David E. Axelson

Multivariate modeling and prediction of breast cancer prognostic factors using MR metabolomics.

Axillary lymph node status together with estrogen and progesterone receptor status are important prognostic factors in breast cancer. In this study, the potential of using MR metabolomics for prediction of these prognostic factors was evaluated. Biopsies from breast cancer patients (n = 160) were excised during surgery and analyzed by high resolution magic angle spinning MR spectroscopy (HR MAS MRS). The spectral data were preprocessed and variable stability (VAST) scaled, and training and test sets were generated using the Kennard-Stone and SPXY sample selection algorithms. The data were analyzed by partial least-squares discriminant analysis (PLS-DA), probabilistic neural networks (PNNs) and Bayesian belief networks (BBNs), and blind samples (n = 50) were predicted for verification. Estrogen and progesterone receptor status was successfully predicted from the MR spectra, and were best predicted by PLS-DA with a correct classification of 44 of 50 and 39 of 50 samples, respectively. Lymph node status was best predicted by BBN with 34 of 50 samples correctly classified, indicating a relationship between metabolic profile and lymph node status. Thus, MR profiles contain prognostic information that may be of benefit in treatment planning, and MR metabolomics may become an important tool for diagnosis of breast cancer patients.

Predicting survival and early clinical response to primary chemotherapy for patients with locally advanced breast cancer using DCE-MRI

To evaluate dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) as a tool for early prediction of response to neoadjuvant chemotherapy (NAC) and 5‐year survival in patients with locally advanced breast cancer.

13C NMR pattern recognition techniques for the classification of Atlantic salmon (Salmo salar L.) according to their wild, farmed, and geographical origin.

(13)C nuclear magnetic resonance (NMR) in combination with multivariate data analysis was used to (1) discriminate between farmed and wild Atlantic salmon ( Salmo salar L.), (2) discriminate between different geographical origins, and (3) verify the origin of market samples. Muscle lipids from 195 Atlantic salmon of known origin (wild and farmed salmon from Norway, Scotland, Canada, Iceland, Ireland, the Faroes, and Tasmania) in addition to market samples were analyzed by (13)C NMR spectroscopy and multivariate analysis. Both probabilistic neural networks (PNN) and support vector machines (SVM) provided excellent discrimination (98.5 and 100.0%, respectively) between wild and farmed salmon. Discrimination with respect to geographical origin was somewhat more difficult, with correct classification rates ranging from 82.2 to 99.3% by PNN and SVM, respectively. In the analysis of market samples, five fish labeled and purchased as wild salmon were classified as farmed salmon (indicating mislabeling), and there were also some discrepancies between the classification and the product declaration with regard to geographical origin.

Water distribution in brine salted cod (Gadus morhua) and salmon (Salmo salar): a low-field 1H NMR study.

Low-field (LF) (1)H NMR T 2 relaxation measurements were used to study changes in water distribution in lean (Atlantic cod) and fatty (Atlantic salmon) fish during salting in 15% NaCl and 25% NaCl brines. The NMR data were treated by PCA, continuous distribution analysis, and biexponential fitting and compared with physicochemical data. Two main water pools were observed in unsalted fish, T 21, with relaxation times in the range 20-100 ms, and T 22, with relaxation times in the range 100-300 ms. Pronounced changes in T 2 relaxation data were observed during salting, revealing changes in the water properties. Salting in 15% brine lead to a shift toward longer relaxation times, reflecting increased water mobility, whereas, salting in saturated brines had the opposite effect. Water mobility changes were observed earlier in the salting process for cod compared to salmon. Good linear correlations ( F </= 0.05) were found between T 2 parameters and water holding capacity, centrifugation loss, water activity, and salt content in the liquid phase for all fish groups. Fillet pH and total weight changes correlated linearly with T 2 parameters for some of the fish groups.

Applications of neural network analyses to in vivo 1H magnetic resonance spectroscopy of Parkinson disease patients

To apply neural network analyses to in vivo magnetic resonance spectra of controls and Parkinson disease (PD) patients for the purpose of classification.

Solid‐state nuclear magnetic resonance relaxation times in crosslinked macroporous polymer particles of divinylbenzene homopolymers

Monodisperse porous particles of poly(divinylbenzene) prepared by the activated swelling method have been investigated by solid-state 13 C crosspolarization magic-angle spinning (CPMAS) nuclear magnetic resonance (NMR) relaxation measurements. Homopolymeric combinations of two porogens (toluene and 2-ethylhexanoic acid) and two monomers (meta- and para-divinylbenzene) were studied. Residual vinyl groups were systematically reacted with increasing amounts of bromine, producing 20 different polymers samples for which we measured crosspolarization times, T CH , proton rotating frame spin-lattice relaxation, T 1ρ H , 13 C spin-lattice relaxation, T 1 C , and proton spin-lattice relaxation, T 1 H . These parameters were chosen to reflect expected changes in a wide range of frequencies of motion as a function of structure. Relative differences in the molecular mobility of the major functional groups (aromatic, vinyl and aliphatic) is related to initial reactants used, vinyl concentration, relative reactivity of vinyl groups, distribution of vinyl groups, pore structure, and degree of crosslinking. Variable temperature 1 H combined rotation and multiple pulse NMR (CRAMPS) was used to derive activation energies for selected samples via measurement of the proton spin-lattice relaxation time, T 1 H . Irreversible thermal effects were observed in ambient temperature relaxation after heating to temperatures in the range of 393-418 K. Simple univariate statistical analyses failed to reveal consistent correlations among the known variables. However, the application of more sophisticated multivariate and neural network analyses allowed excellent structure-property predictions to be made from the relaxation time data.

Combining clinical assessment scores and in vivo MR spectroscopy neurometabolites in very low birth weight adolescents

OBJECTIVE Very low birth weight (VLBW) survivors are at increased risk of neurological impairments that may persist into adolescence and adulthood. The aims of this study were to identify the most important clinical assessments that characterize differences between VLBW and control adolescents, and to look at the relationship between clinical assessments and the metabolites in in vivo MR spectra. METHODS At 14-15 years of age, 54 VLBW survivors and 64 term controls were examined clinically. Several neuropsychological and motor assessments were performed. The magnetic resonance (MR) brain spectra were acquired from volumes localized in the left frontal lobe and contained mainly white matter. RESULTS Probabilistic neural networks and support vector machines demonstrated that clinical assessments rendered a possibility of the classification of VLBW versus control adolescents. The most important clinical assessments in this classification were visual-motor integration, motor coordination, stroop test, full scale IQ, and grooved pegboard. Through the use of outer product analysis-partial least squares discriminant analysis on a subset of adolescents (n=36), the clinical assessments found to most strongly correlate with the spectral data were the global assessment scale, Wisconsin card sorting test, full scale IQ, grooved pegboard test, and motor coordination test. Clinical assessments that relate to spectral data may be especially dependent on an intact microstructure in frontal white matter.