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The patients with prostate-specific antigen (PSA) levels of 4 ng/mL and above are considered for a prostate biopsy to rule out prostate cancer (PCa). However, the specificity of PSA test is not satisfied, especially in the PSA gray zone of 4 to 10 ng/mL. In this study, we aimed to assess the feasibility of a combined approach of radiomics and machine learning based on bpMRI images for a non-invasive diagnosis of PCa in PSA gray zone cases, specifically differentiation of PCa and benign prostatic hyperplasia (BPH). Images acquired on a 3-Tesla scanner (T2-weighted and diffusion-weighted imaging) from 103 patients (54 with PCa and 49 with BPH) were annotated to generate volumes of interest enclosing lesions. After image resampling and filtering, 2300 features were extracted. The Wilcoxon rank-sum test and LASSO regression algorithm was applied to select the radiomics features for building models. The binary logistics regression model of selected radiomics features was constructed with 4-fold cross validation and the rad-scores of BPH and PCa were calculated. The AUC of both models from T2WI and ADC showed satisfactory diagnostic performances (AUC > 0.9). The best results in terms of accuracy (80.9%) in test set were achieved by ADC model with 5 radiomics features. These evidences support the hypothesis that machine learning-based bpMRI radiomics models might be a potential and practical pathway to clinicians to better clinical decision-making and reduce the number of unnecessary prostate biopsies.
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