Clinical efficacy of radical nephrectomy versus nephron-sparing surgery on localized renal cell carcinoma

BACKGROUND: The aim of the present study was to compare the clinical efficacy of radical nephrectomy (RN) with nephron-sparing surgery (NSS) in treating patients with localized renal cell carcinoma (RCC). METHODS: The literature search was performed in PubMed, MEDLINE Springer, Elsevier Science Direct, Cochrane Library, and Google Scholar up to December 2012. The software Review Manager 5.1 and the STATA software package v.11.0 were used for analyses. The odds ratios (ORs) and its 95% confidence interval (95% CI) were calculated for comparison. Subgroup analyses were performed based on the tumor size of RCC. RESULTS: In total, 10 studies with 10,174 RCC patients (7,050 treated with RN and 3,124 treated with NSS) were selected. The pooled estimate (OR = 1.58, 95% CI = 1.15–2.15, P = 0.004) showed a significantly lower rate of cancer-specific deaths in the patients treated with NSS compared to RN. However, no statistically significant differences were found in the rate of tumor recurrence (OR = 0.84, 95% CI = 0.67–1.06, P = 0.14) and complications (OR = 0.91, 95% CI = 0.51–1.63, P = 0.74) between the patients treated with NSS and RN. In addition, all the subgroup analyses presented consistent results with the overall analyses. CONCLUSIONS: NSS had no significantly different from RN in tumor recurrence and complications for localized RCC. However, the significantly lower rate of cancer-specific deaths supported the use of NSS not only for RCC with tumor size >4.0 cm but also for tumor sizes ≤4.0 cm compared with RN

2nd Place Solution to Google Landmark Retrieval 2020

This paper presents the 2nd place solution to the Google Landmark Retrieval Competition 2020. We propose a training method of global feature model for landmark retrieval without post-processing, such as local feature and spatial verification. There are two parts in our retrieval method in this competition. This training scheme mainly includes training by increasing margin value of arcmargin loss and increasing image resolution step by step. Models are trained by PaddlePaddle framework and Pytorch framework, and then converted to tensorflow 2.2. Using this method, we got a public score of 0.40176 and a private score of 0.36278 and achieved 2nd place in the Google Landmark Retrieval Competition 2020