Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods
Nattkemper TW, Arnrich B, Lichte O, Timm W, Degenhard A, Pointon L, Hayes C, Leach MO (2005)
ARTIFICIAL INTELLIGENCE IN MEDICINE 34(2): 129-139.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
Download
Es wurden keine Dateien hochgeladen. Nur Publikationsnachweis!
Autor*in
Nattkemper, Tim WilhelmUniBi
;
Arnrich, Bert;
Lichte, Oliver;
Timm, Wiebke;
Degenhard, Andreas;
Pointon, Linda;
Hayes, Carmel;
Leach, Martin O.
![](https://pub.uni-bielefeld.de/images/icon_orcid.png)
Einrichtung
Abstract / Bemerkung
Objective: In this work, methods utilizing supervised and unsupervised machine learning are applied to analyze radiologically derived morphological and calculated kinetic tumour features. The features are extracted from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) time-course data. Material: The DCE-MRI data of the female breast are obtained within the UK Multicenter Breast Screening Study. The group of patients imaged in this study is selected on the basis of an increased genetic risk for developing breast cancer. Methods: The k-means clustering and self-organizing maps (SOM) are applied to analyze the signal structure in terms of visualization. We employ k-nearest neighbor classifiers (k-nn), support vector machines (SVM) and decision trees (DT) to classify features using a computer aided diagnosis (CAD) approach. Results: Regarding the unsupervised techniques, clustering according to features indicating benign and malignant characteristics is observed to a limited extend. The supervised approaches classified the data with 74% accuracy (DT) and providing an area under the receiver-operator-characteristics (ROC) curve (AUC) of 0.88 (SVM). Conclusion: It was found that contour and wash-out type (WOT) features determined by the radiologists lead to the best SVM classification results. Although a fast signal uptake in early time-point measurements is an important feature for malignant/benign classification of tumours, our results indicate that the wash-out characteristics might be considered as important. © 2004 Elsevier B.V. All rights reserved.
Stichworte
decision trees;
support;
artificial neural networks;
machine learning;
magnetic resonance imaging;
vector machine (SVM);
aided diagnosis;
breast cancer;
clinical screening;
computer
Erscheinungsjahr
2005
Zeitschriftentitel
ARTIFICIAL INTELLIGENCE IN MEDICINE
Band
34
Ausgabe
2
Seite(n)
129-139
ISSN
0933-3657
Page URI
https://pub.uni-bielefeld.de/record/1603560
Zitieren
Nattkemper TW, Arnrich B, Lichte O, et al. Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods. ARTIFICIAL INTELLIGENCE IN MEDICINE. 2005;34(2):129-139.
Nattkemper, T. W., Arnrich, B., Lichte, O., Timm, W., Degenhard, A., Pointon, L., Hayes, C., et al. (2005). Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods. ARTIFICIAL INTELLIGENCE IN MEDICINE, 34(2), 129-139. https://doi.org/10.1016/j.artmed.2004.09.001
Nattkemper, Tim Wilhelm, Arnrich, Bert, Lichte, Oliver, Timm, Wiebke, Degenhard, Andreas, Pointon, Linda, Hayes, Carmel, and Leach, Martin O. 2005. “Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods”. ARTIFICIAL INTELLIGENCE IN MEDICINE 34 (2): 129-139.
Nattkemper, T. W., Arnrich, B., Lichte, O., Timm, W., Degenhard, A., Pointon, L., Hayes, C., and Leach, M. O. (2005). Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods. ARTIFICIAL INTELLIGENCE IN MEDICINE 34, 129-139.
Nattkemper, T.W., et al., 2005. Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods. ARTIFICIAL INTELLIGENCE IN MEDICINE, 34(2), p 129-139.
T.W. Nattkemper, et al., “Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods”, ARTIFICIAL INTELLIGENCE IN MEDICINE, vol. 34, 2005, pp. 129-139.
Nattkemper, T.W., Arnrich, B., Lichte, O., Timm, W., Degenhard, A., Pointon, L., Hayes, C., Leach, M.O.: Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods. ARTIFICIAL INTELLIGENCE IN MEDICINE. 34, 129-139 (2005).
Nattkemper, Tim Wilhelm, Arnrich, Bert, Lichte, Oliver, Timm, Wiebke, Degenhard, Andreas, Pointon, Linda, Hayes, Carmel, and Leach, Martin O. “Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods”. ARTIFICIAL INTELLIGENCE IN MEDICINE 34.2 (2005): 129-139.
Daten bereitgestellt von European Bioinformatics Institute (EBI)
20 Zitationen in Europe PMC
Daten bereitgestellt von Europe PubMed Central.
Multiparametric dynamic contrast-enhanced ultrasound imaging of prostate cancer.
Wildeboer RR, Postema AW, Demi L, Kuenen MPJ, Wijkstra H, Mischi M., Eur Radiol 27(8), 2017
PMID: 28004162
Wildeboer RR, Postema AW, Demi L, Kuenen MPJ, Wijkstra H, Mischi M., Eur Radiol 27(8), 2017
PMID: 28004162
Nuclear spatial and spectral features based evolutionary method for meningioma subtypes classification in histopathology.
Fatima K, Majeed H, Irshad H., Microsc Res Tech 80(8), 2017
PMID: 28379628
Fatima K, Majeed H, Irshad H., Microsc Res Tech 80(8), 2017
PMID: 28379628
Involvement of Machine Learning for Breast Cancer Image Classification: A Survey.
Nahid AA, Kong Y., Comput Math Methods Med 2017(), 2017
PMID: 29463985
Nahid AA, Kong Y., Comput Math Methods Med 2017(), 2017
PMID: 29463985
A computer-aided diagnosis system for dynamic contrast-enhanced MR images based on level set segmentation and ReliefF feature selection.
Pang Z, Zhu D, Chen D, Li L, Shao Y., Comput Math Methods Med 2015(), 2015
PMID: 25628755
Pang Z, Zhu D, Chen D, Li L, Shao Y., Comput Math Methods Med 2015(), 2015
PMID: 25628755
Supervised embedding of textual predictors with applications in clinical diagnostics for pediatric cardiology.
Perry TE, Zha H, Zhou K, Frias P, Zeng D, Braunstein M., J Am Med Inform Assoc 21(e1), 2014
PMID: 24076750
Perry TE, Zha H, Zhou K, Frias P, Zeng D, Braunstein M., J Am Med Inform Assoc 21(e1), 2014
PMID: 24076750
A vector machine formulation with application to the computer-aided diagnosis of breast cancer from DCE-MRI screening examinations.
Levman JE, Warner E, Causer P, Martel AL., J Digit Imaging 27(1), 2014
PMID: 23836079
Levman JE, Warner E, Causer P, Martel AL., J Digit Imaging 27(1), 2014
PMID: 23836079
Classification of dynamic contrast enhanced MR images of cervical cancers using texture analysis and support vector machines.
Torheim T, Malinen E, Kvaal K, Lyng H, Indahl UG, Andersen EK, Futsaether CM., IEEE Trans Med Imaging 33(8), 2014
PMID: 24802069
Torheim T, Malinen E, Kvaal K, Lyng H, Indahl UG, Andersen EK, Futsaether CM., IEEE Trans Med Imaging 33(8), 2014
PMID: 24802069
Characterization of spatiotemporal changes for the classification of dynamic contrast-enhanced magnetic-resonance breast lesions.
Milenković J, Hertl K, Košir A, Zibert J, Tasič JF., Artif Intell Med 58(2), 2013
PMID: 23548472
Milenković J, Hertl K, Košir A, Zibert J, Tasič JF., Artif Intell Med 58(2), 2013
PMID: 23548472
Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy.
Mani S, Chen Y, Li X, Arlinghaus L, Chakravarthy AB, Abramson V, Bhave SR, Levy MA, Xu H, Yankeelov TE., J Am Med Inform Assoc 20(4), 2013
PMID: 23616206
Mani S, Chen Y, Li X, Arlinghaus L, Chakravarthy AB, Abramson V, Bhave SR, Levy MA, Xu H, Yankeelov TE., J Am Med Inform Assoc 20(4), 2013
PMID: 23616206
A new endoscopic ultrasonography image processing method to evaluate the prognosis for pancreatic cancer treated with interstitial brachytherapy.
Xu W, Liu Y, Lu Z, Jin ZD, Hu YH, Yu JG, Li ZS., World J Gastroenterol 19(38), 2013
PMID: 24151368
Xu W, Liu Y, Lu Z, Jin ZD, Hu YH, Yu JG, Li ZS., World J Gastroenterol 19(38), 2013
PMID: 24151368
3D lacunarity in multifractal analysis of breast tumor lesions in dynamic contrast-enhanced magnetic resonance imaging.
Soares F, Janela F, Pereira M, Seabra J, Freire MM., IEEE Trans Image Process 22(11), 2013
PMID: 24057004
Soares F, Janela F, Pereira M, Seabra J, Freire MM., IEEE Trans Image Process 22(11), 2013
PMID: 24057004
Computer-aided diagnosis for dynamic contrast-enhanced breast MRI of mass-like lesions using a multiparametric model combining a selection of morphological, kinetic, and spatiotemporal features.
Agliozzo S, De Luca M, Bracco C, Vignati A, Giannini V, Martincich L, Carbonaro LA, Bert A, Sardanelli F, Regge D., Med Phys 39(4), 2012
PMID: 22482596
Agliozzo S, De Luca M, Bracco C, Vignati A, Giannini V, Martincich L, Carbonaro LA, Bert A, Sardanelli F, Regge D., Med Phys 39(4), 2012
PMID: 22482596
New spatiotemporal features for improved discrimination of benign and malignant lesions in dynamic contrast-enhanced-magnetic resonance imaging of the breast.
Gal Y, Mehnert A, Bradley A, Kennedy D, Crozier S., J Comput Assist Tomogr 35(5), 2011
PMID: 21926864
Gal Y, Mehnert A, Bradley A, Kennedy D, Crozier S., J Comput Assist Tomogr 35(5), 2011
PMID: 21926864
Early prediction of the response of breast tumors to neoadjuvant chemotherapy using quantitative MRI and machine learning.
Mani S, Chen Y, Arlinghaus LR, Li X, Chakravarthy AB, Bhave SR, Welch EB, Levy MA, Yankeelov TE., AMIA Annu Symp Proc 2011(), 2011
PMID: 22195145
Mani S, Chen Y, Arlinghaus LR, Li X, Chakravarthy AB, Bhave SR, Welch EB, Levy MA, Yankeelov TE., AMIA Annu Symp Proc 2011(), 2011
PMID: 22195145
Computer-aided diagnostic models in breast cancer screening.
Ayer T, Ayvaci MU, Liu ZX, Alagoz O, Burnside ES., Imaging Med 2(3), 2010
PMID: 20835372
Ayer T, Ayvaci MU, Liu ZX, Alagoz O, Burnside ES., Imaging Med 2(3), 2010
PMID: 20835372
Differential diagnosis of pancreatic cancer from normal tissue with digital imaging processing and pattern recognition based on a support vector machine of EUS images.
Zhang MM, Yang H, Jin ZD, Yu JG, Cai ZY, Li ZS., Gastrointest Endosc 72(5), 2010
PMID: 20855062
Zhang MM, Yang H, Jin ZD, Yu JG, Cai ZY, Li ZS., Gastrointest Endosc 72(5), 2010
PMID: 20855062
Model-based and model-free parametric analysis of breast dynamic-contrast-enhanced MRI.
Eyal E, Degani H., NMR Biomed 22(1), 2009
PMID: 18022997
Eyal E, Degani H., NMR Biomed 22(1), 2009
PMID: 18022997
Effect of the enhancement threshold on the computer-aided detection of breast cancer using MRI.
Levman JE, Causer P, Warner E, Martel AL., Acad Radiol 16(9), 2009
PMID: 19515584
Levman JE, Causer P, Warner E, Martel AL., Acad Radiol 16(9), 2009
PMID: 19515584
Classification of dynamic contrast-enhanced magnetic resonance breast lesions by support vector machines.
Levman J, Leung T, Causer P, Plewes D, Martel AL., IEEE Trans Med Imaging 27(5), 2008
PMID: 18450541
Levman J, Leung T, Causer P, Plewes D, Martel AL., IEEE Trans Med Imaging 27(5), 2008
PMID: 18450541
Visual MRI: merging information visualization and non-parametric clustering techniques for MRI dataset analysis.
Castellani U, Cristani M, Combi C, Murino V, Sbarbati A, Marzola P., Artif Intell Med 44(3), 2008
PMID: 18775655
Castellani U, Cristani M, Combi C, Murino V, Sbarbati A, Marzola P., Artif Intell Med 44(3), 2008
PMID: 18775655
30 References
Daten bereitgestellt von Europe PubMed Central.
Application of magnetic resonance imaging to early detection of breast cancer.
Schnall MD., Breast Cancer Res. 3(1), 2001
PMID: 11300101
Schnall MD., Breast Cancer Res. 3(1), 2001
PMID: 11300101
Heywang-Kobrunner, 1996
Classification of signal-time curves from dynamic MR mammography by neural networks.
Lucht RE, Knopp MV, Brix G., Magn Reson Imaging 19(1), 2001
PMID: 11295347
Lucht RE, Knopp MV, Brix G., Magn Reson Imaging 19(1), 2001
PMID: 11295347
Contrast-enhanced MRI of the breast: accuracy, value, controversies, solutions.
Heywang-Kobrunner SH, Viehweg P, Heinig A, Kuchler C., Eur J Radiol 24(2), 1997
PMID: 9097051
Heywang-Kobrunner SH, Viehweg P, Heinig A, Kuchler C., Eur J Radiol 24(2), 1997
PMID: 9097051
Diagnostic performance characteristics of architectural features revealed by high spatial-resolution MR imaging of the breast.
Nunes LW, Schnall MD, Siegelman ES, Langlotz CP, Orel SG, Sullivan D, Muenz LA, Reynolds CA, Torosian MH., AJR Am J Roentgenol 169(2), 1997
PMID: 9242744
Nunes LW, Schnall MD, Siegelman ES, Langlotz CP, Orel SG, Sullivan D, Muenz LA, Reynolds CA, Torosian MH., AJR Am J Roentgenol 169(2), 1997
PMID: 9242744
Improved diagnostic accuracy in dynamic contrast enhanced MRI of the breast by combined quantitative and qualitative analysis.
Liu PF, Debatin JF, Caduff RF, Kacl G, Garzoli E, Krestin GP., Br J Radiol 71(845), 1998
PMID: 9691895
Liu PF, Debatin JF, Caduff RF, Kacl G, Garzoli E, Krestin GP., Br J Radiol 71(845), 1998
PMID: 9691895
MR imaging of the breast with rotating delivery of excitation off resonance: clinical experience with pathologic correlation.
Harms SE, Flamig DP, Hesley KL, Meiches MD, Jensen RA, Evans WP, Savino DA, Wells RV., Radiology 187(2), 1993
PMID: 8475297
Harms SE, Flamig DP, Hesley KL, Meiches MD, Jensen RA, Evans WP, Savino DA, Wells RV., Radiology 187(2), 1993
PMID: 8475297
Dynamic sequential 3D gadolinium-enhanced MRI of the whole breast.
Heiberg EV, Perman WH, Herrmann VM, Janney CG., Magn Reson Imaging 14(4), 1996
PMID: 8782170
Heiberg EV, Perman WH, Herrmann VM, Janney CG., Magn Reson Imaging 14(4), 1996
PMID: 8782170
Healthy premenopausal breast parenchyma in dynamic contrast-enhanced MR imaging of the breast: normal contrast medium enhancement and cyclical-phase dependency.
Kuhl CK, Bieling HB, Gieseke J, Kreft BP, Sommer T, Lutterbey G, Schild HH., Radiology 203(1), 1997
PMID: 9122382
Kuhl CK, Bieling HB, Gieseke J, Kreft BP, Sommer T, Lutterbey G, Schild HH., Radiology 203(1), 1997
PMID: 9122382
Magnetic resonance imaging screening in women at genetic risk of breast cancer: imaging and analysis protocol for the UK multicentre study. UK MRI Breast Screening Study Advisory Group.
Brown J, Buckley D, Coulthard A, Dixon AK, Dixon JM, Easton DF, Eeles RA, Evans DG, Gilbert FG, Graves M, Hayes C, Jenkins JP, Jones AP, Keevil SF, Leach MO, Liney GP, Moss SM, Padhani AR, Parker GJ, Pointon LJ, Ponder BA, Redpath TW, Sloane JP, Turnbull LW, Walker LG, Warren RM., Magn Reson Imaging 18(7), 2000
PMID: 11027869
Brown J, Buckley D, Coulthard A, Dixon AK, Dixon JM, Easton DF, Eeles RA, Evans DG, Gilbert FG, Graves M, Hayes C, Jenkins JP, Jones AP, Keevil SF, Leach MO, Liney GP, Moss SM, Padhani AR, Parker GJ, Pointon LJ, Ponder BA, Redpath TW, Sloane JP, Turnbull LW, Walker LG, Warren RM., Magn Reson Imaging 18(7), 2000
PMID: 11027869
Genetic linkage analysis in familial breast and ovarian cancer: results from 214 families. The Breast Cancer Linkage Consortium.
Easton DF, Bishop DT, Ford D, Crockford GP., Am. J. Hum. Genet. 52(4), 1993
PMID: 8460634
Easton DF, Bishop DT, Ford D, Crockford GP., Am. J. Hum. Genet. 52(4), 1993
PMID: 8460634
Comparison between radiological and artificial neural network diagnosis in clinical screening.
Degenhard A, Tanner C, Hayes C, Hawkes DJ, Leach MO; UK MRI Breast Screening Study., Physiol Meas 23(4), 2002
PMID: 12450272
Degenhard A, Tanner C, Hayes C, Hawkes DJ, Leach MO; UK MRI Breast Screening Study., Physiol Meas 23(4), 2002
PMID: 12450272
An evolutionary artificial neural networks approach for breast cancer diagnosis
Abbass, Artif Intell Med 25(3), 2001
Abbass, Artif Intell Med 25(3), 2001
Self-organizing map for cluster analysis of a breast cancer database.
Markey MK, Lo JY, Tourassi GD, Floyd CE Jr., Artif Intell Med 27(2), 2003
PMID: 12636975
Markey MK, Lo JY, Tourassi GD, Floyd CE Jr., Artif Intell Med 27(2), 2003
PMID: 12636975
Model selection for a medical diagnostic decision support system: a breast cancer detection case.
West D, West V., Artif Intell Med 20(3), 2000
PMID: 10998586
West D, West V., Artif Intell Med 20(3), 2000
PMID: 10998586
Multisurface method of pattern separation for medical diagnosis applied to breast cytology
Wolberg, 1990
Wolberg, 1990
Bishop, 1997
Kohonen, 1989
Kohonen, 2000
Machine learning for medical diagnosis: history, state of the art and perspective.
Kononenko I., Artif Intell Med 23(1), 2001
PMID: 11470218
Kononenko I., Artif Intell Med 23(1), 2001
PMID: 11470218
Kernel principal component analysis
Schölkopf, 1999
Schölkopf, 1999
Fisher discriminant analysis with kernels
Mika, 1999
Mika, 1999
Vapnik, 1995
Fast training of support vector machines using sequential minimal optimization
Platt, 1998
Platt, 1998
AUTHOR UNKNOWN, 0
Quinlan, 1993
On the well-behavedness of important attribute evaluation functions
Elomaa, 1997
Elomaa, 1997
Multi-interval discretization of continuous-valued attributes for classification learning
Fayyad, 1993
Fayyad, 1993
Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine.
Zweig MH, Campbell G., Clin. Chem. 39(4), 1993
PMID: 8472349
Zweig MH, Campbell G., Clin. Chem. 39(4), 1993
PMID: 8472349
Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests.
Greiner M, Pfeiffer D, Smith RD., Prev. Vet. Med. 45(1-2), 2000
PMID: 10802332
Greiner M, Pfeiffer D, Smith RD., Prev. Vet. Med. 45(1-2), 2000
PMID: 10802332
Export
Markieren/ Markierung löschen
Markierte Publikationen
Web of Science
Dieser Datensatz im Web of Science®Quellen
PMID: 15894177
PubMed | Europe PMC
Suchen in