ArticleFrontiers in oncology2020
Performance of
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 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
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Prediction of oncogene mutation status in non-small cell lung cancer: a systematic review and meta-analysis with a special focus on artificial intelligence-based methods.European radiology · 2026Pooled it
- Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.Frontiers in oncology · 2025Pooled it
- Predictive value ofFrontiers in oncology · 2024Pooled it
- Noninvasive Method for Predicting the Expression of Ki67 and Prognosis in Non-Small-Cell Lung Cancer Patients: Radiomics.Journal of healthcare engineering · 2022Trial
- The predictive value ofFrontiers in medicine · 2026Article
- Development and validation of a nomogram using interpretable machine learning to integrate CT radiomics and PET metabolic parameters for predicting benign-malignant differentiation of pulmonary space-occupying lesions.Frontiers in radiology · 2026Article
- A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer.European radiology experimental · 2024Article
- FDG PET texture indices as imaging biomarkers for epidermal growth factor receptor mutation status in lung adenocarcinoma.Scientific reports · 2023Article
- The predictive value of [EJNMMI research · 2023Article
- New research progress on 18F-FDG PET/CT radiomics for EGFR mutation prediction in lung adenocarcinoma: a review.Frontiers in oncology · 2023Review
- Prediction of EGFR mutation status in lung adenocarcinoma based onAmerican journal of nuclear medicine and molecular imaging · 2023Article
- A nomogram based onQuantitative imaging in medicine and surgery · 2022Article
- Combination ofKorean journal of radiology · 2022Article
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers.Diagnostics (Basel, Switzerland) · 2022Review
- Machine learning in the differentiation of follicular lymphoma from diffuse large B-cell lymphoma with radiomic [European journal of nuclear medicine and molecular imaging · 2022Article
- PET/CT Radiomic Features: A Potential Biomarker for EGFR Mutation Status and Survival Outcome Prediction in NSCLC Patients Treated With TKIs.Frontiers in oncology · 2022Article
- Recent and current advances in PET/CT imaging in the field of predicting epidermal growth factor receptor mutations in non-small cell lung cancer.Frontiers in oncology · 2022Review
- PET/CT Based EGFR Mutation Status Classification of NSCLC Using Deep Learning Features and Radiomics Features.Frontiers in pharmacology · 2022Article
- A Review of the Correlation Between Epidermal Growth Factor Receptor Mutation Status andFrontiers in oncology · 2022Review
- Artificial Intelligence Applications to Improve the Treatment of Locally Advanced Non-Small Cell Lung Cancers.Cancers · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo assess the performance of pretreatment PATIENTS AND
methodsWe enrolled total 173 patients with histologically proven NSCLC who underwent preoperative
resultsFour CT and two PET radiomics features were finally selected to build the PET/CT radiomics model. Compared with area under the ROC curve (AUC) equal to 0.664, 0.683 and 0.662 for clinical features, maximum standardized uptake values (SUV
conclusionsEGFR mutations status in patients with NSCLC could be well predicted by the combined model based on
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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.