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

This work was partially funded by the European Commission: HealthAgents (contract no. FP6-2005-IST 027214). Jan Luts is a PhD student supported by an IWT grant. Carlos Saez, Salvador Tortajada and Javier Vicente are PhD students partially supported by the Programa Torres Quevedo from the Ministerio de Educacion y Ciencia, co-founded by the European Social Fund (PTQ-08-01-06817, PTQ-08-01-06802, PTQ05-02-03386). We thank INTERPRET partners for their support and for providing the data used for training some of the classifiers included in the HealthAgents network; in particular we thank C. Majos (IDI-Bellvitge), John Griffiths (SGUL), Arend Heerschap (RU), Witold Gajewicz (MUL), Jorge Calvar (FLENI), Margarida Julia-Sape (UAB) and Carles Arus (UAB). The language revision of this document was funded by the Universidad Politecnica de Valencia. This work has been partially supported by the Health Institute Carlos III through the RETICS Combiomed, RD07/0067/2001.

[1]  F. T. de Dombal,et al.  Computer-Assisted Diagnosis of Abdominal Pain using “Estimates” Provided by Clinicians , 1972, British medical journal.

[2]  M. Kendall.  Statistical Methods for Research Workers , 1937, Nature.

[3]  B. Scheithauer,et al.  The 2007 WHO classification of tumours of the central nervous system , 2007, Acta Neuropathologica.

[4]  Sabine Van Huffel,et al.  Multiproject–multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy , 2008, Magnetic Resonance Materials in Physics, Biology and Medicine.

[5]  Murray Silverstein,et al.  A Pattern Language , 1977 .

[6]  Guido Valli,et al.  Neural networks for computer-aided diagnosis: detection of lung nodules in chest radiograms , 2003, IEEE Transactions on Information Technology in Biomedicine.

[7]  V. B. Kartha,et al.  Principal component analysis and artificial neural network analysis of oral tissue fluorescence spectra: Classification of normal premalignant and malignant pathological conditions , 2006, Biopolymers.

[8]  Franklyn A Howe,et al.  1H MR spectroscopy of brain tumours and masses , 2003, NMR in biomedicine.

[9]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[10]  Marta P. Tjoa,et al.  A fusion-based clinical decision support for disease diagnosis from endoscopic images , 2005, Comput. Biol. Medicine.

[11]  Dorin Comaniciu,et al.  Computer-assisted discrimination among malignant lymphomas and leukemia using immunophenotyping, intelligent image repositories, and telemicroscopy , 2000, IEEE Transactions on Information Technology in Biomedicine.

[12]  J. Cardoso,et al.  Blind beamforming for non-gaussian signals , 1993 .

[13]  Liang Xiao,et al.  Developing a security protocol for a distributed decision support system in a healthcare environment , 2008, 2008 ACM/IEEE 30th International Conference on Software Engineering.

[14]  Ralph Johnson,et al.  design patterns elements of reusable object oriented software , 2019 .

[15]  A. W. Simonetti,et al.  Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra , 2006, NMR in biomedicine.

[16]  Edward H. Shortliffe,et al.  ONCOCIN: An Expert System for Oncology Protocol Management , 1981, IJCAI.

[17]  Sabine Van Huffel,et al.  The effect of combining two echo times in automatic brain tumor classification by MRS , 2008, NMR in biomedicine.

[18]  F Lacombe,et al.  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 , 2007, Leukemia.

[19]  B Michael Kelm,et al.  Automated estimation of tumor probability in prostate magnetic resonance spectroscopic imaging: Pattern recognition vs quantification , 2007, Magnetic resonance in medicine.

[20]  Paulo J. G. Lisboa,et al.  A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer , 2003, Artif. Intell. Medicine.

[21]  P Ravazoula,et al.  A computer-based diagnostic and prognostic system for assessing urinary bladder tumour grade and predicting cancer recurrence , 2002, Medical informatics and the Internet in medicine.

[22]  P. Lucas,et al.  Computer-based Decision Support in the Management of Primary Gastric non-Hodgkin Lymphoma , 1998, Methods of Information in Medicine.

[23]  Sabine Van Huffel,et al.  HealthAgents: distributed multi-agent brain tumor diagnosis and prognosis , 2009, Applied Intelligence.

[24]  Juan Miguel García-Gómez,et al.  Automated Brain Tumor Biopsy Prediction Using Single-labeling cDNA Microarrays-based Gene Expression Profiling , 2009, Diagnostic molecular pathology : the American journal of surgical pathology, part B.

[25]  Erkki Oja,et al.  Independent Component Analysis , 2001 .

[26]  Hiok Chai Quek,et al.  GenSo-FDSS: a neural-fuzzy decision support system for pediatric ALL cancer subtype identification using gene expression data , 2005, Artif. Intell. Medicine.

[27]  Fred D. Davis.  Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[28]  Jan Luts,et al.  Effect of feature extraction for brain tumor classification based on short echo time 1H MR spectra , 2008, Magnetic resonance in medicine.

[29]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[30]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[31]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..