ArticleFrontiers in oncology2022
Three-Dimensional Convolutional Neural Network-Based Prediction of Epidermal Growth Factor Receptor Expression Status in Patients With Non-Small Cell Lung Cancer.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- 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
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Role of Three-Dimensional Convolution Neural Networks (3D-CNN) in Image Processing and Recognition in Oncology: A Systematic Review and Meta-Analysis.International journal of hematology-oncology and stem cell research · 2026Review
- CT Radiomics-based machine learning approach for the invasiveness of pulmonary ground-glass nodules prediction.European journal of radiology open · 2025Article
- EGFR Mutation Detection in Whole Slide Images of Non-Small Cell Lung Cancers Using a Two-Stage Deep Transfer Learning Approach.Cancer medicine · 2025Article
- An online explainable ensemble machine learning model for predicting epidermal growth factor receptor mutation status in lung adenocarcinoma.Translational lung cancer research · 2025Article
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Habitat radiomics and deep learning fusion nomogram to predict EGFR mutation status in stage I non-small cell lung cancer: a multicenter study.Scientific reports · 2024Article
- Using Vision Transformer for high robustness and generalization in predicting EGFR mutation status in lung adenocarcinoma.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2024Article
- Effectiveness of CT radiomic features combined with clinical factors in predicting prognosis in patients with limited-stage small cell lung cancer.BMC cancer · 2024Article
- Article
- Development and validation of MRI-based radiomics model to predict recurrence risk in patients with endometrial cancer: a multicenter study.European radiology · 2023Article
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- Radiomics-Based Machine Learning for Predicting the Injury Time of Rib Fractures in Gemstone Spectral Imaging Scans.Bioengineering (Basel, Switzerland) · 2022Article
- Explainable 1-mm Peritumoral CT Radiomics forCancer control : journal of the Moffitt Cancer CenterArticle
Corrections and comments
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Authors and funding
14 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesEGFR testing is a mandatory step before targeted therapy for non-small cell lung cancer patients. Combining some quantifiable features to establish a predictive model of EGFR expression status, break the limitations of tissue biopsy. MATERIALS AND
methodsWe retrospectively analyzed 1074 patients of non-small cell lung cancer with complete reports of
resultsWe successfully established Model
conclusionsEither deep learning or radiomic signature-based models can provide a fairly accurate non-invasive prediction of EGFR expression status. The model combined both features effectively enhanced the performance of radiomic models and provided marginal enhancement to deep learning models. Collectively, fusion models offer a novel and more reliable way of providing the efficacy of currently developed prediction models, and have far-reaching potential for the optimization of noninvasive EGFR mutation status prediction methods.
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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.