用k-均值算法聚类检测肿瘤生长动态差异可能性的研究

N. Marnautov, A. Elfimov, L. Komissarova
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摘要

本文探讨了基于肿瘤发展过程动力学的聚类小鼠的可能性。这项研究是在C57Bl/6系小鼠身上进行的,这些小鼠患有交织的刘易斯癌。对照组(1组)不给药。实验组采用红霉素(2号组)/磁脂质体红霉素(3 - 4号组)化疗,4号组在给药后在肿瘤部位外加磁场照射1小时。分别于肿瘤移植后第10、14、18天给药。使用Mann-Whitney u检验确定组间差异的可靠性。使用k-means算法(kMeans)对获得的数据进行聚类。结果发现,聚类能够很好地区分未接受化疗的一组小鼠。我们还发现,当分成三组时,第3组和第4组的大多数小鼠被分配到同一组,尽管在肿瘤移植后第21天,两组之间的差异有统计学意义(p <0.05)。
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A STUDY OF THE POSSIBILITY OF DETECTING DIFFERENCES IN DYNAMICS OF TUMOR GROWTH BY CLUSTERING USING k-MEANS ALGORITHM
The paper investigates the possibility of clustering mice based on the dynamics of the development of the tumor process. The study was carried out on mice of the C57Bl/6 line, with an intertwined Lewis carcinoma. The control group (group No. 1) was not administered drugs. The experimental groups were treated with chemotherapy using rubomycin (group No. 2) / magnetoliposomal rubomycin (groups No. 3 - No. 4). Group No. 4 was additionally exposed to an external magnetic field on the tumor area for 1 hour after administration of the drug. The drugs were administered on the 10th, 14th, 18th day after the tumor was transplanted. The reliability of the differences between the groups was determined using the Mann-Whitney U-test. Clustering of the obtained data was carried out using the k-means algorithm (kMeans). It was found that clustering confidently distinguishes a cluster of mice that have not received chemotherapy. It was also found that when clustering into three clusters, most of the mice from groups No. 3 and No. 4 were assigned to the same cluster, despite the fact that statistically significant differences were observed between these groups on the 21st day after tumor transplantation (p <0.05).
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