The RBIE method improved the quality of elastographic moving images compared with the manual compression method. High-grade tumors and tumors of impalpable regions of the prostate were more frequently detected using RBIE. We conclude that RBIE is a promising method with which to detect prostate cancer.

The number of patients undergoing robotic surgery for rectal cancer has rapidly increased in Japan, since the government approved the procedure for national insurance coverage in April 2018. Robotic surgery has the potential to overcome some limitations of laparoscopic surgery, especially in the narrow pelvis, providing a three-dimensional view, articulated instruments, and a stable camera platform. Although meta-analyses and randomized controlled trials have failed to demonstrate the superiority of robotic surgery over laparoscopic surgery with respect to the short-term clinical outcomes, the published findings suggest that robotic surgery may be potentially beneficial for patients who are obese, male, or patients undergoing sphincterpreserving surgery for rectal cancer. The safety and feasibility of robotic surgery for lateral lymph node dissection, the standard procedure for locally advanced lower rectal cancer in Japan, have been demonstrated in some retrospective studies. However, additional prospective, randomized trials are required to determine the actual benefits of robotic surgery to ameliorate the urogenital and oncological outcomes. The cost of this approach is a long-standing principal concern. A literature search showed that the cost of robotic surgery for rectal cancer was 1.3-2.5 times higher per patient than that for the laparoscopic approach. We herein describe our surgical technique using a da Vinci Surgical System (S/Si/Xi) with 10 years of experience in performing robotic surgery. We also review current evidence regarding short-term clinical and longterm oncological outcomes, lateral lymph node dissection, and the cost of the procedure.

No abstract

No abstract

Background Prostate-specific antigen (PSA)–based screening for prostate cancer has been widely performed, but its accuracy is unsatisfactory. To improve accuracy, building an effective statistical model using machine learning methods (MLMs) is a promising approach. Methods Data on continuous changes in the PSA level over the past 2 years were accumulated from 512 patients who underwent prostate biopsy after PSA screening. The age of the patients, PSA level, prostate volumes, and white blood cell count in urinalysis were used as input data for the MLMs. As MLMs, we evaluated the efficacy of three different techniques: artificial neural networks (ANNs), random forest, and support vector machine. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and compared with the PSA level and the conventional PSA–based parameters: PSA density and PSA velocity. Results When using two annual PSA testing, all receiver operating characteristic curves of the three MLMs were above the curve for the PSA level, PSA density, and PSA velocity. The AUCs of ANNs, random forest, and support vector machine were 0.69, 0.64, and 0.63, respectively. Those values were higher than the AUCs of the PSA level, PSA density, and PSA velocity, 0.53, 0.41, and 0.55, respectively. The accuracies of the MLMs (71.6% to 72.1%) were also superior to those of the PSA level (39.1%), PSA density (49.7%), and PSA velocity (54.9%). Among the MLMs, ANNs showed the most favorable AUC. The MLMs showed higher sensitivity and specificity than conventional PSA–based parameters. The model performance did not improve when using three annual PSA testing. Conclusion The present retrospective study results indicate that machine learning techniques can predict prostate cancer with significantly better AUCs than those of PSA density and PSA velocity.