Differentiation of glioblastomas and metastases using 1H-MRS spectral data

Hydrogen-1 magnetic resonance spectroscopy (1H-MRS) allows noninvasive in vivo quantification of metabolite concentrations in brain tissue. In this work two of the most aggressive brain tumors are studied with the purpose of differentiating them. The challenging aspect in this task resides in that their radiological appearance is often similar, despite the fact that treatment of patients suffering these conditions is quite different. Efforts to differentiate between these two profiles are getting increasing attention, mainly because the consequences of performing an incorrect diagnosis. Due to the high dimensionality, initiatives oriented to reduce the description complexity become important. In this work we present a feature selection algorithm that generates relevant subsets of spectral frequencies. Experimental results deliver models that are both simple in terms of numbers of frequencies and show good generalization capabilities.Postprint (author’s final draft

A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases

Machine learning has provided, over the last decades, tools for knowledge extraction in complex medical domains. Most of these tools, though, are ad hoc solutions and lack the systematic approach that would be required to become mainstream in medical practice. In this brief paper, we define a machine learning-based analysis pipeline for helping in a difficult problem in the field of neuro-oncology, namely the discrimination of brain glioblastomas from single brain metastases. This pipeline involves source extraction using k-Meansinitialized Convex Non-negative Matrix Factorization and a collection of classifiers, including Logistic Regression, Linear Discriminant Analysis, AdaBoost, and Random Forests.Peer ReviewedPostprint (published version

Classifying malignant brain tumours from 1H-MRS data using Breadth Ensemble Learning

In neuro oncology, the accurate diagnostic identification and characterization of tumours is paramount for determining their prognosis and the adequate course of treatment. This is usually a difficult problem per se, due to the localization of the tumour in an extremely sensitive and difficult to reach organ such as the brain. The clinical analysis of brain tumours often requires the use of non-invasive measurement methods, the most common of which resort to imaging techniques. The discrimination between high-grade malignant tumours of different origin but similar characteristics, such as glioblastomas and metastases, is a particularly difficult problem in this context. This is because imaging techniques are often not sensitive enough and their spectroscopic signal is overall too similar. In spite of this, machine learning techniques, coupled with robust feature selection procedures, have recently made substantial inroads into the problem. In this study, magnetic resonance spectroscopy data from an international, multicentre database were used to discriminate between these two types of malignant brain tumours using ensemble learning techniques, with a focus on the definition of a feature selection method specifically designed for ensembles. This method, Breadth Ensemble Learning, takes advantage of the fact that many of the frequencies of the available spectra convey no relevant information for the discrimination of the tumours. The potential of the proposed method is supported by some of the best results reported to date for this problem.Postprint (author's final draft

Spektroskopia rezonansu magnetycznego w wewnątrzczaszkowych nowotworach pochodzenia glejowego

Background and purpose To determine in vivo magnetic resonance spectroscopy (MRS) characteristics of intracranial glial tumours and to assess MRS reliability in glioma grading and discrimination between different histopathological types of tumours. Material and methods Analysis of spectra of 26 patients with glioblastomas, 6 with fibrillary astrocytomas, 4 with anaplastic astrocytomas, 2 with pilocytic astrocytoma, 3 with oligodendrogliomas, 3 with anaplastic oligodendrogliomas and 17 control spectra taken from healthy hemispheres. Results All tumours’ metabolite ratios, except for Cho/Cr in fibrillary astrocytomas (p = 0.06), were statistically signiflcantly different from the control. The tumours showed decreased Naa and Cr contents and a high Cho signal. The Lac-Lip signal was high in grade III astrocytomas and glioblastomas. Reports that Cho/Cr ratio increases with glioma's grade whereas Naa/Cr decreases were not confirmed. Anaplastic astrocytomas compared to grade II astrocytomas had a statistically significantly greater ml/Cr ratio (p = 0.02). In pilocytic astrocytomas the Naa/Cr value (2.58 ± 0.39) was greater, whilst the Cho/Naa ratio was lower (2.14 ± 0.64) than in the other astrocytomas. The specific feature of oligodendrogliomas was the presence of glutamate/glutamine peak Glx. However, this peak was absent in two out of three anaplastic oligodendrogliomas. Characteristically, the latter tumours had a high Lac-Lip signal. Conclusions MRS in vivo cannot be used as a reliable method for glioma grading. The method is useful in discrimination between WHO grade I and WHO grade II astrocytomas as well as oligodendrogliomas from other gliomas.Wstęp i cel pracy Ustalenie charakterystyki spektroskopii magnetycznego rezonansu jądrowego (magnetic resonance spectroscopy – MRS) u chorych z nowotworami wewnątrzczaszkowymi pochodzenia glejowego oraz ocena przydatności tego badania w diagnostyce różnicowej typów histologicznych glejaków. Materiał i metody Przeprowadzono analizę widm MRS nowotworów u 26 chorych z glejakami wielopostaciowymi, 6 z gwiaździakami włókienkowymi, 4 z gwiaździakami anaplastycznymi, 2 z włosowatokomórkowymi, 3 ze skąpodrzewiakami, 3 ze skąpodrzewiakami anaplastycznymi oraz 17 widm kontrolnych pochodzących ze zdrowych półkul mózgu. Wyniki Wszystkie wskaźniki metaboliczne w przypadkach nowotworów, z wyjątkiem Cho/Cr w gwiaździakach włókienkowych (p = 0,06), różniły się znamiennie od tych w grupie kontrolnej. Nowotwory wykazywały zmniejszoną zawartość Naa i Cr oraz wysoki sygnał Cho. Sygnał Lac-Lip był wysoki w gwiaździakach III stopnia wg WHO i glejakach wielopostaciowych. Nie udało się potwierdzić doniesień, że wskaźnik Cho/Cr rośnie, a wskaźnik Naa/Cr maleje wraz ze wzrostem stopnia złośliwości glejaka. Gwiaździaki anaplastyczne wykazywały znamiennie wyższy wskaźnik ml/Cr (p = 0,02) w porównaniu z gwiaździakami II stopnia wg WHO. W gwiaździakach włosowatokomórkowych wartość Naa/Cr (2,58 ± 0,39) była większa, a Cho/Naa mniejsza (2,14 ± 0,64) niż w innych gwiaździakach. Skąpodrzewiaki charakteryzowała obecność szczytu glutaminianu/glutaminy (Glx), którego jednak nie obserwowano w 2 spośród 3 przypadków skąpodrzewiaków anaplastycznych. Dla tych ostatnich symptomatyczna była obecność silnego sygnału Lac-Lip. Wnioski Badanie MRS in vivo nie jest niezawodną metodą różnicującą glejaki wewnątrzczaszkowe. Wydaje się użyteczne w diagnostyce różnicowej gwiaździaków I i II stopnia wg WHO oraz w odróżnianiu skąpodrzewiaków od pozostałych glejaków

Feature selection in proton magnetic resonance spectroscopy data of brain tumors

In cancer diagnosis, classification of the different tumor types is of great importance. An accurate prediction of different tumor types provides better treatment and may minimize the negative impact of incorrectly targeted toxic or aggressive treatments. Moreover, the correct prediction of cancer types using non-invasive information –e.g. 1H-MRS data– could avoid patients to suffer collateral problems derived from exploration techniques that require surgery. A Feature Selection Algorithm specially designed to be use in 1H-MRS Proton Magnetic Resonance Spectroscopy data of brain tumors is presented. It takes advantage of a highly distinctive aspect in this data: some metabolite levels are notoriously different between types of tumors. Experimental read- ings on an international dataset show highly competitive models in terms of accuracy, complexity and medical interpretability.Postprint (author’s final draft

A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data

BackgroundThe clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal.Methodology/Principal FindingsNon-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification.Conclusions/SignificanceWe show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source identification and brain tumor labeling from single-voxel spectroscopy data. We are confident that the proposed methodology has wider applicability for biomedical signal processing

A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases

Machine learning has provided, over the last decades, tools for knowledge extraction in complex medical domains. Most of these tools, though, are ad hoc solutions and lack the systematic approach that would be required to become mainstream in medical practice. In this brief paper, we define a machine learning-based analysis pipeline for helping in a difficult problem in the field of neuro-oncology, namely the discrimination of brain glioblastomas from single brain metastases. This pipeline involves source extraction using k-Meansinitialized Convex Non-negative Matrix Factorization and a collection of classifiers, including Logistic Regression, Linear Discriminant Analysis, AdaBoost, and Random Forests

Ex-vivo HRMAS of adult brain tumours: metabolite quantification and assignment of tumour biomarkers

Background: High-resolution magic angle spinning (HRMAS) NMR spectroscopy allows detailed metabolic analysis of whole biopsy samples for investigating tumour biology and tumour classification. Accurate biochemical assignment of small molecule metabolites that are “NMR visible” will improve our interpretation of HRMAS data and the translation of NMR tumour biomarkers to in-vivo studies. Results: 1D and 2D 1H HRMAS NMR was used to determine that 29 small molecule metabolites, along with 8 macromolecule signals, account for the majority of the HRMAS spectrum of the main types of brain tumour(astrocytoma grade II, grade III gliomas, glioblastomas, metastases, meningiomas and also lymphomas). Differences in concentration of 20 of these metabolites were statistically significant between these brain tumour types. During the course of an extended 2D data acquisition the HRMAS technique itself affects sample analysis: glycine, glutathione and glycerophosphocholine all showed small concentration changes; analysis of the sample after HRMAS indicated structural damage that may affect subsequent histopathological analysis. Conclusions: A number of small molecule metabolites have been identified as potential biomarkers of tumour type that may enable development of more selective in-vivo 1H NMR acquisition methods for diagnosis and prognosis of brain tumours

Magnetic resonance spectroscopy — Revisiting the biochemical and molecular milieu of brain tumors

AbstractBackgroundMagnetic resonance spectroscopy (MRS) is an established tool for in-vivo evaluation of the biochemical basis of human diseases. On one hand, such lucid depiction of ‘live biochemistry’ helps one to decipher the true nature of the pathology while on the other hand one can track the response to therapy at sub-cellular level. Brain tumors have been an area of continuous interrogation and instigation for mankind. Evaluation of these lesions by MRS plays a crucial role in the two aspects of disease management described above.Scope of reviewPresented is an overview of the window provided by MRS into the biochemical aspects of brain tumors. We systematically visit each metabolite deciphered by MRS and discuss the role of deconvoluting the biochemical aspects of pathologies (here in context of brain tumors) in the disease management cycle. We further try to unify a radiologist's perspective of disease with that of a biochemist to prove the point that preclinical work is the mother of the treatment we provide at bedside as clinicians. Furthermore, an integrated approach by various scientific experts help resolve a query encountered in everyday practice.Major conclusionsMR spectroscopy is an integral tool for evaluation and systematic follow-up of brain tumors. A deeper understanding of this technology by a biochemist would help in a swift and more logical development of the technique while a close collaboration with radiologist would enable definitive application of the same.General significanceThe review aims at inciting closer ties between the two specialists enabling a deeper understanding of this valuable technology