Cancers, Vol. 12, Pages 2373: Comprehensive Serum Glycopeptide Spectra Analysis Combined with Artificial Intelligence (CSGSA-AI) to Diagnose Early-Stage Ovarian Cancer
Cancers, Vol. 12, Pages 2373: Comprehensive Serum Glycopeptide Spectra Analysis Combined with Artificial Intelligence (CSGSA-AI) to Diagnose Early-Stage Ovarian Cancer
Cancers doi: 10.3390/cancers12092373
Authors:
Kazuhiro Tanabe
Masae Ikeda
Masaru Hayashi
Koji Matsuo
Miwa Yasaka
Hiroko Machida
Masako Shida
Tomoko Katahira
Tadashi Imanishi
Takeshi Hirasawa
Kenji Sato
Hiroshi Yoshida
Mikio Mikami
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.
Source: Cancers - Category: Cancer & Oncology Authors: Kazuhiro Tanabe Masae Ikeda Masaru Hayashi Koji Matsuo Miwa Yasaka Hiroko Machida Masako Shida Tomoko Katahira Tadashi Imanishi Takeshi Hirasawa Kenji Sato Hiroshi Yoshida Mikio Mikami Tags: Article Source Type: research
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