Background Artificial intelligence (AI) is the recently advanced technology in machine learning which is increasingly used to help radiologists, especially when working in arduous conditions. Microsoft Corporation offered a free-trial service calling Custom Vision to develop AI for images. Results This study included 161 prostate cancer images with 189 lesions from 52 patients. The 160-tag iteration presented the best performance: precision 20.0%, recall 6.3%, mean average precision (M.A.P.) 13.1%, and prediction rate 31.58%. The performance of a 1-h training was better than quick training, but was not different from a 2-h training. Conclusion Health personnel can easily develop AI for the detection of prostate cancer lesions in MRI. However, the AI development is further required, and the result should be interpreted along with radiologist.

Background The PSAD calculating by the serum PSA level divided by prostate volume had more specificity and accuracy than the serum PSA level for detection of prostate cancer. Methods MRI examinations of 319 patients who had suspected prostate cancer between January 2014 and December 2019 were retrospectively reviewed. Prostate volumes were measured by MRI images and PSAD values were calculated. The accuracy and optimal cutoff points of MRI-based PSAD were evaluated using receiver operating characteristic curves (ROC curves). Correlations between the MRI-based PSAD and Gleason scores were also analyzed to predict prognosis of prostate cancer. Results Overall, of 154 patients were included in this study, 59 patients (38.31%) were diagnosed with prostate cancer. The optimal cutoff point of PSAD was 0.16 (81.40% sensitivity, 54.70% specificity, 52.70% PPV, 82.50% NPV), and the AUC was 0.680 (95% CI: 0.609–0.751). In subgroup analyses, the optimal cutoff point of PSAD in patients with serum PSA 4–10 ng/ml was 0.16 (61.10% sensitivity, 76.00% specificity) and for > 10 ng/ml was 0.30 (68.30% sensitivity, 64.30% specificity). Furthermore, there was a statistically significant correlation between PSAD and Gleason scores (p-value 0.014). Conclusions The optimal cutoff point of MRI-based PSAD was 0.16 which was relatively different from international consensus.

No abstract

No abstract

Background BPH is commonly found in older men which can lead to lower urinary tract symptoms. Magnetic resonance elastography (MRE) is an innovative, noninvasive imaging technique used to evaluate tissue stiffness. There has not been any study, however, that assessed the tissue stiffness in patients with BPH. A prospective descriptive study was performed to demonstrated MRI and MRE techniques of the prostate gland in ten patients with BPH to assess tissue stiffness, features of BPH on MRI and components of BPH in the area of increased stiffness. Results MRI and MRE examinations in all patients were successful without any complications. The mean tissue stiffness of the whole prostate gland was 4.40 ± 0.71 kPa with good reproducibility (ICC 0.82). Stromal components and mixed glandular-stromal components tended to be associated with the areas of increased stiffness on stiffness images, 50.6% for stromal components and 37.9% for mixed glandular-stromal components. Some MRI findings were seen on the patients with high mean stiffness values such as prostatic calcification, type-5 BPH pattern and large prostate volumes. Conclusions Prostate MRE is a useful noninvasive reproducible diagnostic tool for evaluating prostate tissue stiffness by both qualitative and quantitative assessments. The mean prostate tissue stiffness from MRE in patients with BPH in this study was 4.40 ± 0.71 kPa. Some MRI features might be associated with increased tissue stiffness. Trial registration: PID 229. Registered 4 October 2019. http://md.redcap.kku.ac.th