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人工神经网络在慢性淋巴细胞白血病诊断中的应用。

Application of an Artificial Neural Network in the Diagnosis of Chronic Lymphocytic Leukemia.

作者信息

Shaabanpour Aghamaleki Fateme, Mollashahi Behrouz, Nosrati Mokhtar, Moradi Afshin, Sheikhpour Mojgan, Movafagh Abolfazl

机构信息

Genetics, Shahid Beheshti University of Medical Sciences, Tehran, IRN.

Genetics, University of Isfahan, Isfahan, IRN.

出版信息

Cureus. 2019 Feb 4;11(2):e4004. doi: 10.7759/cureus.4004.

Abstract

Introduction Chronic lymphocytic leukemia (CLL) is one of the most common types of leukemia, and the early diagnosis of patients coincides with their proper treatment and survival. If patients are diagnosed late or proper treatment is not applied, it may lead to harmful results. Several methods could be used for the diagnosis of leukemia; some of these include complete blood count (CBC), immunophenotyping, lymph node biopsy, chest X-ray, computerized tomography (CT) scan, and ultrasound. Most of these methods are time-consuming and an application of more than one method will result as intended. This acknowledgment stresses the necessity of rapid and proper diagnosis for leukemia based on clinical and medical findings, inasmuch as it was decided to apply the artificial neural network (ANN) in order to identify a molecular biomarker for rapid leukemia diagnosis from blood samples and evaluate its potential for the detection of cancer. Materials & methods The independent sample t-test was applied with the Statistical Package for the Social Sciences (SPSS; IBM Corp, Armonk, NY, US) software on the microarray gene expression data of Gene Expression Omnibus (GEO) datasets (GSE22529); 12 genes that had shown the highest differences (among parameters whose p-value was less than 0.01) were selected for further ANN analysis. The selected genes of 53 patients were applied to the training network algorithm, with a learning rate of 0.1. Results The results showed a high accuracy of the relationship between the output of the trained network and the test data. The area under the receiver operating characteristic (ROC) curve was 0.991, which provides proof of the precision and the relationship with identifying Gelsolin as a potential biomarker for this research. Conclusions With these results, it was concluded that the training process of the ANN could be applied to rapid CLL diagnosis and finding a potential biomarker. Besides, it is suggested that this method could be performed to diagnose other forms of cancer in order to get a rapid and reliable outcome.

摘要

引言 慢性淋巴细胞白血病(CLL)是最常见的白血病类型之一,患者的早期诊断与其恰当治疗及生存情况密切相关。若患者诊断过晚或未接受恰当治疗,可能会导致不良后果。有多种方法可用于白血病的诊断;其中包括全血细胞计数(CBC)、免疫表型分析、淋巴结活检、胸部X线、计算机断层扫描(CT)以及超声检查。这些方法大多耗时较长,且采用多种方法联合应用才能达到预期效果。鉴于此,基于临床和医学发现,快速且准确地诊断白血病显得尤为必要,因此决定应用人工神经网络(ANN)从血液样本中识别用于快速白血病诊断的分子生物标志物,并评估其在癌症检测方面的潜力。

材料与方法 使用社会科学统计软件包(SPSS;美国纽约州阿蒙克市IBM公司)对基因表达综合数据库(GEO)数据集(GSE22529)的微阵列基因表达数据进行独立样本t检验;选择了12个差异最大(p值小于0.01的参数中)的基因用于进一步的人工神经网络分析。将53例患者的所选基因应用于训练网络算法,学习率为0.1。

结果 结果显示训练后的网络输出与测试数据之间具有高度准确性。受试者工作特征(ROC)曲线下面积为0.991,这证明了该研究在识别凝溶胶蛋白作为潜在生物标志物方面的准确性及相关性。

结论 基于这些结果,得出结论:人工神经网络的训练过程可应用于快速慢性淋巴细胞白血病诊断及寻找潜在生物标志物。此外,建议可采用该方法诊断其他形式的癌症,以获得快速且可靠的结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2de8/6450593/15ccccfdb696/cureus-0011-00000004004-i01.jpg

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