RANCM: a new ranking scheme for assigning confidence levels to metabolite assignments in NMR-based metabolomics studies.

INTRODUCTION: The Metabolomics Standards Initiative has recommended four categories for metabolite assignments in NMR-based metabolic profiling studies. The "putatively annotated compound" category is most commonly reported by metabolomics investigators. However, there is significant ambiguity in reliability of "putatively annotated compound" assignments, which can range from low confidence made on minimal corroborating data to high confidence made on substantial corroborating data. OBJECTIVES: To introduce a new ranking system, Rank and AssigN Confidence to Metabolites (RANCM), to assign confidence levels to "putatively annotated compound" assignments in NMR-based metabolic profiling studies. METHODS: The ranking system was constructed with three confidence levels ranging from Rank 1 for the lowest confidence assignment level to Rank 3 for the highest confidence assignment level. A decision tree was constructed to guide rank selection for each metabolite assignment. RESULTS: Examples are provided from experimental data demonstrating how to use the decision tree to make confidence level assignments to "putatively annotated compounds" in each of the three rank levels. A standard Excel sheet template is provided to facilitate decision-making, documentation and submission to data repositories. CONCLUSION: RANCM is intended to reduce the ambiguity in "putatively annotated compound" assignments, to facilitate effective communication of the degree of confidence in "putatively annotated compound" assignments, and to make it easier for non-experts to evaluate the significance and reliability of NMR-based metabonomics studies. The system is straightforward to implement, based on the most common datasets collected in NMR-based metabolic profiling studies, and can be used with equal rigor and significance with any set of NMR datasets.

Conventions and nomenclature for double diffusion encoding NMR and MRI.

Stejskal and Tanner's ingenious pulsed field gradient design from 1965 has made diffusion NMR and MRI the mainstay of most studies seeking to resolve microstructural information in porous systems in general and biological systems in particular. Methods extending beyond Stejskal and Tanner's design, such as double diffusion encoding (DDE) NMR and MRI, may provide novel quantifiable metrics that are less easily inferred from conventional diffusion acquisitions. Despite the growing interest on the topic, the terminology for the pulse sequences, their parameters, and the metrics that can be derived from them remains inconsistent and disparate among groups active in DDE. Here, we present a consensus of those groups on terminology for DDE sequences and associated concepts. Furthermore, the regimes in which DDE metrics appear to provide microstructural information that cannot be achieved using more conventional counterparts (in a model-free fashion) are elucidated. We highlight in particular DDE's potential for determining microscopic diffusion anisotropy and microscopic fractional anisotropy, which offer metrics of microscopic features independent of orientation dispersion and thus provide information complementary to the standard, macroscopic, fractional anisotropy conventionally obtained by diffusion MR. Finally, we discuss future vistas and perspectives for DDE.

Classification of single-voxel 1H spectra of brain tumours using LCModel.

This study presents a novel method for the direct classification of (1)H single-voxel MR brain tumour spectra using the widespread analysis tool LCModel. LCModel is designed to estimate individual metabolite proportions by fitting a linear combination of in vitro metabolite spectra to an in vivo MR spectrum. In this study, it is used to fit representations of complete tumour spectra and to perform a classification according to the highest estimated tissue proportion. Each tumour type is represented by two spectra, a mean component and a variability term, as calculated using a principal component analysis of a training dataset. In the same manner, a mean component and a variability term for normal white matter are also added into the analysis to allow a mixed tissue approach. An unbiased evaluation of the method is carried out through the automatic selection of training and test sets using the Kennard and Stone algorithm, and a comparison of LCModel classification results with those of the INTERPRET Decision Support System (IDSS) which incorporates an advanced pattern recognition method. In a test set of 46 spectra comprising glioblastoma multiforme, low-grade gliomas and meningiomas, LCModel gives a classification accuracy of 90% compared with an accuracy of 95% by IDSS.

Application of HR-MAS NMR in the solid-phase synthesis of a glycopeptide using Sieber amide resin.

The solid-phase synthesis (SPS) of a structurally complex glycopeptide, using Sieber amide resin, was monitored by high resolution magic angle spinning NMR, demonstrating the further application of this technique. A synthetic peptidoglycan derivative, a precursor of a biologically active PGN, known to be involved in the cellular recognition, was prepared by SPS. The synthesis involved the preparation of an N-alloc glucosamine moiety and the synthesis of a simple amino acid sequence L-Ala-D-Glu-L-Lys-D-Ala-D-Ala. Last step consisted the coupling, on solid-phase, of the protected muramyl unit to the peptide chain. Proton spectra with good suppression of the polystyrene signals in swollen resin samples were obtained in DMF-d(7) as a solvent and by using a nonselective 1D TOCSY/DIPSI-2 scheme, thus allowing to follow the SPS without losses of compound and cleavage from the resin. The assignment of the proton spectra of the resin-bound amino acid sequence and of the bound glycopeptide was achieved through the combination of MAS COSY, TOCSY and NOESY.

Unsupervised feature dimension reduction for classification of MR spectra.

We present an unsupervised feature dimension reduction method for the classification of magnetic resonance spectra. The technique preserves spectral information, important for disease profiling. We propose to use this technique as a preprocessing step for computationally demanding wrapper-based feature subset selection. We show that the classification accuracy on an independent test set can be sustained while achieving considerable feature reduction. Our method is applicable to other classification techniques, such as neural networks, support vector machines, etc.

Orthogonal subspace projection-based approaches to classification of MR image sequences.

Orthogonal subspace projection (OSP) approach has shown success in hyperspectral image classification. Recently, the feasibility of applying OSP to multispectral image classification was also demonstrated via SPOT (Satellite Pour 1'Observation de la Terra) and Landsat (Land Satellite) images. Since an MR (magnetic resonance) image sequence is also acquired by multiple spectral channels (bands), this paper presents a new application of OSP in MR image classification. The idea is to model an MR image pixel in the sequence as a linear mixture of substances (such as white matter, gray matter, cerebral spinal fluid) of interest from which each of these substances can be classified by a specific subspace projection operator followed by a desired matched filter. The experimental results show that OSP provides a promising alternative to existing MR image classification techniques.

Fuzzy pre-processing of gold standards as applied to biomedical spectra classification.

Fuzzy gold standard adjustment is a novel fuzzy set theoretic pre-processing strategy that compensates for the possible imprecision of a well-established gold standard (reference test) by adjusting, if necessary, the class labels in the design set while maintaining the gold standard's discriminatory power. The adjusted gold standard incorporates robust within-class centroid information. This strategy was applied to biomedical data acquired from a MR spectrometer for the purpose of classifying human brain neoplasms. It is shown that consistent improvement (10-13%) to the discriminatory power of the underlying classifier is obtained when using this pre-processing strategy.

Focal brain lesions: effect of single-voxel proton MR spectroscopic findings on treatment decisions.

PURPOSE: To determine the influence of single-voxel proton magnetic resonance (MR) spectroscopic findings on the treatment of patients suspected of having a brain tumor. MATERIALS AND METHODS: Medical records were reviewed in 78 patients who underwent MR spectroscopy for evaluation of a focal brain mass suspected of being neoplastic. MR spectroscopic findings were positive for neoplasm in 49 patients and negative in 29. Treatment with or without performance of biopsy was noted. In patients with positive findings who underwent irradiation or chemotherapy without biopsy and in patients with negative findings who were treated medically or followed up for interval changes, MR spectroscopy was classified as having a potential positive influence on treatment. In patients with positive findings with subsequently proved nonneoplastic lesions and in patients with negative findings with subsequently proved tumors, MR spectroscopy was classified as having a potential negative influence. RESULTS: MR spectroscopy in eight (16%) patients with positive findings and in 15 (52%) patients with negative findings had a potential positive influence on treatment. In two (3%) patients, MR spectroscopy had a potential negative influence. CONCLUSION: MR spectroscopy may play a beneficial role in the management of suspected brain tumors. Prospective studies are needed to test the effect of MR spectroscopy on clinical practice and to measure costs and benefits.

Computerized consensus diagnosis: a classification strategy for the robust analysis of MR spectra. I. Application to 1H spectra of thyroid neoplasms.

We introduce and apply a new classification strategy we call computerized consensus diagnosis (CCD). Its purpose is to provide robust, reliable classification of biomedical data. The strategy involves the cross-validated training of several classifiers of diverse conceptual and methodological origin on the same data, and appropriately combining their outcomes. The strategy is tested on proton magnetic resonance spectra of human thyroid biopsies, which are successfully allocated to normal or carcinoma classes. We used Linear Discriminant Analysis, a Neural Net-based method, and Genetic Programming as independent classifiers on two spectral regions, and chose the median of the six classification outcomes as the consensus. This procedure yielded 100% specificity and 100% sensitivity on the training sets, and 100% specificity and 98% sensitivity on samples of known malignancy in the test sets. We discuss the necessary steps any classification approach must take to guarantee reliability, and stress the importance of fuzziness and undecidability in robust classification.