Characterization of gradient echo signal decays in healthy and cancerous prostate at 3T improves with a Gaussian augmentation of the mono-exponential (GAME) model.

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.

NMR in biomedicine. 2016 May 31 [Epub ahead of print]

Pelin Aksit Ciris, Mukund Balasubramanian, Ravi T Seethamraju, Junichi Tokuda, Jonathan Scalera, Tobias Penzkofer, Fiona M Fennessy, Clare M Tempany-Afdhal, Kemal Tuncali, Robert V Mulkern

Department of Biomedical Engineering, Akdeniz University, Antalya, Turkey., Harvard Medical School, Boston, MA, USA., Siemens Healthcare, Boston, MA, USA., Harvard Medical School, Boston, MA, USA., Harvard Medical School, Boston, MA, USA., Harvard Medical School, Boston, MA, USA., Harvard Medical School, Boston, MA, USA., Harvard Medical School, Boston, MA, USA., Harvard Medical School, Boston, MA, USA., Harvard Medical School, Boston, MA, USA.

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