Classification of <sup>1</sup>H MR spectra of biopsies from untreated and recurrent ovarian cancer using linear discriminant analysis

Wallace, Joseph C.; Raaphorst, G. P.; Somorjai, Ray L.; Ng, Cho Ee; Fung, MK; Senterman, Mary K.; Smith, Ianc. P.

doi:10.1002/mrm.1910380411

Cited by 59 publications

(31 citation statements)

References 17 publications

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“…Motivations for application of the FPT were provided by the quite extensive data using in vitro MRS [30,54,[62][63][64][65][66][67][68][69]. Further, as thoroughly reviewed in Ref.…”

Section: In Vivo Encoded Mrs Time Signals Using Conventional Fourier-mentioning

confidence: 99%

Synergism of spectra averaging and extrapolation for quantification of in vivo MRS time signals encoded from the ovary

Belkić

2017

J Math Chem

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The fast Padé transform (FPT) is further optimized for encoded in vivo MRS time signals. This is achieved by a judicious combination of spectra averaging and time signal extrapolation. The motivation is in strengthening suppression of the over-sensitivity of signal processing to changes in model order K . Implementation is carried out in the FPT variant FPT (+) which, by numerically performed analytical continuation, converts divergent into convergent series. Convergence of reconstructions is monitored for a sequence of successive values of K . Comparison is made with the corresponding retrieval without spectra averaging and time signal extrapolation. Variances are dramatically reduced for the reconstructed parameters (complex frequencies and complex amplitudes) when spectra averaging and extrapolation are performed. Negligible variances imply convergence, which is accomplished herein using a single averaging procedure (no iterations). This has important implications in practice with encoded MRS data, providing remarkable efficiency, robustness and accuracy of Padé-based quantification. Algorithmically, spectra averaging and time signal extrapolation consist of four steps. First, the encoded time signal is used to compute total shape spectra (envelopes) for a sequence of model orders K , which is the number of resonances. Second, these envelopes are averaged (spectra averaging). All the spectra are computed at the same sweep frequencies whose number considerably exceeds the number of data points in the encoded time signal. Third, the complex average envelope is inverted to yield a new time signal which is longer than the encoded data (time signal extrapolation). Fourth, the Padé extrapolated time signal is quantified for a sequence of K to monitor convergence of the reconstructed parameters. All four steps are applied to in vivo MRS data encoded on a 3 T scanner from a borderline serous cystic ovarian tumor. We focus on this clinical problem to help develop effective methods for early detection of ovarian cancer, in order to improve survival for women afflicted with this malignancy. It is anticipated that the presently refined, multi-purpose Padé-based methodology with its practical advantages for in vivo MRS can contribute to this goal.

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“…Motivations for application of the FPT were provided by the quite extensive data using in vitro MRS [30,54,[62][63][64][65][66][67][68][69]. Further, as thoroughly reviewed in Ref.…”

Section: In Vivo Encoded Mrs Time Signals Using Conventional Fourier-mentioning

confidence: 99%

Synergism of spectra averaging and extrapolation for quantification of in vivo MRS time signals encoded from the ovary

Belkić

2017

J Math Chem

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“…These deficiencies in the method will introduce some extra variance into the data. They may be overcome by procedures not presently available in our laboratory (use of magnitude spectra and automated integration [22]). Other whole-organism fingerprinting techniques are reported to require strict control of growth media and repeated standardization with control cultures (11,12).…”

Section: Discussionmentioning

confidence: 99%

“…Pattern recognition techniques, which detect gross spectral characteristics associated with a priori-defined classes (such as pathological conditions), have been successfully applied to MRS of both tissues and body fluids. Accurate and reliable classifiers based on multivariate analyses of 1 H MR spectroscopic data have been developed and validated for objective diagnosis of thyroid (21), ovarian (22), prostate (9), breast (13), and brain (20) tumors. In some pathologies, MRS is able to detect malignancy before morphological manifestations are visible by light microscopy (17).…”

mentioning

confidence: 99%

Identification ofEnterococcus, Streptococcus, andStaphylococcusby Multivariate Analysis of Proton Magnetic Resonance Spectroscopic Data from Plate Cultures

Bourne

Himmelreich

Sharma³

et al. 2001

J Clin Microbiol

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A new fingerprinting technique with the potential for rapid identification of bacteria was developed by combining proton magnetic resonance spectroscopy ( 1 H MRS) with multivariate statistical analysis. This resulted in an objective identification strategy for common clinical isolates belonging to the bacterial species Staphylococcus aureus, Staphylococcus epidermidis, Enterococcus faecalis, Streptococcus pneumoniae, Streptococcus pyogenes, Streptococcus agalactiae, and the Streptococcus milleri group. Duplicate cultures of 104 different isolates were examined one or more times using 1 H MRS. A total of 312 cultures were examined. An optimized classifier was developed using a bootstrapping process and a seven-group linear discriminant analysis to provide objective classification of the spectra. Identification of isolates was based on consistent high-probability classification of spectra from duplicate cultures and achieved 92% agreement with conventional methods of identification. Fewer than 1% of isolates were identified incorrectly. Identification of the remaining 7% of isolates was defined as indeterminate.In both clinical and industrial laboratories, methods for identification of microorganisms have historically been based on multiple phenotypic characters, including morphological features and a range of biochemical reactions. These tests are often time-consuming and/or relatively expensive in their application, and some are imprecise. Recently, alternative methods have been investigated in an attempt to develop a single, rapid method for characterization and identification of microorganisms. These have included Fourier transform infrared spectroscopy (11,14), pyrolysis mass spectrometry (12), electrospray ionization mass spectrometry (7), UV resonance Raman spectroscopy (15), and protein electrophoresis (16). While reports of these techniques suggest the possibility of rapid and reliable identification of some groups of microorganisms, most have been tested with small data sets. With the exception of Fourier transform infrared spectroscopy, they are destructive techniques which analyze cellular decomposition products. All have the limitation that they do not directly yield information about the biochemistry of the intact viable organism.In contrast, magnetic resonance spectroscopy (MRS) of viable cells can provide information on a large range of metabolites. Biological applications of MRS most commonly exploit the noninvasive nature of the technique to study aspects of cellular biochemistry in living systems (6). However, not all applications of MRS require or include identification of the metabolites contributing to the MR spectrum. Pattern recognition techniques, which detect gross spectral characteristics associated with a priori-defined classes (such as pathological conditions), have been successfully applied to MRS of both tissues and body fluids. Accurate and reliable classifiers based on multivariate analyses of 1 H MR spectroscopic data have been developed and validated for objective diagnosis of t...

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“…It cannot reliably separate out the noise that corrupts the recorded MRS time signal, such that poor SNR is a major problem when using clinical MR scanners. Yet, there are many MR-observable compounds that characterize malignant versus benign ovarian lesions using in vitro MRS [50,[56][57][58][59][60][61][62].…”

Section: In Vivo Magnetic Resonance Spectroscopy Of the Ovary: The Prmentioning

confidence: 99%

“…In an investigation of 19 normal or benign ovarian samples, 3 with BL pathology and 37 ovarian cancers [57], amplitude ratios of peaks at 0.9 ppm (Lip methyl), 1.3 ppm (Lip methylene), 1.7 ppm (Lys and polyamines) and 3.2 ppm (Cho), distinguished normal or benign samples from BL and malignant ovarian samples with a sensitivity of 95 % and specificity of 86 %.…”

Section: In Vitro Mrs Findings From the Ovarymentioning

confidence: 99%

In vivo magnetic resonance spectroscopy for ovarian cancer diagnostics: quantification by the fast Padé transform

Belkić

2016

J Math Chem

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Early-stage ovarian cancer has an excellent prognosis, but due mainly to late detection, ovarian cancer remains a major cause of cancer deaths among women. In vivo magnetic resonance spectroscopy (MRS) would be an excellent candidate for early ovarian cancer detection, being non-invasive, surpassing anatomic imaging to identify metabolic features of cancer, and free of ionizing radiation. However, the present meta-analysis of 13 studies indicates that with conventional Fourier-based processing, in vivo MRS insufficiently distinguished 134 cancerous from 114 benign ovarian lesions. The fast Padé transform (FPT), an advanced signal processor with high-resolution and parametric (quantification-equipped) capabilities is best qualified for MRS time signals from the ovary, as demonstrated in our earlier proof-of-concept studies. We now apply the FPT to MRS time signals encoded in vivo on a 3 T scanner, echo time of 30 ms, from a borderline serous cystic ovarian tumor. The FPT-produced total shape spectrum was better resolved than with Fourier processing. Spectra averaging through the FPT generated a denoised total shape spectrum. Subsequent parametric analysis reconstructed dense component spectra in the "usual" mode: absorption and dispersion components mixed and "ersatz" mode: reconstructed phases set to zero, thus eliminating interference effects. Numerous metabolites, including potential cancer biomarkers, were identified and quantified by the FPT, including isoleucine, valine, lipids, lactate, alanine, lysine, N-acetyl aspartate, N-acetylneuraminic acid, gluta- mine, choline, phosphocholine, myoinositol. Many of these are difficult or impossible to detect with Fourier plus fitting techniques for in vivo MRS of the ovary. These Padé-generated results are promising, overcoming major barriers hindering MRS from becoming a key method for non-invasively assessing ovarian lesions.

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Classification of ¹H MR spectra of biopsies from untreated and recurrent ovarian cancer using linear discriminant analysis

Cited by 59 publications

References 17 publications

Synergism of spectra averaging and extrapolation for quantification of in vivo MRS time signals encoded from the ovary

Synergism of spectra averaging and extrapolation for quantification of in vivo MRS time signals encoded from the ovary

Identification ofEnterococcus, Streptococcus, andStaphylococcusby Multivariate Analysis of Proton Magnetic Resonance Spectroscopic Data from Plate Cultures

In vivo magnetic resonance spectroscopy for ovarian cancer diagnostics: quantification by the fast Padé transform

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Classification of 1H MR spectra of biopsies from untreated and recurrent ovarian cancer using linear discriminant analysis

Cited by 59 publications

References 17 publications

Synergism of spectra averaging and extrapolation for quantification of in vivo MRS time signals encoded from the ovary

Synergism of spectra averaging and extrapolation for quantification of in vivo MRS time signals encoded from the ovary

Identification ofEnterococcus, Streptococcus, andStaphylococcusby Multivariate Analysis of Proton Magnetic Resonance Spectroscopic Data from Plate Cultures

In vivo magnetic resonance spectroscopy for ovarian cancer diagnostics: quantification by the fast Padé transform

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Classification of ¹H MR spectra of biopsies from untreated and recurrent ovarian cancer using linear discriminant analysis