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Candiota AP, Arús C. Establishing Imaging Biomarkers of Host Immune System Efficacy during Glioblastoma Therapy Response: Challenges, Obstacles and Future Perspectives. Metabolites 2022; 12:metabo12030243. [PMID: 35323686 PMCID: PMC8950145 DOI: 10.3390/metabo12030243] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/04/2022] [Accepted: 03/10/2022] [Indexed: 11/16/2022] Open
Abstract
This hypothesis proposal addresses three major questions: (1) Why do we need imaging biomarkers for assessing the efficacy of immune system participation in glioblastoma therapy response? (2) Why are they not available yet? and (3) How can we produce them? We summarize the literature data supporting the claim that the immune system is behind the efficacy of most successful glioblastoma therapies but, unfortunately, there are no current short-term imaging biomarkers of its activity. We also discuss how using an immunocompetent murine model of glioblastoma, allowing the cure of mice and the generation of immune memory, provides a suitable framework for glioblastoma therapy response biomarker studies. Both magnetic resonance imaging and magnetic resonance-based metabolomic data (i.e., magnetic resonance spectroscopic imaging) can provide non-invasive assessments of such a system. A predictor based in nosological images, generated from magnetic resonance spectroscopic imaging analyses and their oscillatory patterns, should be translational to clinics. We also review hurdles that may explain why such an oscillatory biomarker was not reported in previous imaging glioblastoma work. Single shot explorations that neglect short-term oscillatory behavior derived from immune system attack on tumors may mislead actual response extent detection. Finally, we consider improvements required to properly predict immune system-mediated early response (1–2 weeks) to therapy. The sensible use of improved biomarkers may enable translatable evidence-based therapeutic protocols, with the possibility of extending preclinical results to human patients.
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Affiliation(s)
- Ana Paula Candiota
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, 08193 Barcelona, Spain;
- Departament de Bioquímica i Biologia Molecular, Unitat de Bioquímica de Biociències, Edifici Cs, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, 08193 Barcelona, Spain
- Institut de Biotecnologia i de Biomedicina (IBB), Universitat Autònoma de Barcelona, Cerdanyola del Vallès, 08193 Barcelona, Spain
| | - Carles Arús
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, 08193 Barcelona, Spain;
- Departament de Bioquímica i Biologia Molecular, Unitat de Bioquímica de Biociències, Edifici Cs, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, 08193 Barcelona, Spain
- Institut de Biotecnologia i de Biomedicina (IBB), Universitat Autònoma de Barcelona, Cerdanyola del Vallès, 08193 Barcelona, Spain
- Correspondence:
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Yang X, Han G, Cai H, Song Y. Recovering Hidden Diagonal Structures via Non-Negative Matrix Factorization with Multiple Constraints. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:1760-1772. [PMID: 28371782 DOI: 10.1109/tcbb.2017.2690282] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Revealing data with intrinsically diagonal block structures is particularly useful for analyzing groups of highly correlated variables. Earlier researches based on non-negative matrix factorization (NMF) have been shown to be effective in representing such data by decomposing the observed data into two factors, where one factor is considered to be the feature and the other the expansion loading from a linear algebra perspective. If the data are sampled from multiple independent subspaces, the loading factor would possess a diagonal structure under an ideal matrix decomposition. However, the standard NMF method and its variants have not been reported to exploit this type of data via direct estimation. To address this issue, a non-negative matrix factorization with multiple constraints model is proposed in this paper. The constraints include an sparsity norm on the feature matrix and a total variational norm on each column of the loading matrix. The proposed model is shown to be capable of efficiently recovering diagonal block structures hidden in observed samples. An efficient numerical algorithm using the alternating direction method of multipliers model is proposed for optimizing the new model. Compared with several benchmark models, the proposed method performs robustly and effectively for simulated and real biological data.
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Pedrosa de Barros N, Meier R, Pletscher M, Stettler S, Knecht U, Herrmann E, Schucht P, Reyes M, Gralla J, Wiest R, Slotboom J. On the relation between MR spectroscopy features and the distance to MRI-visible solid tumor in GBM patients. Magn Reson Med 2018; 80:2339-2355. [DOI: 10.1002/mrm.27359] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 04/23/2018] [Accepted: 04/23/2018] [Indexed: 11/12/2022]
Affiliation(s)
- Nuno Pedrosa de Barros
- University Institute for Diagnostic and Interventional Neuroradiology, University of Bern; Bern Switzerland
| | - Raphael Meier
- University Institute for Diagnostic and Interventional Neuroradiology, University of Bern; Bern Switzerland
| | - Martin Pletscher
- University Institute for Diagnostic and Interventional Neuroradiology, University of Bern; Bern Switzerland
| | - Samuel Stettler
- University Institute for Diagnostic and Interventional Neuroradiology, University of Bern; Bern Switzerland
| | - Urspeter Knecht
- University Institute for Diagnostic and Interventional Neuroradiology, University of Bern; Bern Switzerland
| | - Evelyn Herrmann
- Department of Radiation Oncology; University of Bern; Bern Switzerland
| | - Philippe Schucht
- Department of Neurosurgery; University of Bern; Bern Switzerland
| | - Mauricio Reyes
- Institute for Surgical Technology and Biomechanics, University of Bern; Bern Switzerland
| | - Jan Gralla
- University Institute for Diagnostic and Interventional Neuroradiology, University of Bern; Bern Switzerland
| | - Roland Wiest
- University Institute for Diagnostic and Interventional Neuroradiology, University of Bern; Bern Switzerland
| | - Johannes Slotboom
- University Institute for Diagnostic and Interventional Neuroradiology, University of Bern; Bern Switzerland
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Li Y, Liu X, Wei F, Sima DM, Van Cauter S, Himmelreich U, Pi Y, Hu G, Yao Y, Van Huffel S. An advanced MRI and MRSI data fusion scheme for enhancing unsupervised brain tumor differentiation. Comput Biol Med 2017; 81:121-129. [DOI: 10.1016/j.compbiomed.2016.12.017] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Revised: 12/09/2016] [Accepted: 12/26/2016] [Indexed: 01/12/2023]
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Bharath HN, Sima DM, Sauwen N, Himmelreich U, De Lathauwer L, Van Huffel S. Nonnegative Canonical Polyadic Decomposition for Tissue-Type Differentiation in Gliomas. IEEE J Biomed Health Inform 2016; 21:1124-1132. [PMID: 27429452 DOI: 10.1109/jbhi.2016.2583539] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Magnetic resonance spectroscopic imaging (MRSI) reveals chemical information that characterizes different tissue types in brain tumors. Blind source separation techniques are used to extract the tissue-specific profiles and their corresponding distribution from the MRSI data. We focus on automatic detection of the tumor, necrotic and normal brain tissue types by constructing a 3D MRSI tensor from in vivo 2D-MRSI data of individual glioma patients. Nonnegative canonical polyadic decomposition (NCPD) is applied to the MRSI tensor to differentiate various tissue types. An in vivo study shows that NCPD has better performance in identifying tumor and necrotic tissue type in glioma patients compared to previous matrix-based decompositions, such as nonnegative matrix factorization and hierarchical nonnegative matrix factorization.
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Affiliation(s)
- H N Bharath
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - D M Sima
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - N Sauwen
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - U Himmelreich
- Biomedical MRI Unit/Molecular Small Animal Imaging Center, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - L De Lathauwer
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - S Van Huffel
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
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Laudadio T, Croitor Sava AR, Sima DM, Wright AJ, Heerschap A, Mastronardi N, Van Huffel S. Hierarchical non-negative matrix factorization applied to three-dimensional 3 T MRSI data for automatic tissue characterization of the prostate. NMR IN BIOMEDICINE 2016; 29:751-758. [PMID: 27061522 DOI: 10.1002/nbm.3527] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2015] [Revised: 03/01/2016] [Accepted: 03/01/2016] [Indexed: 06/05/2023]
Abstract
In this study non-negative matrix factorization (NMF) was hierarchically applied to simulated and in vivo three-dimensional 3 T MRSI data of the prostate to extract patterns for tumour and benign tissue and to visualize their spatial distribution. Our studies show that the hierarchical scheme provides more reliable tissue patterns than those obtained by performing only one NMF level. We compared the performance of three different NMF implementations in terms of pattern detection accuracy and efficiency when embedded into the same kind of hierarchical scheme. The simulation and in vivo results show that the three implementations perform similarly, although one of them is more robust and better pinpoints the most aggressive tumour voxel(s) in the dataset. Furthermore, they are able to detect tumour and benign tissue patterns even in spectra with lipid artefacts. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Teresa Laudadio
- Istituto per le Applicazioni del Calcolo 'M. Picone' (IAC), Consiglio Nazionale delle Ricerche, Bari, Italy
| | - Anca R Croitor Sava
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
- iMinds Medical Information Technologies, Leuven, Belgium
| | - Diana M Sima
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
- iMinds Medical Information Technologies, Leuven, Belgium
| | - Alan J Wright
- Cancer Institute CRUK, University of Cambridge, Li Ka Shing Centre, Cambridge, UK
| | - Arend Heerschap
- Department of Radiology, Radboud University Nijmegen Medical Center, Nijmegen, Netherlands
| | - Nicola Mastronardi
- Istituto per le Applicazioni del Calcolo 'M. Picone' (IAC), Consiglio Nazionale delle Ricerche, Bari, Italy
| | - Sabine Van Huffel
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
- iMinds Medical Information Technologies, Leuven, Belgium
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Bharath HN, Sima DM, Sauwen N, Himmelreich U, De Lathauwer L, Van Huffel S. Tensor based tumor tissue type differentiation using magnetic resonance spectroscopic imaging. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:7003-6. [PMID: 26737904 DOI: 10.1109/embc.2015.7320004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Magnetic resonance spectroscopic imaging (MRSI) has the potential to characterise different tissue types in brain tumors. Blind source separation techniques are used to extract the specific tissue profiles and their corresponding distribution from the MRSI data. A 3-dimensional MRSI tensor is constructed from in vivo 2D-MRSI data of individual tumor patients. Non-negative canonical polyadic decomposition (NCPD) with common factor in mode-1 and mode-2 and l(1) regularization on mode-3 is applied on the MRSI tensor to differentiate various tissue types. Initial in vivo study shows that NCPD has better performance in identifying tumor and necrotic tissue type in high grade glioma patients compared to previous matrix-based decompositions, such as non-negative matrix factorization and hierarchical non-negative matrix factorization.
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Sauwen N, Sima DM, Van Cauter S, Veraart J, Leemans A, Maes F, Himmelreich U, Van Huffel S. Hierarchical non-negative matrix factorization to characterize brain tumor heterogeneity using multi-parametric MRI. NMR IN BIOMEDICINE 2015; 28:1599-1624. [PMID: 26458729 DOI: 10.1002/nbm.3413] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2015] [Revised: 08/17/2015] [Accepted: 08/24/2015] [Indexed: 06/05/2023]
Abstract
Tissue characterization in brain tumors and, in particular, in high-grade gliomas is challenging as a result of the co-existence of several intra-tumoral tissue types within the same region and the high spatial heterogeneity. This study presents a method for the detection of the relevant tumor substructures (i.e. viable tumor, necrosis and edema), which could be of added value for the diagnosis, treatment planning and follow-up of individual patients. Twenty-four patients with glioma [10 low-grade gliomas (LGGs), 14 high-grade gliomas (HGGs)] underwent a multi-parametric MRI (MP-MRI) scheme, including conventional MRI (cMRI), perfusion-weighted imaging (PWI), diffusion kurtosis imaging (DKI) and short-TE (1)H MRSI. MP-MRI parameters were derived: T2, T1 + contrast, fluid-attenuated inversion recovery (FLAIR), relative cerebral blood volume (rCBV), mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK) and the principal metabolites lipids (Lip), lactate (Lac), N-acetyl-aspartate (NAA), total choline (Cho), etc. Hierarchical non-negative matrix factorization (hNMF) was applied to the MP-MRI parameters, providing tissue characterization on a patient-by-patient and voxel-by-voxel basis. Tissue-specific patterns were obtained and the spatial distribution of each tissue type was visualized by means of abundance maps. Dice scores were calculated by comparing tissue segmentation derived from hNMF with the manual segmentation by a radiologist. Correlation coefficients were calculated between each pathologic tissue source and the average feature vector within the corresponding tissue region. For the patients with HGG, mean Dice scores of 78%, 85% and 83% were obtained for viable tumor, the tumor core and the complete tumor region. The mean correlation coefficients were 0.91 for tumor, 0.97 for necrosis and 0.96 for edema. For the patients with LGG, a mean Dice score of 85% and mean correlation coefficient of 0.95 were found for the tumor region. hNMF was also applied to reduced MRI datasets, showing the added value of individual MRI modalities.
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Affiliation(s)
- Nicolas Sauwen
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium
- iMinds Medical IT, Leuven, Belgium
| | - Diana M Sima
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium
- iMinds Medical IT, Leuven, Belgium
| | - Sofie Van Cauter
- Department of Radiology, University Hospitals of Leuven, Leuven, Belgium
| | - Jelle Veraart
- iMinds Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
- Center for Biomedical Imaging, Department of Radiology, New York University Langone Medical Center, New York, NY, USA
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Frederik Maes
- iMinds Medical IT, Leuven, Belgium
- KU Leuven, Department of Electrical Engineering (ESAT), PSI Centre for Processing Speech and Images, Leuven, Belgium
| | - Uwe Himmelreich
- Biomedical MRI/MoSAIC, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Sabine Van Huffel
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium
- iMinds Medical IT, Leuven, Belgium
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Li Y, Sima DM, Van Cauter S, Himmelreich U, Sava ARC, Pi Y, Liu Y, Van Huffel S. Corrections to “Unsupervised Nosologic Imaging for Glioma Diagnosis” [Jun 13 1760-1763]. IEEE Trans Biomed Eng 2015; 62:2091. [DOI: 10.1109/tbme.2015.2449931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Keunen O, Taxt T, Grüner R, Lund-Johansen M, Tonn JC, Pavlin T, Bjerkvig R, Niclou SP, Thorsen F. Multimodal imaging of gliomas in the context of evolving cellular and molecular therapies. Adv Drug Deliv Rev 2014; 76:98-115. [PMID: 25078721 DOI: 10.1016/j.addr.2014.07.010] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2014] [Revised: 07/14/2014] [Accepted: 07/22/2014] [Indexed: 01/18/2023]
Abstract
The vast majority of malignant gliomas relapse after surgery and standard radio-chemotherapy. Novel molecular and cellular therapies are thus being developed, targeting specific aspects of tumor growth. While histopathology remains the gold standard for tumor classification, neuroimaging has over the years taken a central role in the diagnosis and treatment follow up of brain tumors. It is used to detect and localize lesions, define the target area for biopsies, plan surgical and radiation interventions and assess tumor progression and treatment outcome. In recent years the application of novel drugs including anti-angiogenic agents that affect the tumor vasculature, has drastically modulated the outcome of brain tumor imaging. To properly evaluate the effects of emerging experimental therapies and successfully support treatment decisions, neuroimaging will have to evolve. Multi-modal imaging systems with existing and new contrast agents, molecular tracers, technological advances and advanced data analysis can all contribute to the establishment of disease relevant biomarkers that will improve disease management and patient care. In this review, we address the challenges of glioma imaging in the context of novel molecular and cellular therapies, and take a prospective look at emerging experimental and pre-clinical imaging techniques that bear the promise of meeting these challenges.
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Coquery N, Francois O, Lemasson B, Debacker C, Farion R, Rémy C, Barbier EL. Microvascular MRI and unsupervised clustering yields histology-resembling images in two rat models of glioma. J Cereb Blood Flow Metab 2014; 34:1354-62. [PMID: 24849664 PMCID: PMC4126096 DOI: 10.1038/jcbfm.2014.90] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2013] [Revised: 04/22/2014] [Accepted: 04/24/2014] [Indexed: 01/05/2023]
Abstract
Imaging heterogeneous cancer lesions is a real challenge. For diagnosis, histology often remains the reference, but it is widely acknowledged that biopsies are not reliable. There is thus a strong interest in establishing a link between clinical in vivo imaging and the biologic properties of tissues. In this study, we propose to construct histology-resembling images based on tissue microvascularization, a magnetic resonance imaging (MRI) accessible source of contrast. To integrate the large amount of information collected with microvascular MRI, we combined a manual delineation of a spatial region of interest with an unsupervised, model-based cluster analysis (Mclust). This approach was applied to two rat models of glioma (C6 and F98). Six MRI parameters were mapped: apparent diffusion coefficient, vessel wall permeability, cerebral blood volume fraction, cerebral blood flow, tissular oxygen saturation, and cerebral metabolic rate of oxygen. Five clusters, defined by their MRI features, were found to correspond to specific histologic features, and revealed intratumoral spatial structures. These results suggest that the presence of a cluster within a tumor can be used to assess the presence of a tissue type. In addition, the cluster composition, i.e., a signature of the intratumoral structure, could be used to characterize tumor models as histology does.
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Affiliation(s)
- Nicolas Coquery
- 1] INSERM, U836, Grenoble, France [2] Université Joseph Fourier, Grenoble, France
| | - Olivier Francois
- 1] Université Joseph Fourier, Grenoble, France [2] CNRS, UMR5525, TIMC-IMAG Laboratory, La Tronche, France
| | - Benjamin Lemasson
- 1] INSERM, U836, Grenoble, France [2] Université Joseph Fourier, Grenoble, France
| | - Clément Debacker
- 1] INSERM, U836, Grenoble, France [2] Université Joseph Fourier, Grenoble, France [3] Bruker Biospin MRI, Wissembourg, France
| | - Régine Farion
- 1] INSERM, U836, Grenoble, France [2] Université Joseph Fourier, Grenoble, France
| | - Chantal Rémy
- 1] INSERM, U836, Grenoble, France [2] Université Joseph Fourier, Grenoble, France
| | - Emmanuel Luc Barbier
- 1] INSERM, U836, Grenoble, France [2] Université Joseph Fourier, Grenoble, France
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Tsolaki E, Svolos P, Kousi E, Kapsalaki E, Fezoulidis I, Fountas K, Theodorou K, Kappas C, Tsougos I. Fast spectroscopic multiple analysis (FASMA) for brain tumor classification: a clinical decision support system utilizing multi-parametric 3T MR data. Int J Comput Assist Radiol Surg 2014; 10:1149-66. [PMID: 25024116 DOI: 10.1007/s11548-014-1088-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2013] [Accepted: 05/05/2014] [Indexed: 01/14/2023]
Abstract
INTRODUCTION A clinical decision support system (CDSS) for brain tumor classification can be used to assist in the diagnosis and grading of brain tumors. A Fast Spectroscopic Multiple Analysis (FASMA) system that uses combinations of multiparametric MRI data sets was developed as a CDSS for brain tumor classification. METHODS MRI metabolic ratios and spectra, from long and short TE, respectively, as well as diffusion and perfusion data were acquired from the intratumoral and peritumoral area of 126 patients with untreated intracranial tumors. These data were categorized based on the pathology, and different machine learning methods were evaluated regarding their classification performance for glioma grading and differentiation of infiltrating versus non-infiltrating lesions. Additional databases were embedded to the system, including updated literature values of the related MR parameters and typical tumor characteristics (imaging and histological), for further comparisons. Custom Graphical User Interface (GUI) layouts were developed to facilitate classification of the unknown cases based on the user's available MR data. RESULTS The highest classification performance was achieved with a support vector machine (SVM) using the combination of all MR features. FASMA correctly classified 89 and 79% in the intratumoral and peritumoral area, respectively, for cases from an independent test set. FASMA produced the correct diagnosis, even in the misclassified cases, since discrimination between infiltrative versus non-infiltrative cases was possible. CONCLUSIONS FASMA is a prototype CDSS, which integrates complex quantitative MR data for brain tumor characterization. FASMA was developed as a diagnostic assistant that provides fast analysis, representation and classification for a set of MR parameters. This software may serve as a teaching tool on advanced MRI techniques, as it incorporates additional information regarding typical tumor characteristics derived from the literature.
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Affiliation(s)
- Evangelia Tsolaki
- Medical Physics Department, Medical School, University of Thessaly, 41110 , Biopolis, Larissa, Greece
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Tsolaki E, Kousi E, Svolos P, Kapsalaki E, Theodorou K, Kappas C, Tsougos I. Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques. World J Radiol 2014; 6:72-81. [PMID: 24778769 PMCID: PMC4000611 DOI: 10.4329/wjr.v6.i4.72] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2013] [Revised: 01/23/2014] [Accepted: 03/18/2014] [Indexed: 02/06/2023] Open
Abstract
In recent years, advanced magnetic resonance imaging (MRI) techniques, such as magnetic resonance spectroscopy, diffusion weighted imaging, diffusion tensor imaging and perfusion weighted imaging have been used in order to resolve demanding diagnostic problems such as brain tumor characterization and grading, as these techniques offer a more detailed and non-invasive evaluation of the area under study. In the last decade a great effort has been made to import and utilize intelligent systems in the so-called clinical decision support systems (CDSS) for automatic processing, classification, evaluation and representation of MRI data in order for advanced MRI techniques to become a part of the clinical routine, since the amount of data from the aforementioned techniques has gradually increased. Hence, the purpose of the current review article is two-fold. The first is to review and evaluate the progress that has been made towards the utilization of CDSS based on data from advanced MRI techniques. The second is to analyze and propose the future work that has to be done, based on the existing problems and challenges, especially taking into account the new imaging techniques and parameters that can be introduced into intelligent systems to significantly improve their diagnostic specificity and clinical application.
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Data analysis and tissue type assignment for glioblastoma multiforme. BIOMED RESEARCH INTERNATIONAL 2014; 2014:762126. [PMID: 24724098 PMCID: PMC3958772 DOI: 10.1155/2014/762126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2013] [Revised: 01/13/2014] [Accepted: 01/23/2014] [Indexed: 11/18/2022]
Abstract
Glioblastoma multiforme (GBM) is characterized by high infiltration. The interpretation of MRSI data, especially for GBMs, is still challenging. Unsupervised methods based on NMF by Li et al. (2013, NMR in Biomedicine) and Li et al. (2013, IEEE Transactions on Biomedical Engineering) have been proposed for glioma recognition, but the tissue types is still not well interpreted. As an extension of the previous work, a tissue type assignment method is proposed for GBMs based on the analysis of MRSI data and tissue distribution information. The tissue type assignment method uses the values from the distribution maps of all three tissue types to interpret all the information in one new map and color encodes each voxel to indicate the tissue type. Experiments carried out on in vivo MRSI data show the feasibility of the proposed method. This method provides an efficient way for GBM tissue type assignment and helps to display information of MRSI data in a way that is easy to interpret.
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