Project/Area Number |
18K09300
|
Research Category |
Grant-in-Aid for Scientific Research (C)
|
Allocation Type | Multi-year Fund |
Section | 一般 |
Review Section |
Basic Section 56040:Obstetrics and gynecology-related
|
Research Institution | Tokai 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
|
Project Status |
Completed (Fiscal Year 2020)
|
Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
|
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 / リキッドバイオプシー / 腫瘍マーカー / 早期診断 / がん検診 |
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.
|
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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