Hierarchical non‐negative matrix factorization (hNMF): a tissue pattern differentiation method for glioblastoma multiforme diagnosis using MRSI

MRSI has shown potential in the diagnosis and prognosis of glioblastoma multiforme (GBM) brain tumors, but its use is limited by difficult data interpretation. When the analyzed MRSI data present more than two tissue patterns, conventional non‐negative matrix factorization (NMF) implementation may lead to a non‐robust estimation. The aim of this article is to introduce an effective approach for the differentiation of GBM tissue patterns using MRSI data. A hierarchical non‐negative matrix factorization (hNMF) method that can blindly separate the most important spectral sources in short‐TE ¹H MRSI data is proposed. This algorithm consists of several levels of NMF, where only two tissue patterns are computed at each level. The method is demonstrated on both simulated and in vivo short‐TE ¹H MRSI data in patients with GBM. For the in vivo study, the accuracy of the recovered spectral sources was validated using expert knowledge. Results show that hNMF is able to accurately estimate the three tissue patterns present in the tumoral and peritumoral area of a GBM, i.e. normal, tumor and necrosis, thus providing additional useful information that can help in the diagnosis of GBM. Moreover, the hNMF results can be displayed as easily interpretable maps showing the contribution of each tissue pattern to each voxel. Copyright © 2012 John Wiley & Sons, Ltd.     

Nosologic imaging of the brain: segmentation and classification using MRI and MRSI

A new technique is presented to create nosologic images of the brain based on magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging (MRSI). A nosologic image summarizes the presence of different tissues and lesions in a single image by color coding each voxel or pixel according to the histopathological class it is assigned to. The proposed technique applies advanced methods from image processing as well as pattern recognition to segment and classify brain tumors. First, a registered brain atlas and a subject‐specific abnormal tissue prior, obtained from MRSI data, are used for the segmentation. Next, the detected abnormal tissue is classified based on supervised pattern recognition methods. Class probabilities are also calculated for the segmented abnormal region. Compared to previous approaches, the new framework is more flexible and able to better exploit spatial information leading to improved nosologic images. The combined scheme offers a new way to produce high‐resolution nosologic images, representing tumor heterogeneity and class probabilities, which may help clinicians in decision making. Copyright © 2008 John Wiley & Sons, Ltd.     

Automated tissue segmentation and blind recovery of (1)H MRS imaging spectral patterns of normal and diseased human brain

Constrained non-negative matrix factorization (cNMF) with iterative data selection is described and demonstrated as a data analysis method for fast and automatic recovery of biochemically meaningful and diagnostically specific spectral patterns of the human brain from (1)H MRS imaging ((1)H MRSI) data. To achieve this goal, cNMF decomposes in vivo multidimensional (1)H MRSI data into two non-negative matrices representing (a) the underlying tissue-specific spectral patterns and (b) the spatial distribution of the corresponding metabolite concentrations. Central to the proposed approach is automatic iterative data selection which uses prior knowledge about the spatial distribution of the spectra to remove voxels that are due to artifacts and undesired metabolites/tissues such as the strong lipid and water components. The automatic recovery of diagnostic spectral patterns is demonstrated for long-TE (1)H MRSI data on normal human brain, multiple sclerosis, and serial brain tumor. The results show the ability of cNMF with iterative data selection to automatically and simultaneously recover tissue-specific spectral patterns and achieve segmentation of normal and diseased human brain tissue, concomitant with simplification of information content. These features of cNMF, which permit rapid recovery, reduction and interpretation of the complex diagnostic information content of large multi-dimensional spectroscopic imaging data sets, have the potential to enhance the clinical utility of in vivo(1)H MRSI.     

Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification

The purpose of this paper is to evaluate the effect of the combination of magnetic resonance spectroscopic imaging (MRSI) data and magnetic resonance imaging (MRI) data on the classification result of four brain tumor classes. Suppressed and unsuppressed short echo time MRSI and MRI were performed on 24 patients with a brain tumor and four volunteers. Four different feature reduction procedures were applied to the MRSI data: simple quantitation, principal component analysis, independent component analysis and LCModel. Water intensities were calculated from the unsuppressed MRSI data. Features were extracted from the MR images which were acquired with four different contrasts to comply with the spatial resolution of the MRSI. Evaluation was performed by investigating different combinations of the MRSI features, the MRI features and the water intensities. For each data set, the isolation in feature space of the tumor classes, healthy brain tissue and cerebrospinal fluid was calculated and visualized. A test set was used to calculate classification results for each data set. Finally, the effect of the selected feature reduction procedures on the MRSI data was investigated to ascertain whether it was more important than the addition of MRI information. Conclusions are that the combination of features from MRSI data and MRI data improves the classification result considerably when compared with features obtained from MRSI data alone. This effect is larger than the effect of specific feature reduction procedures on the MRSI data. The addition of water intensities to the data set also increases the classification result, although not significantly. We show that the combination of data from different MR investigations can be very important for brain tumor classification, particularly if a large number of tumors are to be classified simultaneously.     

Magnetic resonance spectroscopic imaging of tumor metabolic markers for cancer diagnosis, metabolic phenotyping, and characterization of tumor microenvironment

Cancer cells display heterogeneous genetic characteristics, depending on the tumor dynamic microenvironment. Abnormal tumor vasculature and poor tissue oxygenation generate a fraction of hypoxic tumor cells that have selective advantages in metastasis and invasion and often resist chemo- and radiation therapies. The genetic alterations acquired by tumors modify their biochemical pathways, which results in abnormal tumor metabolism. An elevation in glycolysis known as the "Warburg effect" and changes in lipid synthesis and oxidation occur. Magnetic resonance spectroscopy (MRS) has been used to study tumor metabolism in preclinical animal models and in clinical research on human breast, brain, and prostate cancers. This technique can identify specific genetic and metabolic changes that occur in malignant tumors. Therefore, the metabolic markers, detectable by MRS, not only provide information on biochemical changes but also define different metabolic tumor phenotypes. When combined with the contrast-enhanced Magnetic Resonance Imaging (MRI), which has a high sensitivity for cancer diagnosis, in vivo magnetic resonance spectroscopic imaging (MRSI) improves the diagnostic specificity of malignant human cancers and is becoming an important clinical tool for cancer management and care. This article reviews the MRSI techniques as molecular imaging methods to detect and quantify metabolic changes in various tumor tissue types, especially in extracranial tumor tissues that contain high concentrations of fat. MRI/MRSI methods have been used to characterize tumor microenvironments in terms of blood volume and vessel permeability. Measurements of tissue oxygenation and glycolytic rates by MRS also are described to illustrate the capability of the MR technology in probing molecular information non-invasively in tumor tissues and its important potential for studying molecular mechanisms of human cancers in physiological conditions.     

Dimethyl sulfoxide (DMSO) as a potential contrast agent for brain tumors

Dimethyl sulfoxide (DMSO) is commonly used in preclinical studies of animal models of high‐grade glioma as a solvent for chemotherapeutic agents. A strong DMSO signal was detected by single‐voxel MRS in the brain of three C57BL/6 control mice during a pilot study of DMSO tolerance after intragastric administration. This led us to investigate the accumulation and wash‐out kinetics of DMSO in both normal brain parenchyma (n = 3 control mice) by single‐voxel MRS, and in 12 GL261 glioblastomas (GBMs) by single‐voxel MRS (n = 3) and MRSI (n = 9). DMSO accumulated differently in each tissue type, reaching its highest concentration in tumors: 6.18 ± 0.85 µmol/g water, 1.5‐fold higher than in control mouse brain (p < 0.05). A faster wash‐out was detected in normal brain parenchyma with respect to GBM tissue: half‐lives of 2.06 ± 0.58 and 4.57 ± 1.15 h, respectively. MRSI maps of time‐course DMSO changes revealed clear hotspots of differential spatial accumulation in GL261 tumors. Additional MRSI studies with four mice bearing oligodendrogliomas (ODs) revealed similar results as in GBM tumors. The lack of T₁ contrast enhancement post‐gadolinium (gadopentetate dimeglumine, Gd‐DTPA) in control mouse brain and mice with ODs suggested that DMSO was fully able to cross the intact blood–brain barrier in both normal brain parenchyma and in low‐grade tumors. Our results indicate a potential role for DMSO as a contrast agent for brain tumor detection, even in those tumors ‘invisible’ to standard gadolinium‐enhanced MRI, and possibly for monitoring heterogeneities associated with progression or with therapeutic response. Copyright © 2012 John Wiley & Sons, Ltd.     

Characterization of oligodendrogliomas using short echo time 1H MR spectroscopic imaging

Oligodendroglial tumors may not be distinguished easily from other brain tumors based on clinical presentation and magnetic resonance imaging (MRI) alone. Identification of these tumors however may have therapeutic consequences. The purpose of this study was to characterize and identify oligodendrogliomas by their metabolic profile as measured by (1)H MR spectroscopic imaging (MRSI). Fifteen patients with oligodendroglial tumors (eight high-grade oligodendrogliomas, seven low-grade oligodendrogliomas) underwent MRI and short echo time (1)H MRSI examinations. Five main metabolites found in brain MR spectra were quantified and expressed as ratios of tumor to contralateral white matter tissue. The level of lipids plus lactate was also assessed in the tumor. For comparison six patients with a low grade astrocytoma were also included in the study. The metabolic profile of oligodendrogliomas showed a decreased level of N-acetylaspartate and increased levels of choline-containing compounds and glutamine plus glutamate compared with white matter. The level of glutamine plus glutamate was significantly higher in low-grade oligodendrogliomas than in low-grade astrocytomas and may serve as a metabolic marker in diagnosis and treatment planning. In high-grade oligodendrogliomas large resonances of lipids plus lactate were observed in contrast to low-grade tumors.     

Spatio‐spectral regularization to improve magnetic resonance spectroscopic imaging quantification

Magnetic resonance spectroscopic imaging (MRSI) is a non‐invasive technique able to provide the spatial distribution of relevant biochemical compounds commonly used as biomarkers of disease. Information provided by MRSI can be used as a valuable insight for the diagnosis, treatment and follow‐up of several diseases such as cancer or neurological disorders. Obtaining accurate metabolite concentrations from in vivo MRSI signals is a crucial requirement for the clinical utility of this technique. Despite the numerous publications on the topic, accurate quantification is still a challenging problem due to the low signal‐to‐noise ratio of the data, overlap of spectral lines and the presence of nuisance components. We propose a novel quantification method, which alleviates these limitations by exploiting a spatio‐spectral regularization scheme. In contrast to previous methods, the regularization terms are not expressed directly on the parameters being sought, but on appropriate transformed domains. In order to quantify all signals simultaneously in the MRSI grid, while introducing prior information, a fast proximal optimization algorithm is proposed. Experiments on synthetic MRSI data demonstrate that the error in the estimated metabolite concentrations is reduced by a mean of 41% with the proposed scheme. Results on in vivo brain MRSI data show the benefit of the proposed approach, which is able to fit overlapping peaks correctly and to capture metabolites that are missed by single‐voxel methods due to their lower concentrations. Copyright © 2016 John Wiley & Sons, Ltd.     

Fast nosological imaging using canonical correlation analysis of brain data obtained by two-dimensional turbo spectroscopic imaging

A new fast and accurate tissue typing technique has recently been successfully applied to prostate MR spectroscopic imaging (MRSI) data. This technique is based on canonical correlation analysis (CCA), a statistical method able to simultaneously exploit the spectral and spatial information characterizing the MRSI data. Here, the performance of CCA is further investigated by using brain data obtained by two-dimensional turbo spectroscopic imaging (2DTSI) from patients affected by glioblastoma. The purpose of this study is to investigate the applicability of CCA when typing tissues of heterogeneous tumors. The performance of CCA is also compared with that of ordinary correlation analysis on simulated as well as in vivo data. The results show that CCA outperforms ordinary correlation analysis in terms of robustness and accuracy.     

Perturbation of mouse glioma MRS pattern by induced acute hyperglycemia

(1)H MRS is evolving into an invaluable tool for brain tumor classification in humans based on pattern recognition analysis, but there is still room for improvement. Here we propose a new approach: to challenge tumor metabolism in vivo by a defined perturbation, and study the induced changes in MRS pattern. For this we recorded single voxel (1)H MR spectra from mice bearing a stereotactically induced GL261 grade IV brain glioma during a period of induced acute hyperglycemia. A total of 29 C57BL/6 mice were used. Single voxel spectra were acquired at 7 T with point resolved spectroscopy and TE of 12, 30 and 136 ms. Tumors were induced by stereotactic injection of 10(5) GL261cells in 17 mice. Hyperglycemia (up to 338 +/- 36 mg/dL glucose in the blood) was induced by intraperitoneal bolus injection. Maximal increases in glucose resonances of up to 2.4-fold were recorded from tumors in vivo. Our observations are in agreement with extracellular accumulation of glucose, which may suggest that glucose transport and/or metabolism are working close to their maximum capacity in GL261 tumors. The significant and specific MRS pattern changes observed when comparing euglycemia and hyperglycemia may be of use for future pattern-recognition studies of animal and human brain tumors by enhancing MRS-based discrimination between tumor types and grades.     

Quality control of prostate 1 H MRSI data

MRSI of prostate cancer provides a potential clinical tool to aid in the detection and characterisation of this disease, but its clinical use is limited by the need for the specialist training of radiologists to read these datasets. An essential part of this reading is the assessment of the usability and reliability of MRSI spectra because they can be affected by artefacts such as poor signal to noise, lipid signal contamination and broad resonances that could cause errors of interpretation. We have developed an automated quality control algorithm that classifies every voxel of an MRSI dataset as either acceptable or unacceptable for further analysis, based on the spectral profile alone. The method was trained and tested based on a gold standard of agreement of four experts. It was highly accurate: testing with a novel set of data from MRSI patients produced agreement with the experts' consensus decisions with a specificity of 0.95 and sensitivity of 0.95. This method provides fast quality control of three‐dimensional MRSI datasets of the prostate, removing the need for radiologists to perform this time consuming, but necessary, task prior to further analysis. Copyright © 2012 John Wiley & Sons, Ltd.     

Non‐water‐suppressed short‐echo‐time magnetic resonance spectroscopic imaging using a concentric ring k‐space trajectory

Water‐suppressed MRS acquisition techniques have been the standard MRS approach used in research and for clinical scanning to date. The acquisition of a non‐water‐suppressed MRS spectrum is used for artefact correction, reconstruction of phased‐array coil data and metabolite quantification. Here, a two‐scan metabolite‐cycling magnetic resonance spectroscopic imaging (MRSI) scheme that does not use water suppression is demonstrated and evaluated. Specifically, the feasibility of acquiring and quantifying short‐echo (T_E = 14 ms), two‐dimensional stimulated echo acquisition mode (STEAM) MRSI spectra in the motor cortex is demonstrated on a 3 T MRI system. The increase in measurement time from the metabolite‐cycling is counterbalanced by a time‐efficient concentric ring k‐space trajectory. To validate the technique, water‐suppressed MRSI acquisitions were also performed for comparison. The proposed non‐water‐suppressed metabolite‐cycling MRSI technique was tested for detection and correction of resonance frequency drifts due to subject motion and/or hardware instability, and the feasibility of high‐resolution metabolic mapping over a whole brain slice was assessed. Our results show that the metabolite spectra and estimated concentrations are in agreement between non‐water‐suppressed and water‐suppressed techniques. The achieved spectral quality, signal‐to‐noise ratio (SNR) > 20 and linewidth <7 Hz allowed reliable metabolic mapping of five major brain metabolites in the motor cortex with an in‐plane resolution of 10 × 10 mm² in 8 min and with a Cramér‐Rao lower bound of less than 20% using LCModel analysis. In addition, the high SNR of the water peak of the non‐water‐suppressed technique enabled voxel‐wise single‐scan frequency, phase and eddy current correction. These findings demonstrate that our non‐water‐suppressed metabolite‐cycling MRSI technique can perform robustly on 3 T MRI systems and within a clinically feasible acquisition time.     

Quantification of prostate MRSI data by model-based time domain fitting and frequency domain analysis

This paper compares two spectral processing methods for obtaining quantitative measures from in vivo prostate spectra, evaluates their effectiveness, and discusses the necessary modifications for accurate results. A frequency domain analysis (FDA) method based on peak integration was compared with a time domain fitting (TDF) method, a model-based nonlinear least squares fitting algorithm. The accuracy of both methods at estimating the choline + creatine + polyamines to citrate ratio (CCP:C) was tested using Monte Carlo simulations, empirical phantom MRSI data and in vivo MRSI data. The paper discusses the different approaches employed to achieve the quantification of the overlapping choline, creatine and polyamine resonances. Monte Carlo simulations showed induced biases on the estimated CCP:C ratios. Both methods were successful in identifying tumor tissue, provided that the CCP:C ratio was greater than a given (normal) threshold. Both methods predicted the same voxel condition in 94% of the in vivo voxels (68 out of 72). Both TDF and FDA methods had the ability to identify malignant voxels in an artifact-free case study using the estimated CCP:C ratio. Comparing the ratios estimated by the TDF and the FDA, the methods predicted the same spectrum type in 17 out of 18 voxels of the in vivo case study (94.4%).     

Using spatial prior knowledge in the spectral fitting of MRS images

We propose a Bayesian smoothness prior in the spectral fitting of MRS images which can be used in addition to commonly employed prior knowledge. By combining a frequency-domain model for the free induction decay with a Gaussian Markov random field prior, a new optimization objective is derived that encourages smooth parameter maps. Using a particular parameterization of the prior, smooth damping, frequency and phase maps can be obtained whilst preserving sharp spatial features in the amplitude map. A Monte Carlo study based on two sets of simulated data demonstrates that the variance of the estimated parameter maps can be reduced considerably, even below the Cramér-Rao lower bound, when using spatial prior knowledge. Long-TE (1)H MRSI at 1.5 T of a patient with a brain tumor shows that the use of the spatial prior resolves the overlapping peaks of choline and creatine when a single voxel method fails to do so. Improved and detailed metabolic maps can be derived from high-spatial-resolution, short-TE (1)H MRSI at 3 T. Finally, the evaluation of four series of long-TE brain MRSI data with various signal-to-noise ratios shows the general benefit of the proposed approach.     

Image-fusion of MR spectroscopic images for treatment planning of gliomas

1H magnetic resonance spectroscopic imaging (MRSI) can improve the accuracy of target delineation for gliomas, but it lacks the anatomic resolution needed for image fusion. This paper presents a simple protocol for fusing simulation computer tomography (CT) and MRSI images for glioma intensity-modulated radiotherapy (IMRT), including a retrospective study of 12 patients. Each patient first underwent whole-brain axial fluid-attenuated-inversion-recovery (FLAIR) MRI (3 mm slice thickness, no spacing), followed by three-dimensional (3D) MRSI measurements (TE/TR: 144/1000 ms) of a user-specified volume encompassing the extent of the tumor. The nominal voxel size of MRSI ranged from 8 x 8 x 10 mm3 to 12 x 12 x 10 mm3. A system was developed to grade the tumor using the choline-to-creatine (Cho/Cr) ratios from each MRSI voxel. The merged MRSI images were then generated by replacing the Cho/Cr value of each MRSI voxel with intensities according to the Cho/Cr grades, and resampling the poorer-resolution Cho/Cr map into the higher-resolution FLAIR image space. The FUNCTOOL processing software was also used to create the screen-dumped MRSI images in which these data were overlaid with each FLAIR MRI image. The screen-dumped MRSI images were manually translated and fused with the FLAIR MRI images. Since the merged MRSI images were intrinsically fused with the FLAIR MRI images, they were also registered with the screen-dumped MRSI images. The position of the MRSI volume on the merged MRSI images was compared with that of the screen-dumped MRSI images and was shifted until agreement was within a predetermined tolerance. Three clinical target volumes (CTVs) were then contoured on the FLAIR MRI images corresponding to the Cho/Cr grades. Finally, the FLAIR MRI images were fused with the simulation CT images using a mutual-information algorithm, yielding an IMRT plan that simultaneously delivers three different dose levels to the three CTVs. The image-fusion protocol was tested on 12 (six high-grade and six low-grade) glioma patients. The average agreement of the MRSI volume position on the screen-dumped MRSI images and the merged MRSI images was 0.29 mm with a standard deviation of 0.07 mm. Of all the voxels with Cho/Cr grade one or above, the distribution of Cho/Cr grade was found to correlate with the glioma grade from pathologic finding and is consistent with literature results indicating Cho/Cr elevation as a marker for malignancy. In conclusion, an image-fusion protocol was developed that successfully incorporates MRSI information into the IMRT treatment plan for glioma.     

Diffuse reflectance spectroscopy for in vivo pediatric brain tumor detection

The concept of using diffuse reflectance spectroscopy to distinguish intraoperatively between pediatric brain tumors and normal brain parenchyma at the edge of resection cavities is evaluated using an in vivo human study. Diffuse reflectance spectra are acquired from normal and tumorous brain areas of 12 pediatric patients during their tumor resection procedures, using a spectroscopic system with a handheld optical probe. A total of 400 spectra are acquired at the rate of 33 Hz from a single investigated site, from which the mean spectrum and the standard deviation are calculated. The mean diffuse reflectance spectra collected are divided into the normal and the tumorous categories in accordance with their corresponding results of histological analysis. Statistical methods are used to identify those spectral features that effectively separated the two tissue categories, and to quantify the spectral variations induced by the motion of the handheld probe during a single spectral acquisition procedure. The results show that diffuse reflectance spectral intensities between 600 and 800 nm are effective in terms of differentiating normal cortex from brain tumors. Furthermore, probe movements induce large variations in spectral intensities (i.e., larger standard deviation) between 400 and 600 nm.     

Fast data acquisition techniques in magnetic resonance spectroscopic imaging

Magnetic resonance spectroscopic imaging (MRSI) is an important technique for assessing the spatial variation of metabolites in vivo. The long scan times in MRSI limit clinical applicability due to patient discomfort, increased costs, motion artifacts, and limited protocol flexibility. Faster acquisition strategies can address these limitations and could potentially facilitate increased adoption of MRSI into routine clinical protocols with minimal addition to the current anatomical and functional acquisition protocols in terms of imaging time. Not surprisingly, a lot of effort has been devoted to the development of faster MRSI techniques that aim to capture the same underlying metabolic information (relative metabolite peak areas and spatial distribution) as obtained by conventional MRSI, in greatly reduced time. The gain in imaging time results, in some cases, in a loss of signal‐to‐noise ratio and/or in spatial and spectral blurring. This review examines the current techniques and advances in fast MRSI in two and three spatial dimensions and their applications. This review categorizes the acceleration techniques according to their strategy for acquisition of the k‐space. Techniques such as fast/turbo‐spin echo MRSI, echo‐planar spectroscopic imaging, and non‐Cartesian MRSI effectively cover the full k‐space in a more efficient manner per T_R. On the other hand, techniques such as parallel imaging and compressed sensing acquire fewer k‐space points and employ advanced reconstruction algorithms to recreate the spatial‐spectral information, which maintains statistical fidelity in test conditions (ie no statistically significant differences on voxel‐wise comparisions) with the fully sampled data. The advantages and limitations of each state‐of‐the‐art technique are reviewed in detail, concluding with a note on future directions and challenges in the field of fast spectroscopic imaging.     

Automatic quality control in clinical 1H MRSI of brain cancer

MRSI grids frequently show spectra with poor quality, mainly because of the high sensitivity of MRS to field inhomogeneities. These poor quality spectra are prone to quantification and/or interpretation errors that can have a significant impact on the clinical use of spectroscopic data. Therefore, quality control of the spectra should always precede their clinical use. When performed manually, quality assessment of MRSI spectra is not only a tedious and time‐consuming task, but is also affected by human subjectivity. Consequently, automatic, fast and reliable methods for spectral quality assessment are of utmost interest. In this article, we present a new random forest‐based method for automatic quality assessment of ¹H MRSI brain spectra, which uses a new set of MRS signal features. The random forest classifier was trained on spectra from 40 MRSI grids that were classified as acceptable or non‐acceptable by two expert spectroscopists. To account for the effects of intra‐rater reliability, each spectrum was rated for quality three times by each rater. The automatic method classified these spectra with an area under the curve (AUC) of 0.976. Furthermore, in the subset of spectra containing only the cases that were classified every time in the same way by the spectroscopists, an AUC of 0.998 was obtained. Feature importance for the classification was also evaluated. Frequency domain skewness and kurtosis, as well as time domain signal‐to‐noise ratios (SNRs) in the ranges 50–75 ms and 75–100 ms, were the most important features. Given that the method is able to assess a whole MRSI grid faster than a spectroscopist (approximately 3 s versus approximately 3 min), and without loss of accuracy (agreement between classifier trained with just one session and any of the other labelling sessions, 89.88%; agreement between any two labelling sessions, 89.03%), the authors suggest its implementation in the clinical routine. The method presented in this article was implemented in jMRUI's SpectrIm plugin. Copyright © 2016 John Wiley & Sons, Ltd.     

Intrinsic Near-Infrared Spectroscopic Markers of Breast Tumors

We have discovered quantitative optical biomarkers unique to cancer by developing a double-differential spectroscopic analysis method for near-infrared (NIR, 650–1000 nm) spectra acquired non-invasively from breast tumors. These biomarkers are characterized by specific NIR absorption bands. The double-differential method removes patient specific variations in molecular composition which are not related to cancer, and reveals these specific cancer biomarkers. Based on the spectral regions of absorption, we identify these biomarkers with lipids that are present in tumors either in different abundance than in the normal breast or new lipid components that are generated by tumor metabolism. Furthermore, the O-H overtone regions (980–1000 nm) show distinct variations in the tumor as compared to the normal breast. To quantify spectral variation in the absorption bands, we constructed the Specific Tumor Component (STC) index. In a pilot study of 12 cancer patients we found 100% sensitivity and 100% specificity for lesion identification. The STC index, combined with other previously described tissue optical indices, further improves the diagnostic power of NIR for breast cancer detection.