ArticleEuropean radiology2021
A clinically practical radiomics-clinical combined model based on PET/CT data and nomogram predicts EGFR mutation in lung adenocarcinoma.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 4 of them syntheses that pooled it.
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33 citing papers in PubMed, 4 syntheses or guidelines pooled it, 70 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
- 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
- Predictive modeling of peripheral blood EGFR positivity in patients with advanced primary non-small cell lung cancer.Translational lung cancer research · 2026Article
- An interpretable machine learning model integrating clinical and CT radiomic features for the detection of interstitial lung disease in idiopathic inflammatory myopathies.Arthritis research & therapy · 2026Article
- The Predictive Value ofCurrent medical imaging · 2026Article
- An Integrated Clinical-Radiomics-Deep Learning Model Based onCancer medicine · 2025Article
- Survival Nomogram for Lung Adenocarcinoma Patients With Bone Metastasis Based on the SEER Database and an External Validation Cohort.Cancer reports (Hoboken, N.J.) · 2025Article
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- Prediction of EGFR mutations in non-small cell lung cancer: a nomogram based onFrontiers in oncology · 2025Article
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- Subregion-specificAmerican journal of nuclear medicine and molecular imaging · 2024Article
- Multiregional radiomics of brain metastasis can predict response to EGFR-TKI in metastatic NSCLC.European radiology · 2023Article
- Predictive value ofEuropean journal of nuclear medicine and molecular imaging · 2023Article
- The predictive value of [EJNMMI research · 2023Article
- Development and Validation of a New Staging System for Esophageal Squamous Cell Carcinoma Patients Based on Combined Pathological TNM, Radiomics, and Proteomics.Annals of surgical oncology · 2023Article
- Predicting the Efficacy of SBRT for Lung Cancer withLife (Basel, Switzerland) · 2023Article
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Authors and funding
15 authors at 4 institutions in 1 country.
Funding
Abstract
objectivesThis study aims to develop a clinically practical model to predict EGFR mutation in lung adenocarcinoma patients according to radiomics signatures based on PET/CT and clinical risk factors.
methodsThis retrospective study included 583 lung adenocarcinoma patients, including 295 (50.60%) patients with EGFR mutation and 288 (49.40%) patients without EGFR mutation. The clinical risk factors associated with lung adenocarcinoma were collected at the same time. We developed PET/CT, CT, and PET radiomics models for the prediction of EGFR mutation using multivariate logistic regression analysis, respectively. We also constructed a combined PET/CT radiomics-clinical model by nomogram analysis. The diagnostic performance and clinical net benefit of this risk-scoring model were examined via receiver operating characteristic (ROC) curve analysis while the clinical usefulness of this model was evaluated by decision curve analysis (DCA).
resultsThe ROC analysis showed predictive performance for the PET/CT radiomics model (AUC = 0.76), better than the PET model (AUC = 0.71, Delong test: Z = 3.03, p value = 0.002) and the CT model (AUC = 0.74, Delong test: Z = 1.66, p value = 0.098). Also, the PET/CT radiomics-clinical combined model has a better performance (AUC = 0.84) to predict EGFR mutation than the PET/CT radiomics model (AUC = 0.76, Delong test: D = 2.70, df = 790.81, p value < 0.001) or the clinical model (AUC = 0.81, Delong test: Z = 3.46, p value < 0.001).
conclusionsWe demonstrated that the combined PET/CT radiomics-clinical model has an advantage to predict EGFR mutation in lung adenocarcinoma. KEY POINTS: • Radiomics from lung tumor increase the efficiency of the prediction for EGFR mutation in clinical lung adenocarcinoma on PET/CT. • A radiomic nomogram was developed to predict EGFR mutation. • Combining PET/CT radiomics-clinical model has an advantage to predict EGFR mutation.
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