In vivo magnetic resonance spectroscopy: basic methodology and clinical applications

The clinical use of in vivo magnetic resonance spectroscopy (MRS) has been limited for a long time, mainly due to its low sensitivity. However, with the advent of clinical MR systems with higher magnetic field strengths such as 3 Tesla, the development of better coils, and the design of optimized radio-frequency pulses, sensitivity has been considerably improved. Therefore, in vivo MRS has become a technique that is routinely used more and more in the clinic. In this review, the basic methodology of in vivo MRS is described—mainly focused on 1H MRS of the brain—with attention to hardware requirements, patient safety, acquisition methods, data post-processing, and quantification. Furthermore, examples of clinical applications of in vivo brain MRS in two interesting fields are described. First, together with a description of the major resonances present in brain MR spectra, several examples are presented of deviations from the normal spectral pattern associated with inborn errors of metabolism. Second, through examples of MR spectra of brain tumors, it is shown that MRS can play an important role in oncology

Neoadjuvant chemotherapy in breast cancer: early response prediction with quantitative MR imaging and spectroscopy.

A prospective study was undertaken in women undergoing neoadjuvant chemotherapy for locally advanced breast cancer in order to determine the ability of quantitative magnetic resonance imaging (MRI) and proton spectroscopy (MRS) to predict ultimate tumour response (percentage decrease in volume) or to detect early response. Magnetic resonance imaging and MRS were carried out before treatment and after the second of six treatment cycles. Pharmacokinetic parameters were derived from T1-weighted dynamic contrast-enhanced MRI, water apparent diffusion coefficient (ADC) was measured, and tissue water:fat peak area ratios and water T2 were measured using unsuppressed one-dimensional proton spectroscopic imaging (30 and 135 ms echo times). Pharmacokinetic parameters and ADC did not detect early response; however, early changes in water:fat ratios and water T2 (after cycle two) demonstrated substantial prognostic efficacy. Larger decreases in water T2 accurately predicted final volume response in 69% of cases (11/16) while maintaining 100% specificity and positive predictive value. Small/absent decreases in water:fat ratios accurately predicted final volume non-response in 50% of cases (3/6) while maintaining 100% sensitivity and negative predictive value. This level of accuracy might permit clinical application where early, accurate prediction of non-response would permit an early change to second-line treatment, thus sparing patients unnecessary toxicity, psychological morbidity and delay of initiation of effective treatment

The added value of quantitative multi-voxel MR spectroscopy in breast magnetic resonance imaging

To determine whether quantitative multivoxel MRS improves the accuracy of MRI in the assessment of breast lesions. Twenty-five consecutive patients with 26 breast lesions a parts per thousand yen1 cm assessed as BI-RADS 3 or 4 with mammography underwent quantitative multivoxel MRS and contrast-enhanced MRI. The choline (Cho) concentration was calculated using the unsuppressed water signal as a concentration reference. ROC analysis established the diagnostic accuracy of MRI and MRS in the assessment of breast lesions. Respective Cho concentrations in 26 breast lesions re-classified by MRI as BI-RADS 2 (n = 5), 3 (n = 8), 4 (n = 5) and 5 (n = 8) were 1.16 +/- 0.43 (mean +/- SD), 1.43 +/- 0.47, 2.98 +/- 2.15 and 4.94 +/- 3.10 mM. Two BI-RADS 3 lesions and all BI-RADS 4 and 5 lesions were malignant on histopathology and had Cho concentrations between 1.7 and 11.8 mM (4.03 +/- 2.72 SD), which were significantly higher (P = 0.01) than that in the 11 benign lesions (0.4-1.5 mM; 1.19 +/- 0.33 SD). Furthermore, Cho concentrations in the benign and malignant breast lesions in BI-RADS 3 category differed (P = 0.01). The accuracy of combined multivoxel MRS/breast MRI BI-RADS re-classification (AUC = 1.00) exceeded that of MRI alone (AUC = 0.96 +/- 0.03). These preliminary data indicate that multivoxel MRS improves the accuracy of MRI when using a Cho concentration cut-off a parts per thousand currency sign1.5 mM for benign lesions. Key Points aEuro cent Quantitative multivoxel MR spectroscopy can improve the accuracy of contrast-enhanced breast MRI. aEuro cent Multivoxel-MRS can differentiate breast lesions by using the highest Cho-concentration. aEuro cent Multivoxel-MRS can exclude patients with benign breast lesions from further invasive diagnostic procedures

17-allyamino-17-demethoxygeldanamycin treatment results in a magnetic resonance spectroscopy-detectable elevation in choline-containing metabolites associated with increased expression of choline transporter SLC44A1 and phospholipase A2

Abstract Introduction 17-allyamino-17-demethoxygeldanamycin (17-AAG), a small molecule inhibitor of Hsp90, is currently in clinical trials in breast cancer. However, 17-AAG treatment often results in inhibition of tumor growth rather than shrinkage, making detection of response a challenge. Magnetic resonance spectroscopy (MRS) and spectroscopic imaging (MRSI) are noninvasive imaging methods than can be used to monitor metabolic biomarkers of drug-target modulation. This study set out to examine the MRS-detectable metabolic consequences of Hsp90 inhibition in a breast cancer model. Methods MCF-7 breast cancer cells were investigated, and MRS studies were performed both on live cells and on cell extracts. 31P and 1H MRS were used to determine total cellular metabolite concentrations and 13C MRS was used to probe the metabolism of [1,2-13C]-choline. To explain the MRS metabolic findings, microarray and RT-PCR were used to analyze gene expression, and in vitro activity assays were performed to determine changes in enzymatic activity following 17-AAG treatment. Results Treatment of MCF-7 cells with 17-AAG for 48 hours caused a significant increase in intracellular levels of choline (to 266 ± 18% of control, P = 0.05) and phosphocholine (PC; to 181 ± 10% of control, P = 0.001) associated with an increase in expression of choline transporter SLC44A1 and an elevation in the de novo synthesis of PC. We also detected an increase in intracellular levels of glycerophosphocholine (GPC; to 176 ± 38% of control, P = 0.03) associated with an increase in PLA2 expression and activity. Conclusions This study determined that in the MCF-7 breast cancer model inhibition of Hsp90 by 17-AAG results in a significant MRS-detectable increase in choline, PC and GPC, which is likely due to an increase in choline transport into the cell and phospholipase activation. 1H MRSI can be used in the clinical setting to detect levels of total choline-containing metabolite (t-Cho, composed of intracellular choline, PC and GPC). As Hsp90 inhibitors enter routine clinical use, t-Cho could thus provide an easily detectable, noninvasive metabolic biomarker of Hsp90 inhibition in breast cancer patients

Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles

Saliva is a readily accessible and informative biofluid, making it ideal for the early detection of a wide range of diseases including cardiovascular, renal, and autoimmune diseases, viral and bacterial infections and, importantly, cancers. Saliva-based diagnostics, particularly those based on metabolomics technology, are emerging and offer a promising clinical strategy, characterizing the association between salivary analytes and a particular disease. Here, we conducted a comprehensive metabolite analysis of saliva samples obtained from 215 individuals (69 oral, 18 pancreatic and 30 breast cancer patients, 11 periodontal disease patients and 87 healthy controls) using capillary electrophoresis time-of-flight mass spectrometry (CE-TOF-MS). We identified 57 principal metabolites that can be used to accurately predict the probability of being affected by each individual disease. Although small but significant correlations were found between the known patient characteristics and the quantified metabolites, the profiles manifested relatively higher concentrations of most of the metabolites detected in all three cancers in comparison with those in people with periodontal disease and control subjects. This suggests that cancer-specific signatures are embedded in saliva metabolites. Multiple logistic regression models yielded high area under the receiver-operating characteristic curves (AUCs) to discriminate healthy controls from each disease. The AUCs were 0.865 for oral cancer, 0.973 for breast cancer, 0.993 for pancreatic cancer, and 0.969 for periodontal diseases. The accuracy of the models was also high, with cross-validation AUCs of 0.810, 0.881, 0.994, and 0.954, respectively. Quantitative information for these 57 metabolites and their combinations enable us to predict disease susceptibility. These metabolites are promising biomarkers for medical screening