ArticleFrontiers in oncology2021
Prediction of EGFR Mutation Status Based on
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 26 papers, 2 of them syntheses that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- Diagnostic performance of machine learning-based radiomics models for predicting epidermal growth factor receptor mutation status in lung adenocarcinoma in Chinese patients: A systematic review and meta-analysis.The Journal of international medical research · 2026Pooled it
- Predictive value ofFrontiers in oncology · 2024Pooled it
- Preoperative PET/CT phenotype of gastric-type endocervical adenocarcinoma: integrated morphological, metabolic, serological, and explainable machine-learning analysis.European journal of nuclear medicine and molecular imaging · 2026Article
- Application of deep learning in prognostic prediction of non-small cell lung cancer.Frontiers in cell and developmental biology · 2026Review
- Radiology-based artificial intelligence for predicting targeted therapy response in pan-cancer: a comprehensive review.Journal of translational medicine · 2025Review
- An Integrated Clinical-Radiomics-Deep Learning Model Based onCancer medicine · 2025Article
- CT-based radiomics and deep learning to predict EGFR mutation status in lung adenocarcinoma.Frontiers in oncology · 2025Article
- An interpretableFrontiers in oncology · 2025Article
- Deep learning on brain metastasis for predicting EGFR genotype and EGFR-TKI therapy response in metastatic NSCLC: a multicenter study.Frontiers in bioengineering and biotechnology · 2025Article
- Enhanced prediction of gene mutation and risk stratification in non-small-cell lung cancer through dual-pathway fusion of radiomics and pathomics.Frontiers in oncology · 2025Article
- Prediction of epidermal growth factor receptor (Quantitative imaging in medicine and surgery · 2024Article
- Automated PD-L1 status prediction in lung cancer with multi-modal PET/CT fusion.Scientific reports · 2024Article
- Brain metastasis magnetic resonance imaging-based deep learning for predicting epidermal growth factor receptor (Quantitative imaging in medicine and surgery · 2024Article
- Transfer learning-based PET/CT three-dimensional convolutional neural network fusion of image and clinical information for prediction of EGFR mutation in lung adenocarcinoma.BMC medical imaging · 2024Article
- Deep learning-radiomics integrated noninvasive detection of epidermal growth factor receptor mutations in non-small cell lung cancer patients.Scientific reports · 2024Article
- Adrenal indeterminate nodules: CT-based radiomics analysis of different machine learning models for predicting adrenal metastases in lung cancer patients.Frontiers in oncology · 2024Article
- Predicting epidermal growth factor receptor mutation status of lung adenocarcinoma based on PET/CT images using deep learning.Frontiers in oncology · 2024Article
- Real-time and accurate estimation of surgical hemoglobin loss using deep learning-based medical sponges image analysis.Scientific reports · 2023Article
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- New research progress on 18F-FDG PET/CT radiomics for EGFR mutation prediction in lung adenocarcinoma: a review.Frontiers in oncology · 2023Review
Corrections and comments
- Erratum issued
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe purpose of this study was to develop a deep learning-based system to automatically predict epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma in
methodsThree hundred and one lung adenocarcinoma patients with EGFR mutation status were enrolled in this study. Two deep learning models (SE
resultsThe AUCs of the SE
conclusionThe stacking model based on
Indexed as
Identifiers
What OpenQuestion holds
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.