ArticleFrontiers in oncology2024
Machine learning models based on quantitative dynamic contrast-enhanced MRI parameters assess the expression levels of CD3
Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
3 citing papers in PubMed, 4 citations in OpenAlex.
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Application of deep learning convolutional neural networks to identify gastric squamous cell carcinoma in mice.Frontiers in medicine · 2025Article
- Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To explore the effectiveness of machine learning classifiers based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the expression levels of CD3 Methods: This study investigated 103 patients with confirmed AGC through DCE-MRI and immunohistochemical staining. Immunohistochemical staining was used to evaluate CD3 Results: In terms of CD3 Conclusion: Machine learning classifiers based on DCE-MRI have the potential to accurately predict CD3
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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.