Serum levels of chemical elements in esophageal squamous cell carcinoma in Anyang, China: a case-control study based on machine learning methods.
Serum levels of chemical elements in esophageal squamous cell carcinoma in Anyang, China: a case-control study based on machine learning methods.
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中国安阳市食管鳞癌血清化学元素水平：基于机器学习方法的病例对照研究
DOI：
10.1136/bmjopen-2016-015443
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发表时间：
2017-09-24
期刊：
影响因子：
2.9
通讯作者：
Wang J
中科院分区：
文献类型：
--
作者：
Lin T;Liu T;Lin Y;Zhang C;Yan L;Chen Z;He Z;Wang J
目的食管鳞状细胞癌（esophageal squamous cell carcinoma，ESCC）是食管癌的主要类型，具有侵袭性强、生存率低的特点。我国食管癌高发区食管癌的危险因素尚不清楚。我们使用机器学习方法来研究某些化学元素的血清水平变化与ESCC之间是否存在关联。中国河南省安阳市初级卫生保健单位。研究对象100例食管鳞癌患者和100例年龄、性别、地区相匹配的健康对照者。主要和次要结局指标主要结局是分类准确性。次要结局是t检验或秩和检验的p值。方法采用传统的t检验、秩和检验等统计方法和流行的机器学习方法。结果随机森林在原始特征向量上的分类准确率最高，为98.38%，支持向量机在嵌入空间上的分类准确率最高，为96.56%。所有六个分类器都可以实现基于单个最重要的元素Sr的90%以上的准确率。具有显著差异的另外两个元素是S和P，提供约80%的准确率。食管鳞癌患者与对照组有一半以上的化学元素有显著性差异。结论ESCC患者与对照组之间存在明显差异，在ESCC的诊断、预后、药物和营养方面具有潜在的应用价值。然而，由于回顾性设计的性质、有限的样本量和缺乏几个潜在的混杂因素（包括肥胖、营养状况、水果和蔬菜消费以及潜在的区域致癌物接触），应谨慎解释结果。
Objectives Esophageal squamous cell carcinoma (ESCC) is the predominant form of esophageal carcinoma with extremely aggressive nature and low survival rate. The risk factors for ESCC in the high-incidence areas of China remain unclear. We used machine learning methods to investigate whether there was an association between the alterations of serum levels of certain chemical elements and ESCC. Settings Primary healthcare unit in Anyang city, Henan Province of China. Participants 100 patients with ESCC and 100 healthy controls matched for age, sex and region were included. Primary and secondary outcome measures Primary outcome was the classification accuracy. Secondary outcome was the p Value of the t-test or rank-sum test. Methods Both traditional statistical methods of t-test and rank-sum test and fashionable machine learning approaches were employed. Results Random Forest achieves the best accuracy of 98.38% on the original feature vectors (without dimensionality reduction), and support vector machine outperforms other classifiers by yielding accuracy of 96.56% on embedding spaces (with dimensionality reduction). All six classifiers can achieve accuracies more than 90% based on the single most important element Sr. The other two elements with distinctive difference are S and P, providing accuracies around 80%. More than half of chemical elements were found to be significantly different between patients with ESCC and the controls. Conclusions These results suggest clear differences between patients with ESCC and controls, implying some potential promising applications in diagnosis, prognosis, pharmacy and nutrition of ESCC. However, the results should be interpreted with caution due to the retrospective design nature, limited sample size and the lack of several potential confounding factors (including obesity, nutritional status, and fruit and vegetable consumption and potential regional carcinogen contacts).
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影响因子：
64.8
作者：
RUMELHART, DE;HINTON, GE;WILLIAMS, RJ
通讯作者：
WILLIAMS, RJ
影响因子：
3.7
作者：
Liu T;Lu QB;Yan L;Guo J;Feng F;Qiu J;Wang J
通讯作者：
Wang J
安阳食管癌队列研究：研究设计、实地调查的实施以及计算机辅助调查系统的使用
影响因子：
3.7
作者：
Liu F;Guo F;Zhou Y;He Z;Tian X;Guo C;Ning T;Pan Y;Cai H;Ke Y
通讯作者：
Ke Y
影响因子：
254.7
作者：
Parkin, DM;Bray, F;Pisani, P
通讯作者：
Pisani, P
DOI：
10.1093/bioinformatics/btp191
发表时间：
2009-06-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Abeel T;Van de Peer Y;Saeys Y
通讯作者：
Saeys Y