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A prostate cancer computer-aided diagnosis system using multimodal magnetic resonance imaging and targeted biopsy labels

文献类型： 会议论文

第一作者： Peter Liu

作者： Peter Liu 1 ; Shijun Wang 1 ; Boris Turkbey 2 ; Kinzya Grant 2 ; Peter Pinto 2 ; Peter Choyke 2 ; Bradford J. Wood 3 ; Ronald M. Summers 1 ;

作者机构： 1.Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10 Room 1C224 MSC 1182, Bethesda, MD 20892-1182

2.Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bldg 10, Room B3B69F, Bethesda, MD 20892-1088

3.Interventional Radiology Section, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bldg 10, Room 1C341, Bethesda, MD 20892-1182

关键词： Prostate cancer imaging;multimodality analysis;statistical learning;computer-aided diagnosis;biopsy label;MRI

会议名称： Conference on computer-aided diagnosis

主办单位：

页码： 86701G.1-86701G.6

摘要： 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.

分类号： r312

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