Classification of prostatic diseases by means of multivariate analysis on in vivo proton MRSI and DCE-MRI data

Models and methods for analyzing DCE-MRI: a review

PURPOSE

To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches.

METHODS

DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration-time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases.

RESULTS

Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors.

CONCLUSIONS

Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.

Imaging biomarkers in prostate cancer: role of PET/CT and MRI

Prostate-specific antigen (PSA) is currently the most widely used biomarker of prostate cancer (PCa). PSA suggests the presence of primary tumour and disease relapse after treatment, but it is not able to provide a clear distinction between locoregional and distant disease. Molecular and functional imaging, that are able to provide a detailed and comprehensive overview of PCa extension, are more reliable tools for primary tumour detection and disease extension assessment both in staging and restaging. In the present review we evaluate the role of PET/CT and MRI in the diagnosis, staging and restaging of PCa, and the use of these imaging modalities in prognosis, treatment planning and response assessment. Innovative imaging strategies including new radiotracers and hybrid scanners such as PET/MRI are also discussed.

High-resolution NMR spectroscopy in inhomogeneous fields via Hadamard-encoded intermolecular double-quantum coherences

A new pulse sequence based on intermolecular double-quantum coherences was proposed to obtain one-dimensional high-resolution liquid NMR spectra in inhomogeneous magnetic fields via Hadamard encoding. In contrast with the conventional intermolecular multiple-quantum coherences method with a two-dimensional acquisition to obtain one one-dimensional high-resolution spectrum, the new method can provide relatively high-resolution spectra directly through one-dimensional acquisition, and can greatly improve the signal-to-noise ratio of the spectrum within a relatively short acquisition time. Theoretical derivation was performed and analytical expressions of the resulting signals are given. Solution samples in purposely de-shimmed magnetic fields and pig brain tissue samples were tested. The experimental results demonstrate that this sequence can retain useful structural information, even when the field inhomogeneity is sufficiently severe to erase almost all spectral information with conventional one-dimensional single-quantum coherence techniques, and good solvent suppression can be achieved. This method may provide a promising technique for applications in in vivo and in vitro NMR.

Multivariate analysis of cartilage degradation using the support vector machine algorithm

An important limitation in MRI studies of early osteoarthritis is that measured MRI parameters exhibit substantial overlap between different degrees of cartilage degradation. We investigated whether multivariate support vector machine analysis would permit improved tissue characterization. Bovine nasal cartilage samples were subjected to pathomimetic degradation and their T(1), T(2), magnetization transfer rate (k(m) ), and apparent diffusion coefficient (ADC) were measured. Support vector machine analysis performed using certain parameter combinations exhibited particularly favorable classification properties. The areas under the receiver operating characteristic (ROC) curve for detection of extensive and mild degradation were 1.00 and 0.94, respectively, using the set (T(1), k(m), ADC), compared with 0.97 and 0.60 using T(1), the best univariate classifier. Furthermore, a degradation probability for each sample, derived from the support vector machine formalism using the parameter set (T(1), k(m), ADC), demonstrated much stronger correlations (r(2) = 0.79-0.88) with direct measurements of tissue biochemical components than did even the best-performing individual MRI parameter, T(1) (r(2) = 0.53-0.64). These results, combined with our previous investigation of Gaussian cluster-based tissue discrimination, indicate that the combinations (T(1), k(m)) and (T(1), k(m), ADC) may emerge as particularly useful for characterization of early cartilage degradation.

Words of wisdom. Re: Retrospective analysis of prostate cancer recurrence potential with tissue metabolomic profiles. Maxeiner A, Adkins CB, Zhang Y, et al. Prostate 2010;70:710-7

The authors analysed the value of metabolomic profiles to predict biochemical progression (BP) risk in patients with prostate cancer (PCa) submitted to radical prostatectomy(RP). They retrospectively analysed 183 PCa cases submitted to RP, using an intact-tissue magnetic resonance spectroscopy(MRS) method. A high-resolution, magic-angle, spinning proton MRS was used to investigate a metabolomic analysis of still-intact prostate tissue. This method allows study of chemical metabolites in their native state and control of intact tissue spectra, preserving the tissue for pathologic analysis. In this study, metabolomic profiles analysed by MRS could differentiate cases of PCa with BP after RP from clinical and pathologic-stage matched cases without BP.The major contributing factors to the observed metabolomic profiles were changes in spermine polyamines,phosphoryl choline, glutamine, inositol, and glutamate. The overall accuracy for predicting BP based on metabolomic profiles ranged from 71% to 78%. The suggestion is that PCa metabolic profiles studied at RP with MRS may provide available adjunct parameter to predict the risk of progression after surgery.