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Objectives: To evaluate the effect of a deep learning-based computer-aided diagnosis (DL-CAD) system on experienced and less-experienced radiologists in reading prostate mpMRI., Methods: In this retrospective, multi-reader multi-case study, a consecutive set of 184 patients examined between 01/2018 and 08/2019 were enrolled. Ground truth was combined targeted and 12-core systematic transrectal ultrasound-guided biopsy. Four radiologists, two experienced and two less-experienced, evaluated each case twice, once without (DL-CAD-) and once assisted by DL-CAD (DL-CAD+). ROC analysis, sensitivities, specificities, PPV and NPV were calculated to compare the diagnostic accuracy for the diagnosis of prostate cancer (PCa) between the two groups (DL-CAD- vs. DL-CAD+). Spearman's correlation coefficients were evaluated to assess the relationship between PI-RADS category and Gleason score (GS). Also, the median reading times were compared for the two reading groups., Results: In total, 172 patients were included in the final analysis. With DL-CAD assistance, the overall AUC of the less-experienced radiologists increased significantly from 0.66 to 0.80 (p = 0.001; cutoff ISUP GG ≥ 1) and from 0.68 to 0.80 (p = 0.002; cutoff ISUP GG ≥ 2). Experienced radiologists showed an AUC increase from 0.81 to 0.86 (p = 0.146; cutoff ISUP GG ≥ 1) and from 0.81 to 0.84 (p = 0.433; cutoff ISUP GG ≥ 2). Furthermore, the correlation between PI-RADS category and GS improved significantly in the DL-CAD + group (0.45 vs. 0.57; p = 0.03), while the median reading time was reduced from 157 to 150 s (p = 0.023)., Conclusions: DL-CAD assistance increased the mean detection performance, with the most significant benefit for the less-experienced radiologist; with the help of DL-CAD less-experienced radiologists reached performances comparable to that of experienced radiologists., Key Points: • DL-CAD used as a concurrent reading aid helps radiologists to distinguish between benign and cancerous lesions in prostate MRI. • With the help of DL-CAD, less-experienced radiologists may achieve detection performances comparable to that of experienced radiologists. • DL-CAD assistance increases the correlation between PI-RADS category and cancer grade., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Authors aimed to assess the correlation between the apparent diffusion coefficient (ADC of the tumor, ADC ratio) and final grade group (GG) after radical prostatectomy (RP), and to determine the threshold values of ADC for detecting clinically significant prostate cancer (PC) with subsequent evaluation in a prospective group. 118 patients with PC were included in the retrospective group. These patients underwent RP from 2012 to 2017 with preoperative 3 Tesla multiparametric MRI (mpMRT) with contrast enhancement in a single center. After analyzing all the MRI studies, the average values of tumor ADC and benign tissue ADC were calculated using the maps of ADC. The prospective part of the study included 60 patients with completed pre-biopsy mpMRI and subsequent RP from January 2018 to March 2019. The prospective part of the study demonstrated the effectiveness of applying the obtained diffusion coefficient thresholds. When used as a criterion for determining clinically significant prostate cancer (GG > 6), threshold value of ADC ratio had sensitivity, specificity, accuracy, positive predictive value and negative predictive value were 84%, 91%, 87%, 94% and 78% respectively.