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- Title
Comprehensive Serum Glycopeptide Spectra Analysis Combined with Artificial Intelligence (CSGSA-AI) to Diagnose Early-Stage Ovarian Cancer.
- Authors
Tanabe, Kazuhiro; Ikeda, Masae; Hayashi, Masaru; Matsuo, Koji; Yasaka, Miwa; Machida, Hiroko; Shida, Masako; Katahira, Tomoko; Imanishi, Tadashi; Hirasawa, Takeshi; Sato, Kenji; Yoshida, Hiroshi; Mikami, Mikio
- Abstract
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.
- Subjects
ARTIFICIAL intelligence; FACTOR analysis; GLYCOPROTEINS; MASS spectrometry; ARTIFICIAL neural networks; OVARIAN tumors; POLYSACCHARIDES; STATISTICS; DATA analysis; DESCRIPTIVE statistics; EARLY detection of cancer; DEEP learning
- Publication
Cancers, 2020, Vol 12, Issue 9, p2373
- ISSN
2072-6694
- Publication type
Article
- DOI
10.3390/cancers12092373