Texture analysis is a promising non-invasive tool for distinguishing renal tumors on CT images. These results were further improved upon application of machine learning, and support the further development of texture analysis as a quantitative biomarker for distinguishing various renal tumors.

Quantitative magnetic resonance (MR) imaging seeks to quantify fundamental biologic and MR-inducible tissue properties, in contrast to the routine application of MR imaging in the clinic, in which differences in MR parameters are used to generate contrast for subsequent subjective image analysis. Fundamental parameters that are commonly quantified by using MR imaging include proton density, diffusion, T1 relaxation, T2 and T2* relaxation, and magnetization transfer. Applications of these MR imaging-quantifiable parameters to abdominal imaging include oncologic imaging, evaluation of diffuse liver disease, and assessment of splenic, renal, and pancreatic disease. An understanding of the inherent physical principles underlying the basic quantitative parameters as well as the commonly used pulse sequences requisite to their derivation is critical, as this field is rapidly growing and its use will likely continue to expand in the clinic. The full potential of quantitative MR imaging applied to abdominal imaging has yet to be realized, but the myriad applications reported to date will undoubtedly continue to grow.

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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 content 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, such as of oxygenation status.