PURPOSE: To evaluate the anti-inflammatory and antioxidative effects of extracorporeal shockwave therapy (ESWT) on prostatitis and explore the mechanism of alleviating pain. MATERIALS AND METHODS: For in vitro testing, RWPE-1 cells were randomly divided into 5 groups: (1) RWPE-1 group (normal control), (2) LPS group (lipopolysaccharide inducing inflammation), (3) 0.1ESWT group (treated by 0.1 mJ/mm² energy level), (4) 0.2ESWT group (treated by 0.2 mJ/mm² energy level), and (5) 0.3ESWT group (treated by 0.3 mJ/mm² energy level). After ESWT was administered, cells and supernatant were collected for ELISA and western blot. For in vivo testing, Sprague-Dawley male rats were randomly divided into 3 groups: (1) normal group, (2) prostatitis group, and (3) ESWT group (n=12 for each). Prostatitis was induced by 17 beta-estradiol and dihydrotestosterone (DHT) administration. Four weeks after ESWT, the pain index was assessed for all groups and prostate tissues were collected for immunohistochemistry, immunofluorescence, apoptosis analysis and, western blot. RESULTS: Our in vitro studies showed that the optimal energy flux density of ESWT was 0.2 mJ/mm². In vivo, ESWT ameliorated discomfort in rats with prostatitis and inflammation symptoms were improved. Compared to normal rats, overexpressed NLRP3 inflammasomes triggered apoptosis in rats with prostatitis and this was improved by ESWT. TLR4-NFκB pathway was overactive after experimental prostatitis, compared to normal and ESWT groups, and prostatitis induced alterations in BAX/BAK pathway were inhibited by ESWT. CONCLUSIONS: ESWT improved CP/CPPS by reducing NLRP3 inflammasome and ameliorated apoptosis via inhibiting BAX/BAK pathway in a rat model. TLR4 may play a key role in bonding NLRP3 inflammasome and BAX/BAK pathways. ESWT might be a promising approach for the treatment of CP/CPPS.

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.