Background/Objectives: Prostate cancer (PCa) is the most diagnosed cancer in men worldwide. Early diagnosis of the disease provides better treatment options for these patients. Recent studies have demonstrated that plasma-based extracellular vesicle microRNAs (miRNAs) are functionally linked to cancer progression, metastasis, and aggressiveness. The use of magnetic resonance imaging (MRI) as the standard of care provides an overall assessment of prostate disease. Quantitative metrics (radiomics) from the MRI provide a better evaluation of the tumor and have been shown to improve disease detection. Methods: We conducted a study on prostate cancer patients, analyzing baseline blood plasma and MRI data. Exosomes were isolated from blood plasma samples to quantify miRNAs, while MRI scans provided detailed tumor morphology. Radiomics features from MRI and miRNA expression data were integrated to develop predictive models, which were evaluated using ROC curve analysis, highlighting the multivariable model’s effectiveness. Results: Our findings indicate that the univariate feature-based model with the highest Youden’s index achieved average areas under the receiver operating characteristic (ROC) curve of 0.76, 0.82, and 0.84 for miRNA, MR-T2W, and MR-ADC features, respectively, in identifying clinically aggressive (Gleason grade) disease. The multivariable feature-based model yielded an average area under the curve (AUC) of 0.88 and 0.95 using combinations of miRNA markers with imaging features in MR-ADC and MR-T2W, respectively. Conclusions: Our study demonstrates that combining miRNA markers with MRI-based radiomics improves the identification of clinically aggressive prostate cancer.

Prostate cancer is the most commonly diagnosed cancer in men worldwide. Early diagnosis of the disease provides better treatment options for these patients. Magnetic resonance imaging (MRI) provides an overall assessment of prostate disease. Quantitative metrics (radiomics) from the MRI provide a better evaluation of the tumor and have been shown to improve disease detection. Recent studies have demonstrated that plasma extracellular vesicle microRNAs (miRNAs) are functionally linked to cancer progression, metastasis, and aggressiveness. In our study, we analyzed a matched cohort with baseline blood plasma and MRI to access tumor morphology using imaging-based radiomics and cellular characteristics using miRNAs-based transcriptomics. Our findings indicate that the univariate feature-based model with the highest Youden index achieved average areas under the receiver operating characteristic curve (AUC) of 0.76, 0.82, and 0.84 for miRNA, MR-T2W, and MR-ADC features, respectively, in identifying clinically aggressive (Gleason grade) disease. The multivariable feature-based model demonstrated an average AUC of 0.88 and 0.95 using combinations of miRNA markers with imaging features in MR-ADC and MR- T2W, respectively. Our study demonstrates combining miRNA markers with MRI-based radiomics improves predictability of clinically aggressive prostate cancer.