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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