title: Characterization of gradient echo signal decays in healthy and cancerous prostate at 3T improves with a Gaussian augmentation of the mono-exponential (GAME) model.
abstract: A biomarker of cancer aggressiveness, such as hypoxia, could substantially impact treatment decisions in the prostate, especially radiation therapy, by balancing treatment morbidity (urinary incontinence, erectile dysfunction, etc.) against mortality. R2 (*) mapping with Mono-Exponential (ME) decay modeling has shown potential for identifying areas of prostate cancer hypoxia at 1.5T. However, Gaussian deviations from ME decay have been observed in other tissues at 3T. The purpose of this study is to assess whether gradient-echo signal decays are better characterized by a standard ME decay model, or a Gaussian Augmentation of the Mono-Exponential (GAME) decay model, in the prostate at 3T. Multi-gradient-echo signals were acquired on 20 consecutive patients with a clinical suspicion of prostate cancer undergoing MR-guided prostate biopsies. Data were fitted with both ME and GAME models. The information contents of these models were compared using Akaike's information criterion (second order, AICC ), in skeletal muscle, the prostate central gland (CG), and peripheral zone (PZ) regions of interest (ROIs). The GAME model had higher information content in 30% of the prostate on average (across all patients and ROIs), covering up to 67% of cancerous PZ ROIs, and up to 100% of cancerous CG ROIs (in individual patients). The higher information content of GAME became more prominent in regions that would be assumed hypoxic using ME alone, reaching 50% of the PZ and 70% of the CG as ME R2 (*) approached 40 s(-1) . R2 (*) mapping may have important applications in MRI; however, information lost due to modeling could mask differences in parameters due to underlying tissue anatomy or physiology. The GAME model improves characterization of signal behavior in the prostate at 3T, and may increase the potential for determining correlates of fit parameters with biomarkers, for example of oxygenation status.
authors.affiliation: Department of Biomedical Engineering, Akdeniz University, Antalya, Turkey.|Harvard Medical School, Boston, MA, USA.|Brigham and Women's Hospital, Boston, MA, USA.
authors.forename: Pelin Aksit
authors.initials: PA
authors.lastname: Ciris
authors.affiliation: Harvard Medical School, Boston, MA, USA.|Boston Children's Hospital, Boston, MA, USA.
authors.forename: Mukund
authors.initials: M
authors.lastname: Balasubramanian
authors.affiliation: Siemens Healthcare, Boston, MA, USA.
authors.forename: Ravi T
authors.initials: RT
authors.lastname: Seethamraju
authors.affiliation: Harvard Medical School, Boston, MA, USA.|Brigham and Women's Hospital, Boston, MA, USA.
authors.forename: Junichi
authors.initials: J
authors.lastname: Tokuda
authors.affiliation: Harvard Medical School, Boston, MA, USA.|Brigham and Women's Hospital, Boston, MA, USA.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Scalera
authors.affiliation: Harvard Medical School, Boston, MA, USA.|Brigham and Women's Hospital, Boston, MA, USA.|Charité Universitätsmedizin Berlin, Berlin, Germany.
authors.forename: Tobias
authors.initials: T
authors.lastname: Penzkofer
authors.affiliation: Harvard Medical School, Boston, MA, USA.|Brigham and Women's Hospital, Boston, MA, USA.|Dana-Farber Cancer Institute, MA, USA.
authors.forename: Fiona M
authors.initials: FM
authors.lastname: Fennessy
authors.affiliation: Harvard Medical School, Boston, MA, USA.|Brigham and Women's Hospital, Boston, MA, USA.
authors.forename: Clare M
authors.initials: CM
authors.lastname: Tempany-Afdhal
authors.affiliation: Harvard Medical School, Boston, MA, USA.|Brigham and Women's Hospital, Boston, MA, USA.
authors.forename: Kemal
authors.initials: K
authors.lastname: Tuncali
authors.affiliation: Harvard Medical School, Boston, MA, USA.|Boston Children's Hospital, Boston, MA, USA.
authors.forename: Robert V
authors.initials: RV
authors.lastname: Mulkern
mesh_terms: D000368:Aged; D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D015233:Models, Statistical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
keywords: Gaussian; Lorentzian; biophysical modeling; hypoxia; prostate cancer; transverse relaxation