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


METHODS
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).


CONCLUSIONS
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.
Nattkemper
Tim W
TW
Applied Neuroinformatics Group, Bielefeld University, P.O. Box 100130, D-33501 Bielefeld, Germany. tnattkem@techfak.uni-bielefeld.de
Arnrich
Bert
B
Lichte
Oliver
O
Timm
Wiebke
W
Degenhard
Andreas
A
Pointon
Linda
L
Hayes
Carmel
C
Leach
Martin O
MO
D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003663:Decision Trees; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D011859:Radiography