well as along 12 noncollinear gradient directions for b = 500 s/mm using stimulated echo acquisition mode (STEAM) diffusion tensor imaging (DTI). For this sequence, 6 diffusion times ranging from 20.8 to 350 milliseconds were acquired. Tumors were classified as low-grade (Gleason score [GS] 3 + 3; n = 11), intermediate-grade (GS 3 + 4; n = 16), and high-grade (GS ≥4 + 3; n = 11). Benign peripheral zone and transition zone were also studied.
  
  
  
  Apparent diffusion coefficient (ADC) D(t) decreased with increasing t in all zones of the prostate, though the rate of decay in D(t) was different between sampled zones. Analysis of variance and area under the curve analyses suggested better differentiation of tumor grades at shorter t. Fractional anisotropy (FA) increased with t for all regions of interest. On average, highest FA was observed within GS 3 + 3 tumors.
  
  
  
  There is a measurable time dependence of ADC in prostate cancer, which is dependent on the underlying tissue and Gleason score. Therefore, there may be an optimal selection of t for prediction of tumor grade using ADC. Controlling t should allow ADC to achieve greater reproducibility between different sites and vendors. Intentionally varying t enables targeted exploration of D(t), a previously overlooked biophysical phenomenon in the prostate. Its further microstructural understanding and modeling may lead to novel diffusion-derived biomarkers.
authors.affiliation: From the *Center for Biomedical Imaging, Department of Radiology, and †Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine; and ‡Division of Urologic Oncology, New York University Langone Medical Center, New York, NY.
authors.forename: Gregory
authors.initials: G
authors.lastname: Lemberskiy
authors.forename: Andrew B
authors.initials: AB
authors.lastname: Rosenkrantz
authors.forename: Jelle
authors.initials: J
authors.lastname: Veraart
authors.forename: Samir S
authors.initials: SS
authors.lastname: Taneja
authors.forename: Dmitry S
authors.initials: DS
authors.lastname: Novikov
authors.forename: Els
authors.initials: E
authors.lastname: Fieremans
mesh_terms: D000368:Aged; D014408:Biomarkers, Tumor; D015331:Cohort Studies; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005240:Feasibility Studies; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D060787:Neoplasm Grading; D011467:Prostate / Q000000981:diagnostic imaging / Q000473:pathology; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D015203:Reproducibility of Results; D012189:Retrospective Studies; D013995:Time
chemical_list: D014408:Biomarkers, Tumor