Pattern recognition of <sup>31</sup>P magnetic resonance spectroscopy tumour spectra obtained <i>in vivo</i>

Howells, Sían; Maxwell, Ross J.; Howe, Franklyn A.; Peet, Andrew C.; Stubbs, Marion; Rodrigues, L. M.; Robinson, Simon P.; Baluch, S.; Griffiths, John R.

doi:10.1002/nbm.1940060402

Cited by 27 publications

(8 citation statements)

References 3 publications

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“…Using 31 P spectra of four types of tumours (Morris hepatoma, GH3 prolactinoma, RIF-1, MNU-induced mammary) and three types of normal tissue (brain, liver, skeletal muscle), cluster analysis and artificial neural networks correctly classified 62% of the tissues, while only 7% were assigned incorrectly with the rest being partly correct or not assignable. 123 Similar success rates were obtained from excised tissue specimen classified using 1 H spectra. 124,125 Recently, Preul et al 126 have classified and staged various brain tumours (glioblastoma grade II, III, IV, meningioma, metastases) in patients based on 1 H spectra.…”

Section: Oncologysupporting

confidence: 51%

In vivo magnetic resonance methods in pharmaceutical research: current status and perspectives

et al. 1999

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Section: Oncologysupporting

confidence: 51%

In vivo magnetic resonance methods in pharmaceutical research: current status and perspectives

et al. 1999

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“…More recently they have been applied to the analysis of 'H MR spectra of tissue ex vivo (4) and in vivo (11) and 31P MR spectra of tumors in vivo (12).…”

Section: Introductionmentioning

confidence: 99%

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

Wallace

Raaphorst

Somorjai

et al. 1997

Magnetic Resonance in Med

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Proton (1H) magnetic resonance (MR) spectra of ex vivo biopsy samples of ovarian cancers provided biochemical information that was used to discriminate cancer from normal ovarian tissue. Possible differences present in intrinsically resistant tumors or changes in biochemistry after the induction of resistance were identified. Using multivariate techniques, in particular linear discriminant analysis (LDA), ovarian cancer was distinguished from normal ovarian tissue with a sensitivity of 100%, a specificity of 95% and an accuracy of 98%. Moreover, LDA was able to distinguish untreated ovarian cancer from recurrent ovarian cancer with a sensitivity of 92%, a specificity of 100%, and an accuracy of 97%; removal of the single "fuzzy" specimen increased the accuracy to 100%. Applications of this knowledge to in vivo measurements could lead to noninvasive diagnosis of ovarian cancer.

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“…By using cluster analysis, Howells et al reported a 62% of global success in the classification of in vivo 31 P-MRS spectra of tumors implanted in animals (53). In a more recent study, Nadal et al analyzed double extracts of 10 meningiomas and 10 glioblastomas in vitro by 1 H-MRS and 31 P-MRS, and grouped the data with multiple correspondences and ascending hierarchical classification methods.…”

Section: Classification Of Intracranial Tumors By Multivariate Discrimentioning

confidence: 98%

Assessment of ³¹P‐NMR analysis of phospholipid profiles for potential differential diagnosis of human cerebral tumors

Castaño¹,

Cerdán²,

Pascual³

et al. 2009

NMR in Biomedicine

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We describe a novel protocol for the non‐histological diagnosis of human brain tumors in vitro combining high‐resolution 31P magnetic resonance spectroscopy (31P‐MRS) of their phospholipid profile and statistical multivariate analysis. Chloroform/methanol extracts from 40 biopsies of human intracranial tumors obtained during neurosurgical procedures were prepared and analyzed by high‐resolution 31P‐MRS. The samples were grouped in the following seven major classes: normal brain (n = 3), low‐grade astrocytomas (n = 4), high‐grade astrocytomas (n = 7), meningiomas (n = 9), schwannomas (n = 3), pituitary adenomas (n = 4), and metastatic tumors (n = 4). The phospholipid profile of every biopsy was determined by 31P‐NMR analysis of its chloroform/methanol extract and characterized by 19 variables including 10 individual phospholipid contributions and 9 phospholipid ratios. Most tumors depicted a decrease in phosphatidylethanolamine (PtdEtn) and phosphatidylserine (PtdSer), the former mainly in neuroepithelial neoplasms and the latter in metastases. An increase in phosphatidylcholine (PtdCho) and phosphatidylinositol (PtdIns) appeared predominantly in primary non‐neuroepithelial tumors. Linear discriminant analysis (LDA) revealed the optimal combination of variables that could classify each biopsy between every pair of classes. The resultant discriminant functions were used to calculate the probability of correct classifications for each individual biopsy within the seven classes considered. Multilateral analysis classified correctly 100% of the normal brain samples, 89% of the meningiomas, 75% of the metastases, and 57% of the high‐grade astrocytomas. The use of phospholipid profiles may complement appropriately previously proposed methods of intelligent diagnosis of human cerebral tumors. Copyright © 2009 John Wiley & Sons, Ltd.

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Pattern recognition of ³¹P magnetic resonance spectroscopy tumour spectra obtained in vivo

Cited by 27 publications

References 3 publications

In vivo magnetic resonance methods in pharmaceutical research: current status and perspectives

In vivo magnetic resonance methods in pharmaceutical research: current status and perspectives

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

Assessment of ³¹P‐NMR analysis of phospholipid profiles for potential differential diagnosis of human cerebral tumors

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Pattern recognition of 31P magnetic resonance spectroscopy tumour spectra obtained in vivo

Cited by 27 publications

References 3 publications

In vivo magnetic resonance methods in pharmaceutical research: current status and perspectives

In vivo magnetic resonance methods in pharmaceutical research: current status and perspectives

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

Assessment of 31P‐NMR analysis of phospholipid profiles for potential differential diagnosis of human cerebral tumors

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Product

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Pattern recognition of ³¹P magnetic resonance spectroscopy tumour spectra obtained in vivo

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

Assessment of ³¹P‐NMR analysis of phospholipid profiles for potential differential diagnosis of human cerebral tumors