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2020 Fiscal Year Final Research Report

Development of hemodiagnosis for early detection of ovarian cancer with AI using glycopeptide peaks obtained from CSGSA (Comprehensive Serum Glycopeptide Spectra Analysis)

Research Project

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Project/Area Number 18K09300
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Review Section Basic Section 56040:Obstetrics and gynecology-related
Research InstitutionTokai University

Principal Investigator

IKEDA Masae  東海大学, 医学部, 講師 (20365993)

Co-Investigator(Kenkyū-buntansha) 信田 政子  東海大学, 医学部, 講師 (10338717)
三上 幹男  東海大学, 医学部, 教授 (30190606)
柴田 健雄  東海大学, 健康学部, 講師 (30366033)
平澤 猛  東海大学, 医学部, 准教授 (70307289)
Project Period (FY) 2018-04-01 – 2021-03-31
Keywords卵巣癌 / 血清バイオマーカー / 糖ペプチド / 質量分析 / 人工知能 / 深層学習
Outline of Final Research Achievements

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 CSGSA method (CSGSA-AI) 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.

Free Research Field

婦人科腫瘍

Academic Significance and Societal Importance of the Research Achievements

卵巣癌は早期発見が難しくかつ予後も極めて悪い癌であり、新たな発想の新規診断技術導入が重要である。腫瘍マーカーは単一分子と認識され研究されてきたが、現状では卵巣癌早期診断は不可能であろう。そこで古い概念を打ち破り、究極のCombination Assayと考えられる網羅的血清糖ペプチドピークと人工知能を用いた卵巣癌早期診断の開発し、現在汎用されている卵巣癌マーカーであるCA125とHE4よりも有意に初期卵巣癌を判別できる診断法を開発した。

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Published: 2022-01-27  

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