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Assessment of prostate cancer aggressiveness is important because the effectiveness of treatment vary depending on the aggressiveness. The use of multi-parametric MR imaging prior to biopsy is recommended for accurate prostate cancer aggressiveness assessment but suffers from similar visual appearance of tumors between adjacent grades. To improve the predictive performance of prostate cancer aggressiveness, this study proposes a deep regression model involving size-normalized patch generation and multiple losses. First, we generate two types of input patches such as tumor-centered patch and size-normalized patch to effectively learn the characteristics of small tumors. Second, we propose a multiple loss functions consisting of triplet loss, mean squared error, and cross-entropy ordinal loss to increase the ability to discriminate between tumors with similar visual appearance and different aggressiveness. As a result, the proposed model trained with the size-normalized ADC map showed the highest performance with an accuracy of 78.85%, specificity of 89.66%, and AUC of 0.77. The ensemble model of tumor-centered T2w image and size-normalized ADC map improved sensitivity by 8.69% and showed the best performance with accuracy of 78.85%.
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