Comprehensive Serum Glycopeptide Spectra Analysis Combined with Artificial Intelligence (CSGSA-AI) to Diagnose Early-Stage Ovarian Cancer.
Comprehensive Serum Glycopeptide Spectra Analysis Combined with Artificial Intelligence (CSGSA-AI) to Diagnose Early-Stage Ovarian Cancer.
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DOI：
10.3390/cancers12092373
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发表时间：
2020-08-21
期刊：
影响因子：
5.2
通讯作者：
Mikami M
中科院分区：
文献类型：
--
作者：
Tanabe K;Ikeda M;Hayashi M;Matsuo K;Yasaka M;Machida H;Shida M;Katahira T;Imanishi T;Hirasawa T;Sato K;Yoshida H;Mikami M
卵巢癌是妇科癌症死亡的主要原因，迫切需要一种检测早期上皮性卵巢癌（EOC）的方法。我们旨在建立一种基于人工智能（AI）的综合血清糖肽谱分析（CSGSA-AI）方法，结合卷积神经网络（CNN）检测EOC患者血清样本中的异常聚糖。我们将血清糖肽表达模式转换为二维条形码，让CNN学习和区分EOC和非EOC。CNN使用60%的样本进行训练，使用40%的样本进行验证。我们观察到，基于主成分分析的糖肽比对生成二维条形码显著提高了该方法的诊断准确性（88%）。当CNN接受基于血清CA125和HE4水平着色的二维条形码训练时，诊断准确率达到95%。我们相信这种简单、低成本的方法将提高EOC的检出率。
Ovarian cancer is a leading cause of deaths among gynecological cancers, and a method to detect early-stage epithelial ovarian cancer (EOC) is urgently needed. We aimed to develop an artificial intelligence (AI)-based comprehensive serum glycopeptide spectra analysis (CSGSA-AI) method in combination with convolutional neural network (CNN) to detect aberrant glycans in serum samples of patients with EOC. We converted serum glycopeptide expression patterns into two-dimensional (2D) barcodes to let CNN learn and distinguish between EOC and non-EOC. CNN was trained using 60% samples and validated using 40% samples. We observed that principal component analysis-based alignment of glycopeptides to generate 2D barcodes significantly increased the diagnostic accuracy (88%) of the method. When CNN was trained with 2D barcodes colored on the basis of serum levels of CA125 and HE4, a diagnostic accuracy of 95% was achieved. We believe that this simple and low-cost method will increase the detection of EOC.
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影响因子：
3.7
作者：
Kim, Taewook;Kim, Ha Young
通讯作者：
Kim, Ha Young
影响因子：
16.6
作者：
Marouf, Mohamed;Machart, Pierre;Bonn, Stefan
通讯作者：
Bonn, Stefan
DOI：
10.1093/jnci/djx030
发表时间：
2017-09-01
期刊：
Journal of the National Cancer Institute
影响因子：
--
作者：
Jemal A;Ward EM;Johnson CJ;Cronin KA;Ma J;Ryerson B;Mariotto A;Lake AJ;Wilson R;Sherman RL;Anderson RN;Henley SJ;Kohler BA;Penberthy L;Feuer EJ;Weir HK
通讯作者：
Weir HK
影响因子：
4.7
作者：
Mikami M;Tanabe K;Matsuo K;Miyazaki Y;Miyazawa M;Hayashi M;Asai S;Ikeda M;Shida M;Hirasawa T;Kojima N;Sho R;Iijima S
通讯作者：
Iijima S
影响因子：
2.6
作者：
Matsuo K;Tanabe K;Ikeda M;Shibata T;Kajiwara H;Miyazawa M;Miyazawa M;Hayashi M;Shida M;Hirasawa T;Roman LD;Mikami M
通讯作者：
Mikami M