Peritoneal dissemination of prostate cancer due to laparoscopic radical prostatectomy: a case report

<p>Abstract</p> <p>Introduction</p> <p>Peritoneal dissemination with no further metastases of prostate cancer is very rare, with only three cases reported in the available literature. We report the first case of iatrogenic peritoneal dissemination due to laparoscopic radical prostatectomy.</p> <p>Case Presentation</p> <p>A 59-year-old Japanese man underwent laparoscopic radical prostatectomy for clinical T2bN0M0 prostate cancer, and the pathological diagnosis was pT3aN0 Gleason 3+4 adenocarcinoma with a negative surgical margin. Salvage radiation therapy was performed since his serum prostate-specific antigen remained at a measurable value. After the radiation, he underwent castration, followed by combined androgen blockade with estramustine phosphate and dexamethasone as each treatment was effective for only a few months to a year. Nine years after the laparoscopic radical prostatectomy, computed tomography revealed a peritoneal tumor, although no other organ metastasis had been identified until then. He died six months after the appearance of peritoneal metastasis. An autopsy demonstrated peritoneal dissemination of the prostate cancer without any other metastasis.</p> <p>Conclusion</p> <p>Physicians should take into account metastasis to unexpected sites. Furthermore, we suggest that meticulous care be taken not to disseminate cancer cells to the peritoneum during laparoscopic radical prostatectomy.</p

Semantic Scene Difference Detection in Daily Life Patroling by Mobile Robots using Pre-Trained Large-Scale Vision-Language Model

It is important for daily life support robots to detect changes in their environment and perform tasks. In the field of anomaly detection in computer vision, probabilistic and deep learning methods have been used to calculate the image distance. These methods calculate distances by focusing on image pixels. In contrast, this study aims to detect semantic changes in the daily life environment using the current development of large-scale vision-language models. Using its Visual Question Answering (VQA) model, we propose a method to detect semantic changes by applying multiple questions to a reference image and a current image and obtaining answers in the form of sentences. Unlike deep learning-based methods in anomaly detection, this method does not require any training or fine-tuning, is not affected by noise, and is sensitive to semantic state changes in the real world. In our experiments, we demonstrated the effectiveness of this method by applying it to a patrol task in a real-life environment using a mobile robot, Fetch Mobile Manipulator. In the future, it may be possible to add explanatory power to changes in the daily life environment through spoken language.Comment: Accepted to 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023