• Brent J Woods, Bradley D Clymer, Tahsin Kurc, Johannes T Heverhagen, Robert Stevens, Adem Orsdemir, Orhan Bulan, and Michael V Knopp.
• Department of Electrical and Computer Engineering, Ohio State University, Columbus, Ohio 43210, USA.
• J Magn Reson Imaging. 2007 Mar 1; 25 (3): 495-501.

PurposeTo investigate the use of four-dimensional (4D) co-occurrence-based texture analysis to distinguish between nonmalignant and malignant tissues in dynamic contrast-enhanced (DCE) MR images.Materials And Methods4D texture analysis was performed on DCE-MRI data sets of breast lesions. A model-free neural network-based classification system assigned each voxel a "nonmalignant" or "malignant" label based on the textural features. The classification results were compared via receiver operating characteristic (ROC) curve analysis with the manual lesion segmentation produced by two radiologists (observers 1 and 2).ResultsThe mean sensitivity and specificity of the classifier agreed with the mean observer 2 performance when compared with segmentations by observer 1 for a 95% confidence interval, using a two-sided t-test with alpha = 0.05. The results show that an area under the ROC curve (A(z)) of 0.99948, 0.99867, and 0.99957 can be achieved by comparing the classifier vs. observer 1, classifier vs. union of both observers, and classifier vs. intersection of both observers, respectively.ConclusionThis study shows that a neural network classifier based on 4D texture analysis inputs can achieve a performance comparable to that achieved by human observers, and that further research in this area is warranted.

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