Background: The histologic grade (Gleason score) as well as the tumor ratio of the resected specimen plays a significant role in assessing the clinical risk of prostate cancer patients who underwent radical prostatectomy. Gleason scoring is known to be prone to inter- and intra-observer discordance, and eyeballing measurement of tumor ratio is possibly coarse and inaccurate. Several deep learning-based tissue image analysis algorithms that perform prostate cancer diagnosis have been developed and are about to be applied clinically. We evaluated the utility of one of those algorithms by comparing its output with the pathology reports. Methods: A total of 29681 H&E-stained tissue slides were collected for 1001 radical prostatectomy cases during 2010-2021 at Pusan National University Hospital and scanned at 40x magnification into whole-slide images. On each slide, we utilized a tissue detection algorithm to identify tissue regions and measure the area. A deep learning-based algorithm was used to identify prostate cancer lesions, measure the area, and determine the grades. The area measurement was converted into the volume figure assuming that the tissue slice thickness was 5mm. The slide-wise algorithm outputs were then aggregated for each case, resulting in the specimen volume, tumor ratio, and Gleason score. In the evaluation, the algorithm-based Gleason scores were compared with the ones in the hospital pathology reports on the ISUP grade group basis. The correlation analysis was performed for the tumor ratio. The specimen density values were calculated from the volume figures and the hospital weight measurement and utilized to exclude the cases with extreme density values. Results: The min, max, and average number of slides per case were 1, 67, and 29.7. For two cases with a single slide collected, both the algorithm and the expert pathologist found no cancer. The pathologist also confirmed that 7 had residual tumors among 15 cases where there was no Gleason score in the pathology reports while the algorithm found cancer. For the remaining 984 cases, the algorithm provided the grade groups equal to, higher than, and lower than the pathology reports for 482, 384, and 118 cases respectively, showing moderate agreement. The min, max, and mean values of the specimen volume (mL) were 0.01, 75.3, and 32.7, while the corresponding values of the specimen density (g/mL) were 0.11, 3001.79, and 4.38. The density values at the lower and upper 2.5% were 0.42 and 1.40 respectively, and the mean of the values between them (inner 95%) was 0.92. The degree of tumor ratio correlation between the algorithm and the pathology reports was high with the coefficient 0.813 (95% CI: 0.791-0.833), which went up to 0.826 (95% CI: 0.805-0.845) for the inner 95% density cases. Conclusions: Our findings support the usefulness of the algorithm-based analysis of prostatectomy specimens, which can benefit clinicians in the hospital.