Deriving biomedical diagnostics from NMR spectroscopic data

Smith, Ian C. P.; Somorjai, Ray L.

doi:10.1007/s12551-011-0045-8

Cited by 3 publications

(5 citation statements)

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“…Recommended cohort size will depend somewhat on the nature of the data, but anything less than several hundred subjects, both healthy subjects and those with the condition of interest, would not yield a classifier that would be certified for use by a regulatory agency. A detailed discussion of these issues has appeared recently (176,177).In addition, validation of MR spectroscopy biomarkers for clinical use requires their incorporation in robust prospective multicenter clinical trials, where patient selection and treatment meets prespecified criteria and the statistical methodology is set before the trial commences. This requires careful MR spectroscopy protocol design that can be adhered to at all the participating centers.…”

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confidence: 99%

Clinical Proton MR Spectroscopy in Central Nervous System Disorders

Öz¹,

Alger²,

Barker³

et al. 2014

Radiology

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A large body of published work shows that proton (hydrogen 1 [ 1 H]) magnetic resonance (MR) spectroscopy has evolved from a research tool into a clinical neuroimaging modality. Herein, the authors present a summary of brain disorders in which MR spectroscopy has an impact on patient management, together with a critical consideration of common data acquisition and processing procedures. The article documents the impact of 1 H MR spectroscopy in the clinical evaluation of disorders of the central nervous system. The clinical usefulness of 1 H MR spectroscopy has been established for brain neoplasms, neonatal and pediatric disorders (hypoxia-ischemia, inherited metabolic diseases, and traumatic brain injury), demyelinating disorders, and infectious brain lesions. The growing list of disorders for which 1 H MR spectroscopy may contribute to patient management extends to neurodegenerative diseases, epilepsy, and stroke. To facilitate expanded clinical acceptance and standardization of MR spectroscopy methodology, guidelines are provided for data acquisition and analysis, quality assessment, and interpretation. Finally, the authors offer recommendations to expedite the use of robust MR spectroscopy methodology in the clinical setting, including incorporation of technical advances on clinical units.q RSNA, 2014 Online supplemental material is available for this article. G.O. (e-mail: gulin@cmrr.umn.edu). 2 The complete list of authors and affiliations is at the end of this article.q RSNA, 2014 Note: This copy is for your personal non-commercial use only. To order presentation-ready copies for distribution to your colleagues or clients, contact us at www.rsna.org/rsnarights. Radiology H MR Spectrum of the Brain: Metabolites and Their Biomarker PotentialMR spectroscopy provides a very different basic "readout" than MR imaging, namely a spectrum rather than an techniques were developed. These early localization techniques included pointresolved spectroscopy (PRESS) (1,2) and stimulated echo acquisition mode (STEAM) (3), methods that are now widely used in clinical MR spectroscopy applications.Preliminary studies revealed large differences in metabolite levels in acute stroke (4), chronic multiple sclerosis (5), and brain tumors compared with healthy brain (6). Although this work stimulated a surge of interest in 1 H MR spectroscopy for diagnosing and assessing CNS disorders during the early days of the "Decade of the Brain" (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999), many suboptimal patient studies (7) and the lack of consistent guidelines have led to a situation where, 20 years later, MR spectroscopy is still considered an "investigational technique" by some medical professionals and health care organizations. However, the ability to make an early, noninvasive diagnosis or to increase confidence in a suspected diagnosis is highly valued by patients and clinicians alike. As a result, an increasing number of imaging centers are incorporating MR spectroscopy into their clinical protocols for brain...

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mentioning

confidence: 99%

Clinical Proton MR Spectroscopy in Central Nervous System Disorders

Öz¹,

Alger²,

Barker³

et al. 2014

Radiology

Self Cite

540

356

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“…The lower accuracy solution will also be more robust: challenging the resultant classifier with new specimens will yield accuracy similar to that found by a reliable classifier. 5 and enhanced diagnostic potential of MR metabolomics. In a study by Li et al, 12 HRMAS 1 H MRS studies were performed on 31 breast tissue samples (13 cancer and 18 non-cancer) obtained by percutaneous core needle biopsy.…”

Section: Breast Cancermentioning

confidence: 99%

“…INTERPRET DSS (version 3.0) allows radiologists, medical physicists, biochemists or anyone with a minimum knowledge of what an MR spectrum is, to enter their own SV raw data, acquired at 1. 5 T, and to analyze them. 89 The system is expected to help in the categorization of MR spectra from abnormal brain masses.…”

Section: Gastric Cancermentioning

confidence: 99%

“…The lower accuracy solution will also be more robust: challenging the resultant classifier with new specimens will yield accuracy similar to that found by a reliable classifier. 5 …”

Section: Figurementioning

confidence: 99%

“… 4 A more robust and quantifiable approach is based on a statistical classification strategy (SCS). 5 It has the advantage of determining sensitivity, specificity and reliability of conclusion reached by MRS of potentially cancerous systems. We shall outline the methods and provide examples of their application, with a detailed description of the statistical methods in the Appendix .…”

Section: Introductionmentioning

confidence: 99%

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Mrs-based Metabolomics in Cancer Research

Bezabeh

Ijare

Nikulin

et al. 2014

Magn Reson�Insights

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Metabolomics is a relatively new technique that is gaining importance very rapidly. MRS-based metabolomics, in particular, is becoming a useful tool in the study of body fluids, tissue biopsies and whole organisms. Advances in analytical techniques and data analysis methods have opened a new opportunity for such technology to contribute in the field of diagnostics. In the MRS approach to the diagnosis of disease, it is important that the analysis utilizes all the essential information in the spectra, is robust, and is non-subjective. Although some of the data analytic methods widely used in chemical and biological sciences are sketched, a more extensive discussion is given of a 5-stage Statistical Classification Strategy. This proposes powerful feature selection methods, based on, for example, genetic algorithms and novel projection techniques. The applications of MRS-based metabolomics in breast cancer, prostate cancer, colorectal cancer, pancreatic cancer, hepatobiliary cancers, gastric cancer, and brain cancer have been reviewed. While the majority of these applications relate to body fluids and tissue biopsies, some in vivo applications have also been included. It should be emphasized that the number of subjects studied must be sufficiently large to ensure a robust diagnostic classification. Before MRS-based metabolomics can become a widely used clinical tool, however, certain challenges need to be overcome. These include manufacturing user-friendly commercial instruments with all the essential features, and educating physicians and medical technologists in the acquisition, analysis, and interpretation of metabolomics data.

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