Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods

Artificial Intelligence in Medicine

Volumn 34, Issue 2, 2005, Pages 129-139

  Nattkemper, Tim W ^a   Arnrich, Bert ^a   Lichte, Oliver ^a   Timm, Wiebke ^a   Degenhard, Andreas ^a   Pointon, Linda ^b   Hayes, Carmel ^b,c   Leach, Martin O ^b  

^a BIELEFELD UNIVERSITY   (Germany)

^b INSTITUTE OF CANCER RESEARCH   (United Kingdom)

^c SIEMENS AG   (Germany)

Author keywords

Artificial neural networks; Breast cancer; Clinical screening; Computer aided diagnosis; Decision trees; Machine learning; Magnetic resonance imaging; Support vector machine (SVM)

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DESIGN; LEARNING SYSTEMS; MAGNETIC RESONANCE IMAGING; MEDICAL IMAGING; SELF ORGANIZING MAPS; SIGNAL PROCESSING; TREES (MATHEMATICS); TUMORS; VECTOR QUANTIZATION;

BREAST TUMORS; DECISION TREES (DT); RADIOLOGICAL FEATURES; SUPPORT VECTOR MACHINES (SVM);

RADIOLOGY;

ARTICLE; BREAST CANCER; CANCER SCREENING; COMPUTER ASSISTED DIAGNOSIS; CONTRAST ENHANCEMENT; DATA ANALYSIS; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; TUMOR CLASSIFICATION;

ARTIFICIAL INTELLIGENCE; BREAST NEOPLASMS; DECISION TREES; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING;

EID: 19344365337     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2004.09.001     Document Type: Article

References (30)

1. M.D. Schnall Application of magnetic resonance imaging to early detection of breast cancer Breast Cancer Res 3 1 2001 17 21
2. S.H. Heywang-Kobrunner, and R. Beck Contrast enhanced MRI of the breast 1996 Springer Verlag Berlin, Heidelberg, New York
3. R. Lucht, M. Knopp, and G. Brix Classification of signal-time curves from dynamic mr mammography by neural networks Magn Reson Imaging 19 1 2001 51 57
4. S.H. Heywang-Kobrunner, P. Viehweg, A. Heinig, and C. Kuchler Contrast-enhanced MRI of the breast: accuracy, value, controversies, solutions Eur J Radiol 24 2 1997 94 108
5. L. Nunes, M. Schnall, E. Siegelman, C. Langlotz, S. Orel, and D. Sullivan Diagnostic performance characteristics of architectural features revealed by high spatial-resolution MR imaging of the breast AJR Am J Roentgenol 169 2 1997 409 415
6. P.F. Liu, J.F. Debatin, R.F. Caduff, G. Kacl, E. Garzoli, and G.P. Krestin Improved diagnostic accuracy in dynamic contrast enhanced MRI of the breast by combined quantitative and qualitative analysis Br J Radiol 71 845 1998 501 509
7. S. Harms, D. Flamig, K. Hesley, M. Meiches, R. Jensen, and W. Evans MR imaging of the breast with rotating delivery of excitation off resonance: clinical experience with pathologic correlation Radiology 187 2 1993 493 501
8. E. Heiberg, W. Perman, V. Herrmann, and C. Janney Dynamic sequential 3D gadolinium-enhanced MRI of the whole breast Magn Reson Imaging 14 4 1996 337 348
9. C. Kuhl, B. Kreft, H. Bieling, T. Sommer, G. Lutterbey, and J. Gieseke Dynamic MRI in premenopausal healthy volunteers: normal values of contrast enhancement and cycle phase dependency Radiology 203 1997 137 144
10. J. Brown, D. Buckley, A. Coulthard, A. Dixon, J. Dixon, and D. Easton Magnetic resonance imaging screening in women at genetic risk of breast cancer: imaging and analysis protocol for the UK Multicenter Study. UK MRI Breast Screening Study Advisory Group Magn Reson Imaging 18 7 2000 765 776
11. D. Easton, D. Bishop, D. Ford, and G. Crockford Genetic linkage analysis in familial breast and ovarian cancer: results from 214 families. The breast cancer linkage consortium Am J Hum Genet 52 4 1993 678 701
12. A. Degenhard, C. Tanner, C. Hayes, D.J. Hawkes, and M.O. Leach UMBSS comparison between radiological and artificial neural network diagnosis in clinical screening. Special issue: simulation and modelling applied to medicine Phys Meas 23 2002 727 739
13. H.A. Abbass An evolutionary artificial neural networks approach for breast cancer diagnosis Artif Intell Med 25 3 2001 1 12
14. M. Markey, L. Lo, G. Tourassi, and J.C.E. Floyd Self-organizing map for cluster analysis of a breast cancer database Artif Intell Med 27 2003 113 127
15. D. West, and V. West Model selection for a medical diagnostic decision support system: a breast cancer detection case Artif Intell Med 20 3 2000 183 204
16. W. Wolberg, and O. Mangasarian Multisurface method of pattern separation for medical diagnosis applied to breast cytology Proceedings of the National Academy of Sciences (PNAS) 1990 9193 9196
17. C.M. Bishop Neural networks for pattern recognition 1997 Oxford Press
18. T. Kohonen Self-organization and associative memory 1989 Springer-Verlag Berlin
19. T. Kohonen Self-organizing maps. Series in information sciences, vol. 30 2000 Springer
20. I. Kononenko Machine learning for medical diagnosis: history, state of the art and perspective Artif Intell Med 23 2001 89 109
21. B. Schölkopf, A. Smola, and K.-R.R. Müller Kernel principal component analysis B. Schölkopf C.J.C. Burges A.J. Smola Advances in kernel methods - SV learning 1999 MIT Press Cambridge, MA 327 352
22. S. Mika, G. Rätsch, J. Weston, B. Schölkopf, and K.-R.R. Müller Fisher discriminant analysis with kernels Y.-H. Hu J. Larsen E. Wilson S. Douglas Neural networks for signal processing IX, IEEE 1999 41 48
23. V. Vapnik The nature of statistical learning theory 1995 Springer
24. J. Platt Fast training of support vector machines using sequential minimal optimization B. Schölkopf C. Burges A. Smola Advances in kernel methods - support vector learning 1998 MIT Press
25. Keerthi SS, Shevade SS, Bhattacharyya C, Murthy KRK. Improvements to Platt's SMO algorithm for SVM classifier design. Technical report TR CD-99-14. Nat. University of Singapore, 1999.
26. J.R. Quinlan C4. 5: programs for machine learning 1993 MK San Mateo, CA
27. T. Elomaa, and J. Rousu On the well-behavedness of important attribute evaluation functions Scandinavian Conference on AI 1997 95 106
28. U.M. Fayyad, and K.B. Irani Multi-interval discretization of continuous-valued attributes for classification learning Proceedings of the 13th International Joint Conference of Artificial Intelligence (IJCAI), vol. 1 1993 1022 1027
29. H.M. Zweig, and G. Campbell Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine Clin Chem 39 4 1993 561 577
30. M. Greiner, D. Pfeifier, and R.D. Smith Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests Prev Vet Med 45 2000 23 41