Hostname: page-component-76fb5796d-r6qrq Total loading time: 0 Render date: 2024-04-26T04:27:29.264Z Has data issue: false hasContentIssue false

A generic and extensible automatic classification framework applied to brain tumour diagnosis in HealthAgents

Published online by Cambridge University Press:  28 July 2011

Carlos Sáez*
Affiliation:
Grupo de Informática Biomédica, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Edificio 8G, Camino de Vera s/n, 46022 Valencia, España; e-mail: carsaesi@upv.es, juanmig@upv.es, javirob@upv.es, vesaltor@upv.es, mrobles@upv.es
Juan Miguel García-Gómez*
Affiliation:
Grupo de Informática Biomédica, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Edificio 8G, Camino de Vera s/n, 46022 Valencia, España; e-mail: carsaesi@upv.es, juanmig@upv.es, javirob@upv.es, vesaltor@upv.es, mrobles@upv.es
Javier Vicente*
Affiliation:
Grupo de Informática Biomédica, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Edificio 8G, Camino de Vera s/n, 46022 Valencia, España; e-mail: carsaesi@upv.es, juanmig@upv.es, javirob@upv.es, vesaltor@upv.es, mrobles@upv.es
Salvador Tortajada*
Affiliation:
Grupo de Informática Biomédica, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Edificio 8G, Camino de Vera s/n, 46022 Valencia, España; e-mail: carsaesi@upv.es, juanmig@upv.es, javirob@upv.es, vesaltor@upv.es, mrobles@upv.es
Jan Luts*
Affiliation:
Department of Electrical Engineering (ESAT), Research Division SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium; e-mail: jan.luts@esat.kuleuven.be, sabine.vanhuffel@esat.kuleuven.be
David Dupplaw*
Affiliation:
Intelligence, Agents, Multimedia Group, School of Electronics and Computer Science, University of Southampton, Highfield, Southampton, SO17 1BJ, UK; e-mail: dpd@ecs.soton.ac.uk
Sabine Van Huffel*
Affiliation:
Department of Electrical Engineering (ESAT), Research Division SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium; e-mail: jan.luts@esat.kuleuven.be, sabine.vanhuffel@esat.kuleuven.be
Montserrat Robles*
Affiliation:
Grupo de Informática Biomédica, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Edificio 8G, Camino de Vera s/n, 46022 Valencia, España; e-mail: carsaesi@upv.es, juanmig@upv.es, javirob@upv.es, vesaltor@upv.es, mrobles@upv.es

Abstract

New biomedical technologies enable the diagnosis of brain tumours by using non-invasive methods. HealthAgents is a European Union-funded research project that aims to build an agent-based distributed decision support system (dDSS) for the diagnosis of brain tumours. This is achieved using the latest biomedical knowledge, information and communication technologies and pattern recognition (PR) techniques. As part of the PR development of HealthAgents, an independent and automatic classification framework (CF) has been developed. This framework has been integrated with the HealthAgents dDSS using the HealthAgents agent platform. The system offers (1) the functionality to search for distributed classifiers to solve specific questions; (2) automatic classification of new cases; (3) instant deployment of new validated classifiers; and (4) the ability to rank a set of classifiers according to their performance and suitability for the case in hand. The CF enables both the deployment of new classifiers using the provided Extensible Markup Language1 classifier specification, and the inclusion of new PR techniques that make the system extensible. These features may enable the rapid integration of PR laboratory results into industrial or research applications, such as the HealthAgents dDSS. Two classification nodes have been deployed and they currently offer classification services by means of dedicated servers connected to the HealthAgents agent platform: one node being located at the Katholieke Universiteit Leuven, Belgium and the other at the Universidad Politécnica de Valencia, Spain. These classification nodes share the current set of brain tumour classifiers that have been trained from in vivo magnetic resonance spectroscopy data. The combination of the CF with a distributed agent system constitutes the basis of the brain tumour dDSS developed in HealthAgents.

Type
Articles
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alexander, C., Ishikawa, S., Silverstein, M., Jacobson, M., Fiksdahl-King, I., Angel, S. 1977. A Pattern Language. Oxford University Press.Google Scholar
Cardoso, J., Souloumiac, A. 1993. Blind beamforming for non Gaussian signals. IEEE Proceedings-F 140(6), 362370.Google Scholar
Castells, X., García-Gómez, J. M., Navarro, A., Acebes, J. J., Godino, Ó., Boluda, S., Barceló, A., Robles, M., Ariño, J., Arús, C. 2009. Automated brain tumor biopsy prediction using single-labelling cDNA microarrays-based gene expression profiling. Diagnostic Molecular Pathology 18, 206218.CrossRefGoogle Scholar
Comon, P. 1994. Independent component analysis, a new concept? Signal Processing 36(3), 287314.CrossRefGoogle Scholar
Coppini, G., Diciotti, S., Falchini, M., Villari, N., Valli, G. 2003. Neural networks for computer-aided diagnosis: detection of lung nodules in chest radiograms. IEEE Transactions on Information Technology in Biomedicine 7(4), 344357.CrossRefGoogle ScholarPubMed
Cover, T., Hart, P. E. 1967. Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 2127.CrossRefGoogle Scholar
Davis, F. D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13(3), 319339.CrossRefGoogle Scholar
Fisher, R. A. 1925. Statistical Methods for Research Workers. Oliver and Boyd, Edinburgh, UK. http://psychclassics.yorku.ca/Fisher/Methods/.Google Scholar
Foran, D. J., Comaniciu, D., Meer, P., Goodell, L. A. 2000. Computer-assisted discrimination among malignant lymphomas and leukemia using immunophenotyping, intelligent image repositories, and telemicroscopy. IEEE Transactions on Information Technology in Biomedicine 4(4), 265273.CrossRefGoogle ScholarPubMed
Gamma, E., Helm, R., Johnson, R., Vlissides, J. 1995. Design Patterns: Elements of Reusable Object-oriented Software. Pearson Addison-Wesley.Google Scholar
García-Gómez, J. M., Vidal, C., Martí-Bonmatí, L., Robles, M. 2004. Distributed decision support architecture for soft tissue tumor diagnosis. In MEDINFO 2004: 11th World Congress on Medical Informatics, San Francisco, California, USA, 1608.Google Scholar
García-Gómez, J., Tortajada, S., Vidal, C., Julià-Sapé, M., Luts, J., Van Huffel, S., Arús, C., Robles, M. 2008. The influence of combining two echo times in automatic brain tumor classification by magnetic resonance spectroscopy. NMR in Biomedicine 21(10), 11121125.CrossRefGoogle Scholar
García-Gómez, J. M., Luts, J., Julià-Sapé, M., Krooshof, P., Tortajada, S., Robledo, J. V., Melssen, W., Fuster-García, E., Olier, I., Postma, G., Monleón, D., Moreno-Torres, Á., Pujol, J., Candiota, A. P., Martínez-Bisbal, M. C., Suykens, J., Buydens, L., Celda, B., Van Huffel, S., Arús, C., Robles, M. 2009. Multiproject-multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy. MAGMA 22(1), 518.CrossRefGoogle ScholarPubMed
González-Vélez, H., Mier, M., Julià-Sapé, M., Arvanitis, T. N., García-Gómez, J. M., Robles, M., Lewis, P. H., Dasmahapatra, S., Dupplaw, D., Peet, A., Arús, C., Celda, B., Van Huffel, S., Lluch-Ariet, M. 2009. HealthAgents: distributed multi-agent brain tumor diagnosis and prognosis. Applied Intelligence 30(3), 191202.CrossRefGoogle Scholar
Hastie, T., Tibshirani, R., Friedman, J. H. 2001. The Elements of Statistical Learning. Springer.CrossRefGoogle Scholar
Howe, F. A., Opstad, K. S. 2003. 1H MR spectroscopy of brain tumours and masses. NMR in Biomedicine 16(3), 123131.CrossRefGoogle ScholarPubMed
Hyvärinen, A., Karhunen, J., Oja, E. 2001. Independent Component Analysis. John Wiley & Sons.CrossRefGoogle ScholarPubMed
Kelm, B. M., Menze, B. H., Zechmann, C. M., Baudendistel, K. T., Hamprecht, F. A. 2007. Automated estimation of tumor probability in prostate magnetic resonance spectroscopic imaging: pattern recognition vs quantification. Magnetic Resonance in Medicine 57(1), 150159.CrossRefGoogle ScholarPubMed
Leaper, D. J., De Dombal, F. T., Horrocks, J. C., Staniland, J. R. 1972. Computer-assisted diagnosis of abdominal pain using estimates provided by clinicians. The British Journal of Surgery 59(11), 897898.Google ScholarPubMed
Lisboa, P. J. G., Wong, H., Harris, P., Swindell, R. 2003. A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer. Artificial Intelligence in Medicine 28(1), 125.CrossRefGoogle ScholarPubMed
Louis, D., Ohgaki, H., Wiestler, O., Cavenee, W., Burger, P., Jouvet, A., Scheithauer, B., Kleihues, P. 2007. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathologica 114(2), 97109.CrossRefGoogle ScholarPubMed
Lucas, P. J., Boot, H., Taal, B. G. 1998. Computer-based decision support in the management of primary gastric non-Hodgkin lymphoma. Methods of Information in Medicine 37(3), 206219.Google ScholarPubMed
Luts, J., Poullet, J.-B., Garcia-Gomez, J. M., Heerschap, A., Robles, M., Suykens, J. A. K., Van Huffel, S. 2008. Effect of feature extraction for brain tumor classification based on short echo time 1H MR spectra. Magnetic Resonance in Medicine 60(2), 288298.CrossRefGoogle ScholarPubMed
Nayak, G. S., Kamath, S., Pai, K. M., Sarkar, A., Ray, S., Kurien, J., D’Almeida, L., Krishnanand, B. R., Santhosh, C., Kartha, V. B., Mahato, K. K. 2006. Principal component analysis and artificial neural network analysis of oral tissue fluorescence spectra: classification of normal premalignant and malignant pathological conditions. Biopolymers 82(2), 152166.CrossRefGoogle ScholarPubMed
Ratei, R., Karawajew, L., Lacombe, F., Jagoda, K., Del Poeta, G., Kraan, J., De Santiago, M., Kappelmayer, J., Bjorklund, E., Ludwig, W.-D., Gratama, J. W., Orfao, A. 2007. Discriminant function analysis as decision support system for the diagnosis of acute leukemia with a minimal four color screening panel and multiparameter flow cytometry immunophenotyping. Leukemia 21(6), 12041211.CrossRefGoogle ScholarPubMed
Shortliffe, E. H., Scott, A. C., Bischoff, M. B., Campbell, A. B., Melle, W., Jacobs, C. D. 1981. ONCOCIN: an expert system for oncology protocol management. In Seventh International Joint Conference on Artificial Intelligence, Vancouver.Google Scholar
Spyridonos, P., Cavouras, D., Ravazoula, P., Nikiforidis, G. 2002. A computer-based diagnostic and prognostic system for assessing urinary bladder tumour grade and predicting cancer recurrence. Medical Informatics and the Internet in Medicine 27(2), 111122.CrossRefGoogle ScholarPubMed
Suykens, J. A. K., Vandewalle, J. 1999. Least squares support vector machine classifiers. Neural Processing Letters 9(3), 293300.CrossRefGoogle Scholar
Tate, A. R., Underwood, J., Acosta, D. M., Julià-Sapé, M., Majós, C., Moreno-Torres, Á., Howe, F. A., van der Graaf, M., Lefournier, V., Murphy, M. M., Loosemore, A., Ladroue, C., Wesseling, P., Luc Bosson, J., Cabanas, M. E., Simonetti, A. W., Gajewicz, W., Calvar, J., Capdevila, A., Wilkins, P. R., Bell, B. A., Remy, C., Heerschap, A., Watson, D., Griffiths, J. R., Arús, C. 2006. Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra. NMR in Biomedicine 19(4), 411434.CrossRefGoogle ScholarPubMed
Tortajada, S., García-Gómez, J., Vicente, J., Robles, M. 2008. Dynamic learning of brain tumour classifiers based on 1H-MRS. In Book of Abstracts ESMRMB 2008 – Supplement 1, Journal Magnetic Resonance Materials in Physics, Biology and Medicine, Springer Verlag, 21, 14–15.Google Scholar
Tung, W. L., Quek, C. 2005. Genso-fdss: a neural-fuzzy decision support system for pediatric all cancer subtype identification using gene expression data. Artificial Intelligence in Medicine 33(1), 6188.CrossRefGoogle ScholarPubMed
Xiao, L., Lewis, P., Gibb, A. 2008. Developing a security protocol for a distributed decision support system in a healthcare environment. In ICSE08: 30th International Conference on Software Engineering, ACM, Leipzig, Germany, 673–682. .CrossRefGoogle Scholar
Zheng, M. M., Krishnan, S. M., Tjoa, M. P. 2005. A fusion-based clinical decision support for disease diagnosis from endoscopic images. Computers in Biology and Medicine 35(3), 259274.CrossRefGoogle ScholarPubMed