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乳腺癌的预测与监测方法：以科英布拉大学医院中心的数据为例

Prediction and Monitoring Method for Breast Cancer: A Case Study for Data from the University Hospital Centre of Coimbra.

作者信息

Yue Jin, Zhao Na, Liu Liu

机构信息

School of Mathematics and VC & VR Key Laboratory of Sichuan Province, Sichuan Normal University, Chengdu, People's Republic of China.

School of Mathematics, Sichuan University of Arts and Science, Dazhou, People's Republic of China.

出版信息

Cancer Manag Res. 2020 Mar 13;12:1887-1893. doi: 10.2147/CMAR.S242027. eCollection 2020.

DOI:10.2147/CMAR.S242027
PMID:32214846
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7080965/
Abstract

Breast cancer is the second most common cancer in women after skin cancer. Breast cancer can occur in both men and women, but it is far more common in women. Real-time monitoring of breast cancer indicators is becoming increasingly important. It can help create advances in the diagnosis and treatment of breast cancer. In this paper, we provide a nonparametric statistical method to predict and detect breast cancer occur. The exponentially weighted moving average (EWMA) control scheme is based on rank methods so that it is completely nonparametric. It is efficient in detecting the shifts for multivariate processes. A real example data from the University Hospital Centre of Coimbra is given to illustrate this method.

摘要

乳腺癌是女性中仅次于皮肤癌的第二大常见癌症。乳腺癌可发生于男性和女性，但在女性中更为常见。对乳腺癌指标进行实时监测变得越来越重要。它有助于在乳腺癌的诊断和治疗方面取得进展。在本文中，我们提供了一种非参数统计方法来预测和检测乳腺癌的发生。指数加权移动平均（EWMA）控制方案基于秩方法，因此它是完全非参数的。它在检测多变量过程的变化方面很有效。给出了来自科英布拉大学医院中心的一个实际示例数据来说明这种方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fc6/7080965/5ef39c9e9f4a/CMAR-12-1887-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fc6/7080965/99659c87d71e/CMAR-12-1887-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fc6/7080965/150a70810763/CMAR-12-1887-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fc6/7080965/db76e924038d/CMAR-12-1887-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fc6/7080965/5ef39c9e9f4a/CMAR-12-1887-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fc6/7080965/99659c87d71e/CMAR-12-1887-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fc6/7080965/150a70810763/CMAR-12-1887-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fc6/7080965/db76e924038d/CMAR-12-1887-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fc6/7080965/5ef39c9e9f4a/CMAR-12-1887-g0004.jpg

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本文引用的文献

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Differential Effects of Insulin and IGF1 Receptors on ERK and AKT Subcellular Distribution in Breast Cancer Cells.胰岛素和 IGF1 受体对乳腺癌细胞中 ERK 和 AKT 亚细胞分布的差异影响。
Cells. 2019 Nov 23;8(12):1499. doi: 10.3390/cells8121499.
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Multivariate nonparametric chart for influenza epidemic monitoring.用于流感疫情监测的多元非参数图表。
Sci Rep. 2019 Nov 25;9(1):17472. doi: 10.1038/s41598-019-53908-6.
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A Novel 3D Scaffold for Cell Growth to Asses Electroporation Efficacy.用于评估电穿孔效果的新型 3D 细胞生长支架。
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BMC Cancer. 2018 Jan 4;18(1):29. doi: 10.1186/s12885-017-3877-1.
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Stat Med. 2010 Feb 20;29(4):444-54. doi: 10.1002/sim.3788.
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Monitoring change in spatial patterns of disease: comparing univariate and multivariate cumulative sum approaches.监测疾病空间模式的变化：比较单变量和多变量累积和方法。
Stat Med. 2004 Jul 30;23(14):2195-214. doi: 10.1002/sim.1806.
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A risk-adjusted Sets method for monitoring adverse medical outcomes.
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