Comprehensive Serum Glycopeptide Spectra Analysis Combined with Artificial Intelligence (CSGSA-AI) to Diagnose Early-Stage Ovarian Cancer
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/12/9/2373 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Kazuhiro Tanabe |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T17:01:58Z |
| format | Article |
| id | doaj.art-86a757ca13da4e4bb0e7324dd2649de2 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-10T17:01:58Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-86a757ca13da4e4bb0e7324dd2649de22023-11-20T10:56:17ZengMDPI AGCancers2072-66942020-08-01129237310.3390/cancers12092373Comprehensive Serum Glycopeptide Spectra Analysis Combined with Artificial Intelligence (CSGSA-AI) to Diagnose Early-Stage Ovarian CancerKazuhiro Tanabe0Masae Ikeda1Masaru Hayashi2Koji Matsuo3Miwa Yasaka4Hiroko Machida5Masako Shida6Tomoko Katahira7Tadashi Imanishi8Takeshi Hirasawa9Kenji Sato10Hiroshi Yoshida11Mikio Mikami12Medical Solution Promotion Department, Medical Solution Segment, LSI Medience Corporation, Tokyo 1748555, JapanDepartment of Obstetrics and Gynecology, Tokai University School of Medicine, Kanagawa 2591193, JapanDepartment of Obstetrics and Gynecology, Tokai University School of Medicine, Kanagawa 2591193, JapanDivision of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Southern California, Los Angeles, CA 90033, USADepartment of Obstetrics and Gynecology, Tokai University School of Medicine, Kanagawa 2591193, JapanDepartment of Obstetrics and Gynecology, Tokai University School of Medicine, Kanagawa 2591193, JapanDepartment of Obstetrics and Gynecology, Tokai University School of Medicine, Kanagawa 2591193, JapanMedical Solution Promotion Department, Medical Solution Segment, LSI Medience Corporation, Tokyo 1748555, JapanDepartment of Molecular Life Science, Division of Basic Medical Science and Molecular Medicine, Tokai University School of Medicine, Kanagawa 2591193, JapanDepartment of Obstetrics and Gynecology, Tokai University School of Medicine, Kanagawa 2591193, JapanDepartment of Obstetrics and Gynecology, Tokai University School of Medicine, Kanagawa 2591193, JapanDepartment of Obstetrics and Gynecology, Tokai University School of Medicine, Kanagawa 2591193, JapanDepartment of Obstetrics and Gynecology, Tokai University School of Medicine, Kanagawa 2591193, JapanOvarian 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.https://www.mdpi.com/2072-6694/12/9/2373AIdeep learningovarian cancerscreeningearly stageconvolutional neural network |
| spellingShingle | 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 Comprehensive Serum Glycopeptide Spectra Analysis Combined with Artificial Intelligence (CSGSA-AI) to Diagnose Early-Stage Ovarian Cancer Cancers AI deep learning ovarian cancer screening early stage convolutional neural network |
| title | Comprehensive Serum Glycopeptide Spectra Analysis Combined with Artificial Intelligence (CSGSA-AI) to Diagnose Early-Stage Ovarian Cancer |
| title_full | Comprehensive Serum Glycopeptide Spectra Analysis Combined with Artificial Intelligence (CSGSA-AI) to Diagnose Early-Stage Ovarian Cancer |
| title_fullStr | Comprehensive Serum Glycopeptide Spectra Analysis Combined with Artificial Intelligence (CSGSA-AI) to Diagnose Early-Stage Ovarian Cancer |
| title_full_unstemmed | Comprehensive Serum Glycopeptide Spectra Analysis Combined with Artificial Intelligence (CSGSA-AI) to Diagnose Early-Stage Ovarian Cancer |
| title_short | Comprehensive Serum Glycopeptide Spectra Analysis Combined with Artificial Intelligence (CSGSA-AI) to Diagnose Early-Stage Ovarian Cancer |
| title_sort | comprehensive serum glycopeptide spectra analysis combined with artificial intelligence csgsa ai to diagnose early stage ovarian cancer |
| topic | AI deep learning ovarian cancer screening early stage convolutional neural network |
| url | https://www.mdpi.com/2072-6694/12/9/2373 |
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