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Abstract #2061

Development of a lesion-wise metric for evaluation of predictive models of prostate cancer on multiparametric MRI

Ethan Leng1, Jin Jin2, Lin Zhang2, Joseph S. Koopmeiners2, and Gregory J. Metzger1

1Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States, 2Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN

A novel lesion-wise metric was developed to evaluate the quality of predictive models of prostate cancer that use quantitative multiparametric MR data to perform prediction on a voxel-wise basis. The metric is based on the Jaccard similarity coefficient and emphasizes overlap and co-localization of ground truth and predicted lesions. Experiments to characterize the metric demonstrated that it qualitatively reflected the goodness of predictions and was more accurate and informative than voxel-wise measures of sensitivity and specificity. We propose that the metric may be customized to select the best predictive models for specific clinical applications such as performing targeted prostate biopsies.

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