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Proceedings Paper

A prostate cancer computer-aided diagnosis system using multimodal magnetic resonance imaging and targeted biopsy labels
Author(s): Peter Liu; Shijun Wang; Baris Turkbey; Kinzya Grant; Peter Pinto; Peter Choyke; Bradford J. Wood; Ronald M. Summers
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Paper Abstract

We propose a new method for prostate cancer classification based on supervised statistical learning methods by integrating T2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI images with targeted prostate biopsy results. In the first step of the method, all three imaging modalities are registered based on the image coordinates encoded in the DICOM images. In the second step, local statistical features are extracted in each imaging modality to capture intensity, shape, and texture information at every biopsy target. Finally, using support vector machines, supervised learning is conducted with the biopsy results to train a classification system that predicts the pathology of suspicious cancer lesions. The algorithm was tested with a dataset of 54 patients that underwent 164 targeted biopsies (58 positive, 106 negative). The proposed tri-modal MRI algorithm shows significant improvement over a similar approach that utilizes only T2-weighted MRI images (p= 0.048). The areas under the ROC curve for these methods were 0.82 (95% CI: [0.71, 0.93]) and 0.73 (95% CI: [0.55, 0.84]), respectively.

Paper Details

Date Published: 26 February 2013
PDF: 6 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86701G (26 February 2013); doi: 10.1117/12.2007927
Show Author Affiliations
Peter Liu, National Institutes of Health Clinical Ctr. (United States)
Shijun Wang, National Institutes of Health Clinical Ctr. (United States)
Baris Turkbey, National Cancer Ctr., National Institutes of Health (United States)
Kinzya Grant, National Cancer Ctr., National Institutes of Health (United States)
Peter Pinto, National Cancer Ctr., National Institutes of Health (United States)
Peter Choyke, National Cancer Ctr., National Institutes of Health (United States)
Bradford J. Wood, National Institutes of Health Clinical Ctr. (United States)
Ronald M. Summers, National Institutes of Health Clinical Ctr. (United States)


Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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