ArticleCancers2020
Comprehensive Serum Glycopeptide Spectra Analysis Combined with Artificial Intelligence (CSGSA-AI) to Diagnose Early-Stage Ovarian Cancer.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Artificial intelligence in ovarian pathophysiology and management: a systematic review and meta-analysis.Journal of ovarian research · 2026Pooled it
- Enhancing the positive predictive value of early-stage ovarian cancer detection using a two-step machine learning framework.Journal of translational medicine · 2026Article
- Article
- Prospects and challenges of deep learning in gynecologic malignancies.Frontiers in oncology · 2025Review
- Comprehensive serum glycopeptide spectrum analysis with machine learning for non-invasive early detection of gastrointestinal cancers.Computational and structural biotechnology journal · 2025Article
- Comprehensive serum glycopeptide spectra analysis to identify early-stage epithelial ovarian cancer.Scientific reports · 2024Article
- Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities.Polish journal of radiology · 2024Review
- Breast Cancer Detection and Prevention Using Machine Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Emerging trends in point-of-care biosensing strategies for molecular architectures and antibodies of SARS-CoV-2.Biosensors & bioelectronics: X · 2023Review
- Article
- DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics.Scientific reports · 2023Article
- A Hybrid Deep Learning Approach for ECG-Based Arrhythmia Classification.Bioengineering (Basel, Switzerland) · 2022Article
- Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery.Cancer biomarkers : section A of Disease markers · 2022Review
- DeepFeature: feature selection in nonimage data using convolutional neural network.Briefings in bioinformatics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 2 institutions in 2 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.