Circulating tumor cells (CTCs) have been shown to be an indicator for metastatic risk in prostate cancer. We investigated the association between radiomics features extracted from multiparametric MRI of the prostate and CTC counts in prostate cancer patients enrolled in two institutional clinical trials (n=71). We trained a neural network to predict the dichotomized CTCs counts, defined by a 5 CTCs threshold. The top seven features, ranked using maximum-relevance minimum-redundancy, were used as input to a neural network. The training and testing were repeated for 100 runs of 5-Fold cross validation, resulting in AUC 0.834 to predict CTCs ≥ 5.
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