Applied Intelligence

, Volume 30, Issue 3, pp 191–202 | Cite as

HealthAgents: distributed multi-agent brain tumor diagnosis and prognosis

  • Horacio González-Vélez
  • Mariola Mier
  • Margarida Julià-Sapé
  • Theodoros N. Arvanitis
  • Juan M. García-Gómez
  • Montserrat Robles
  • Paul H. Lewis
  • Srinandan Dasmahapatra
  • David Dupplaw
  • Andrew Peet
  • Carles Arús
  • Bernardo Celda
  • Sabine Van Huffel
  • Magí Lluch-Ariet
Article

Abstract

We present an agent-based distributed decision support system for the diagnosis and prognosis of brain tumors developed by the HealthAgents project. HealthAgents is a European Union funded research project, which aims to enhance the classification of brain tumors using such a decision support system based on intelligent agents to securely connect a network of clinical centers. The HealthAgents system is implementing novel pattern recognition discrimination methods, in order to analyze in vivo Magnetic Resonance Spectroscopy (MRS) and ex vivo/in vitro High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HR-MAS) and DNA micro-array data. HealthAgents intends not only to apply forefront agent technology to the biomedical field, but also develop the HealthAgents network, a globally distributed information and knowledge repository for brain tumor diagnosis and prognosis.

Keywords

Machine learning Decision support systems Computational intelligence Agents Pattern recognition Medical ontologies Medical informatics Magnetic resonance 

Abbreviations

API

Application Programming Interface

DSS

Decision Support System

EbSS

Evidence-based Search Service

FIPA

Foundation of Intelligent Physical Agents

GUI

Graphical User Interface

HAL

HealthAgents Language

HR-MAS

High Resolution Magic Angle Spinning Nuclear Magnetic Resonance

LCC

Lightweight Coordination Calculus

LDA

Linear Discriminant Analysis

LS-SVM

Least-Squares Support Vector Machines

LTE

Long Time Echo

MRI

Magnetic Resonance Imaging

MRS

Magnetic Resonance Spectroscopy

MRSI

Magnetic Resonance Spectroscopic Imaging

OWL

Web Ontology Language

RDF

Resource Description Framework

STE

Short Time Echo

SVM

Support Vector Machines

YP

Yellow Pages

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References

  1. 1.
    Alpaydin E (2004) Introduction to machine learning. Adaptive computation and machine learning. MIT Press, Cambridge Google Scholar
  2. 2.
    Armstrong TS, Cohen MZ, Weinberg J, Gilbert MR (2004) Imaging techniques in neuro-oncology. Semin Oncol Nurs 20(4):231–239 CrossRefGoogle Scholar
  3. 3.
    Arús C, Celda B, Dasmahapatra S, Dupplaw D, González-Vélez H, van Huffel S, Lewis P, Lluch i Ariet M, Mier M, Peet A, Robles M (2006) On the design of a web-based decision support system for brain tumour diagnosis using distributed agents. In: WI-IAT 2006. IEEE, Hong Kong, pp 208–211 Google Scholar
  4. 4.
    Barton S, Howe F, Tomlins A, Cudlip S, Nicholson J, Bell B, Griffiths J (1999) Comparison of in vivo 1H MRS of human brain tumors with 1H HR-MAS spectroscopy of intact biopsy samples in vitro. Magn Reson Mater Phys 8(2):121–128 Google Scholar
  5. 5.
    Beckett D (2007) Turtle—terse RDF triple language. ILRT University of Bristol. http://www.ilrt.bris.ac.uk/discovery/2004/01/turtle/ (Last accessed: 13 Feb 2007)
  6. 6.
    Bellifemine F, Poggi A, Rimassa G (2001) JADE: a FIPA2000 compliant agent development environment. In: AGENTS’01. ACM Press, Montreal, pp 216–217 Google Scholar
  7. 7.
    Bishop CM (2006) Pattern recognition and machine learning. Information Science and Statistics. Springer, New York Google Scholar
  8. 8.
    Bizer C, Cyganiak R, Garbers J, Maresch O (2006) D2RQ-treating Non-RDF relational databases as virtual RDF graphs, v0.5 edn. Freie Universitat, Berlin Google Scholar
  9. 9.
    Bray F, Sankila R, Ferlay J, Parkin DM (2002) Estimates of cancer incidence and mortality in Europe in 1995. Eur J Cancer 38(1):99–166 CrossRefGoogle Scholar
  10. 10.
    Brugali D, Sycara. K (2000) Towards agent oriented application frameworks. ACM Comput Surv 32(1):21–27 CrossRefGoogle Scholar
  11. 11.
    Dasmahapatra S, Dupplaw D, Hu B, Lewis PH, Shadbolt N (2005) Ontology-mediated distributed decision support for breast cancer. In: AIME 2005. Lecture notes in computer science, vol 3581. Springer, Aberdeen, pp 221–225 Google Scholar
  12. 12.
    De Turck F, Decruyenaere J, Thysebaert P, Van Hoecke S, Volckaert B, Danneels C, Colpaert K, De Moor G (2007) Design of a flexible platform for execution of medical decision support agents in the intensive care unit. Comput Biol Med 37(1):97–112 CrossRefGoogle Scholar
  13. 13.
    DeAngelis LM (2001) Brain tumors. N Engl J Med 344(2):114–123 CrossRefMathSciNetGoogle Scholar
  14. 14.
    Favre J, Taha JM, Burchiel KJ (2002) An analysis of the respective risks of hematoma formation in 361 consecutive morphological and functional stereotactic procedures. Neurosurgery 50(1):48–57 CrossRefGoogle Scholar
  15. 15.
    Field M, Witham TF, Flickinger JC, Kondziolka D, Lunsford LD (2001) Comprehensive assessment of hemorrhage risks and outcomes after stereotactic brain biopsy. J Neurosurg 94(4):545–551 CrossRefGoogle Scholar
  16. 16.
    Fletcher-Heath LM, Hall LO, Goldgof DB, Murtagh FR (2001) Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artif Intell Med 21(1–3):43–63 CrossRefGoogle Scholar
  17. 17.
    Gennari JH, Musen MA, Fergerson RW, Grosso WE (2003) Crubézy, M., Eriksson, H., N.F. Noy, S.W. Tu: The evolution of Protégé: an environment for knowledge-based systems development. Int J Hum-Comput Stud 58(1):89–123 CrossRefGoogle Scholar
  18. 18.
    Glotsos D, Tohka J, Ravazoula P, Cavouras D, Nikiforidis G (2005) Automated diagnosis of brain tumors astrocytomas using probabilistic neural network clustering and support vector machines. Int J Neural Syst 15(1–2):1–11 CrossRefGoogle Scholar
  19. 19.
    González-Vélez V, Flores-Rodríguez T, Flores-Avalos B, González-Vélez H (1997) A statistical brain-mapping system for the evaluation of communication disorders. In: CBMS 1997. IEEE, Maribor, pp 167–172 Google Scholar
  20. 20.
    Hagberg G (1998) From magnetic resonance spectroscopy to classification of tumors. A review of pattern recognition methods. NMR Biomed 11(4–5):148–156 CrossRefGoogle Scholar
  21. 21.
    Hall W (1998) The safety and efficacy of stereotactic biopsy for intracranial lesions. Cancer 82(9):1749–1755 CrossRefGoogle Scholar
  22. 22.
    Hamdi MS (2006) MASACAD: A multiagent-based approach to information customization. IEEE Intell Syst 21(1):60–67 CrossRefGoogle Scholar
  23. 23.
    Hanahan D, Weinberg RA (2000) The hallmarks of cancer. Cell 100(1):57–70 CrossRefGoogle Scholar
  24. 24.
    Haque S, Mital D, Srinivasan S (2002) Advances in biomedical informatics for the management of cancer. Ann NY Acad Sci 980:287–297 Google Scholar
  25. 25.
    Hendler J (2001) Agents and the semantic web. IEEE Intell Syst 16(2):30–37 CrossRefGoogle Scholar
  26. 26.
    Howe FA, Opstad KS (2003) 1H MR spectroscopy of brain tumors and masses. NMR Biomed 16(3):123–131 CrossRefGoogle Scholar
  27. 27.
    IEEE Computer Society (2007) The foundation of intelligent physical agents. http://www.fipa.org/ (Last accessed 30 May 2007)
  28. 28.
    Julià-Sapé M, Acosta D, Majós C, Moreno-Torres A, Wesseling P, Acebes JJ, Griffiths JR, Arús C (2006) Comparison between neuroimaging classifications and histopathological diagnoses using an international multicenter brain tumor magnetic resonance imaging database. J Neurosurg 105(1):6–14 CrossRefGoogle Scholar
  29. 29.
    Julià-Sapé M, Acosta D, Mier M, Arús C, Watson D (2006) The INTERPRET consortium: a multi-center web-accessible and quality control-checked database of in vivo MR spectra of brain tumour patients. Magn Reson Mater Phys 19(1):22–33 CrossRefGoogle Scholar
  30. 30.
    Larrañaga P, Calvo B, Santana R, Bielza C, Galdiano J, Inza I, Lozano JA, Armañanzas R, Santafé G, Perez A, Robles V (2006) Machine learning in bioinformatics. Brief Bioinform 7(1):86–112 CrossRefGoogle Scholar
  31. 31.
    Lee CS, Jiang CC, Hsieh TC (2006) A genetic fuzzy agent using ontology model for meeting scheduling system. Inf Sci 176(9):1131–1155 MATHCrossRefGoogle Scholar
  32. 32.
    Lee CS, Pan CY (2004) An intelligent fuzzy agent for meeting scheduling decision support system. Fuzzy Sets Syst 142(3):467–488 MATHCrossRefMathSciNetGoogle Scholar
  33. 33.
    Lee CS, Wang MH (2007) Ontology-based intelligent healthcare agent and its application to respiratory waveform recognition. Expert Syst Appl 33(3):606–619 CrossRefMathSciNetGoogle Scholar
  34. 34.
    Luck M, Merelli E (2005) Agents in bioinformatics. Knowl Eng Rev 20(2):117–125 CrossRefGoogle Scholar
  35. 35.
    Lukas L, Devos A, Suykens JAK, Vanhamme L, Howe FA, Majós C, Moreno-Torres A, Graaf MVD, Tate AR, Arús C, Van Huffel S (2004) Brain tumor classification based on long echo proton MRS signals. Artif Intell Med 31(1):73–89 CrossRefGoogle Scholar
  36. 36.
    Martínez-Bisbal MC, Martí-Bonmatí L, Piquer J, Revert A, Ferrer P, Llácer JL, Piotto M, Assemat O, Celda B (2004) 1H and 13C HR-MAS spectroscopy of intact biopsy samples ex vivo and in vivo. NMR Biomed 17(4):191–205 CrossRefGoogle Scholar
  37. 37.
    McGuinness DL, van Harmelen F (2004) OWL web ontology language overview. Standard W3C Recommendation 10 February 2004, World Wide Web Consortium (W3C). http://www.w3.org/TR/owl-features/ (Last accessed 13 January 2007)
  38. 38.
    Merelli E, Armano G, Cannata N, Corradini F, d’Inverno M, Doms A, Lord P, Martin A, Milanesi L, Möller S, Schroeder M, Luck M (2007) Agents in bioinformatics, computational and systems biology. Brief Bioinform 8(1):45–59 CrossRefGoogle Scholar
  39. 39.
    Mischel P, Cloughesy T, Nelson S (2004) DNA-microarray analysis of brain cancer: molecular classification for therapy. Nature Rev Neuroscie 5:782–792 CrossRefGoogle Scholar
  40. 40.
    Mitchell TM (1999) Machine learning and data mining. Commun ACM 42(11):30–36 CrossRefGoogle Scholar
  41. 41.
    Nutt CL, Mani DR, Betensky RA, Tamayo P, Cairncross JG, Ladd C, Pohl U, Hartmann C, McLaughlin ME, Batchelor TT, Black PM, von Deimling A, Pomeroy SL, Golub TR, Louis DN (2003) Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Cancer Res. 63:1602–1607 Google Scholar
  42. 42.
    Peet AC, Leach MO, Pinkerton CR, Price P, Williams SR, Grundy RG (2005) The development of functional imaging in the diagnosis, management and understanding of childhood brain tumors. Pediatr Blood Cancer 44(2):103–113 CrossRefGoogle Scholar
  43. 43.
    Robertson D (2004) A lightweight coordination calculus for agent systems. In: DALT 2004. Lecture notes in computer science, vol 3476. Springer, New York, pp 183–197 Google Scholar
  44. 44.
    Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300 CrossRefMathSciNetGoogle Scholar
  45. 45.
    Tate AR, Underwood J, Acosta DM, Julià-Sapé M, Majós C, Moreno-Torres A, Howe FA, van der Graaf M, Lefournier V, Murphy MM, Loosemore A, Ladroue C, Wesseling P, Bosson JL, Cabañas ME, Simonetti AW, Gajewicz W, Calvar J, Capdevila A, Wilkins PR, Bell BA, Rémy C, Heerschap A, Watson D, Griffiths JR, Arús C (2006) Development of a decision support system for diagnosis and grading of brain tumors using in vivo magnetic resonance single voxel spectra. NMR Biomed 19(4):411–434 CrossRefGoogle Scholar
  46. 46.
    The eTUMOUR Consortium (2004–2008) eTUMOUR. http://www.etumour.net (Last accessed: 5 January 2007)
  47. 47.
    The HealthAgents Consortium (2006–2008) HealthAgents. http://www.healthagents.net (Last accessed: 5 January 2007)
  48. 48.
    Tortajada S, García-Gómez JM, Vidal C, Arús C, Julià-Sapé M, Moreno A, Robles M (2006) Improved classification by pattern recognition of brain tumors combining long and short echo time 1H-MR spectra. In: ESMRMB 2006: 23rd annual scientific meeting. Magn Reson Mater Phys 19(1):168–169 Google Scholar
  49. 49.
    Universitat Autònoma de Barcelona (2000–2002) INTERPRET project. http://azizu.uab.es/INTERPRET/ (Last accessed: 5 January 2007)
  50. 50.
    Vapnik VN (1999) The nature of statistical learning theory 2nd edn. Statistics for engineering and information science. Springer, New York Google Scholar
  51. 51.
    Yan H, Jiang Y, Zheng J, Peng C, Li Q (2006) A multilayer perceptron-based medical decision support system for heart disease diagnosis. Expert Syst Appl 30(2):272–281 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Horacio González-Vélez
    • 1
  • Mariola Mier
    • 1
  • Margarida Julià-Sapé
    • 2
  • Theodoros N. Arvanitis
    • 3
  • Juan M. García-Gómez
    • 4
  • Montserrat Robles
    • 4
  • Paul H. Lewis
    • 5
  • Srinandan Dasmahapatra
    • 5
  • David Dupplaw
    • 5
  • Andrew Peet
    • 6
  • Carles Arús
    • 2
  • Bernardo Celda
    • 7
  • Sabine Van Huffel
    • 8
  • Magí Lluch-Ariet
    • 9
  1. 1.University of EdinburghEdinburghUK
  2. 2.Universitat Autònoma de BarcelonaBarcelonaSpain
  3. 3.University of BirminghamBirminghamUK
  4. 4.Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones AvanzadasValenciaSpain
  5. 5.University of SouthamptonSouthamptonUK
  6. 6.University of Birmingham and Birmingham Children’s HospitalBirminghamUK
  7. 7.Universitat de València and Instituto de Salud Carlos IIIValènciaSpain
  8. 8.Katholieke Universiteit LeuvenLeuvenBelgium
  9. 9.MicroArt S.L. Parc Cientific de BarcelonaBarcelonaSpain

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