ArticleTranslational cancer research2020
Application of CT radiomics features to predict the EGFR mutation status and therapeutic sensitivity to TKIs of advanced lung adenocarcinoma.
Article in Translational cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 20 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
- Systematic Review, Meta-Analysis and Radiomics Quality Score Assessment of CT Radiomics-Based Models Predicting Tumor EGFR Mutation Status in Patients with Non-Small-Cell Lung Cancer.International journal of molecular sciences · 2023Pooled it
- Systematic review of spectral computed tomography quantitative parameters for predictingQuantitative imaging in medicine and surgery · 2026Review
- Nomogram-based prediction of checkpoint inhibitor pneumonitis in lung cancer patients.BMC cancer · 2026Article
- NSCLC EGFR Mutation Prediction via Random Forest Model: A Clinical-CT-Radiomics Integration Approach.Advances in respiratory medicine · 2025Article
- Deep learning radiopathomics predicts targeted therapy sensitivity in EGFR-mutant lung adenocarcinoma.Journal of translational medicine · 2025Article
- Non-invasive, fast, and high-performance EGFR gene mutation prediction method based on deep transfer learning and model stacking for patients with Non-Small Cell Lung Cancer.European journal of radiology open · 2024Article
- Review
- Multimodal data deep learning method for predicting symptomatic pneumonitis caused by lung cancer radiotherapy combined with immunotherapy.Frontiers in immunology · 2024Article
- Combined CT-Based Radiomics and Clinic-Radiological Characteristics for Preoperative Differentiation of Solitary-Type Invasive Mucinous and Non-Mucinous Lung Adenocarcinoma.International journal of general medicine · 2024Article
- Multiregional radiomics of brain metastasis can predict response to EGFR-TKI in metastatic NSCLC.European radiology · 2023Article
- Radiomics for prediction of response to EGFR-TKI based on metastasis/brain parenchyma (M/BP)-interface.La Radiologia medica · 2022Article
- A predictive model of computed tomography and clinical features of EGFR gene mutation in lung adenocarcinoma.Science progressArticle
- Radiomics based on MRI andNeuro-oncology advancesArticle
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo explore the ability of computed tomography (CT) radiomics features to predict the epidermal growth factor receptor (EGFR) mutation status and the therapeutic response of advanced lung adenocarcinoma to treatment with EGFR tyrosine kinase inhibitors (TKIs).
methodsA retrospective analysis was performed on 253 patients with advanced lung adenocarcinoma who underwent EGFR mutation detection, and those with EGFR-sensitive mutations were treated with TKIs. Using the least absolute shrinkage and selection operator (LASSO) regression model and the 10-fold cross-validation method, the radiomics features that predicted EGFR mutation status and TKI sensitivity were obtained.
resultsThe area under the curve (AUC) values of the unenhanced, arterial and venous phases in the EGFR mutation status training group were 0.6713, 0.8194 and 0.8464, respectively. A total of 5, 18 and 23 radiomics features were extracted from the unenhanced, arterial and venous phases, respectively; these features could distinguish the EGFR mutation status. The AUC values of the unenhanced, arterial and venous phases in the EGFR-TKI-sensitive training group were 0.7268, 0.7793 and 0.9104, respectively. A total of 3, 7 and 22 radiomics features were extracted from the unenhanced, arterial and venous phases, respectively; these features could be used to identify the appropriate population for TKI treatment.
conclusionsRadiomics features extracted from CT scans can be used as biomarkers to predict the EGFR mutation status of lung adenocarcinoma and to further screen the dominant population for TKI therapy, providing a basis for accurate targeted therapy of tumors.
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