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Objective To analyze the correlation between Ki-67 expression in tumor tissue and ultrasound features of patients with mass breast cancer.Methods A total of 90 patients(90 masses)with mass breast cancer admitted to Xinxiang Central Hospital from January 2020 to December 2022 were selected and divided into the Ki-67 high expression group(n=53)and Ki-67 low expression group(n=37)according to the expression of Ki-67 in tumor tissue.The qualitative and quantitative characteristics of contrast-enhanced ultrasound(CEUS),shear wave elastography(SWE)and color doppler flow imaging(CDFI)were compared between the two groups,and their correlation with Ki-67 expression in patients with mass breast cancer was analyzed by multivariate logistic regression.Results The proportion of CEUS perfusion defects patients in the Ki-67 high expression group was significantly higher than that in the Ki-67 low expression group(P＜0.05).There was no significant difference in CEUS margin,homogeneity,enhancement level,post-enhancement lesion range and direction between the two groups(P＞0.05).The maximum intensity,rise time,and half time in the Ki-67 high expression group were significantly higher than those in the Ki-67 low expression group(P＜0.05).There was no significant difference in arrival time,time to peak,and mean transit time between the two groups(P＞0.05).The proportion of hard ring signs in SWE of patients in the Ki-67 high expression group was significantly higher than that in the Ki-67 low expression group(P＜0.05),while there was no significant difference in the proportion of SWE black hole signs between the two groups(P＞0.05).The elastic modulus ratio between lesions to surrounding parenchyma of patients in Ki-67 high expression group was higher than that in the Ki-67 low expression group(P＜0.05).There was no significant difference in the maximum,average,and minimum elastic modulus between the two groups(P＞0.05).In the Ki-67 high expression group,the proportion of patient where tumor size under CDFI was 2-5 cm,and the proportion of patients with burrs on the lesion edge were higher than those in the Ki-67 low expression group(P＜0.05).There was no significant difference in the shape,location and calcification of the lesions under CDFI between the two groups(P＞0.05).Perfusion defect,hard ring sign and edge burr were associated with high Ki-67 expression in patients with mass breast cancer(P＜0.05).Conclusion Ultrasonographic features of mass breast cancer can predict Ki-67 expression.Perfusion defect,hard ring sign,and edge burr are related to Ki-67 positive expression.
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With the current accumulation of metagenome data, it is possible to build an integrated platform for processing of rigorously selected metagenomic samples (also referred as "metagenomic communities" here) of interests. Any metagenomic samples could then be searched against this database to find the most similar sample(s). However, on one hand, current databases with a large number of metagenomic samples mostly serve as data repositories but not well annotated database, and only offer few functions for analysis. On the other hand, the few available methods to measure the similarity of metagenomic data could only compare a few pre-defined set of metagenome. It has long been intriguing scientists to effectively calculate similarities between microbial communities in a large repository, to examine how similar these samples are and to find the correlation of the meta-information of these samples. In this work we propose a novel system, Meta-Mesh, which includes a metagenomic database and its companion analysis platform that could systematically and efficiently analyze, compare and search similar metagenomic samples. In the database part, we have collected more than 7 000 high quality and well annotated metagenomic samples from the public domain and in-house facilities. The analysis platform supplies a list of online tools which could accept metagenomic samples, build taxonomical annotations, compare sample in multiple angle, and then search for similar samples against its database by a fast indexing strategy and scoring function. We also used case studies of "database search for identification" and "samples clustering based on similarity matrix" using human-associated habitat samples to demonstrate the performance of Meta-Mesh in metagenomic analysis. Therefore, Meta-Mesh would serve as a database and data analysis system to quickly parse and identify similar