ArticleHeliyon2024
Dual-energy CT-based radiomics in predicting EGFR mutation status non-invasively in lung adenocarcinoma.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 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 of spectral computed tomography quantitative parameters for predictingQuantitative imaging in medicine and surgery · 2026Review
- Quantitative PCCT spectral parameters for noninvasive prediction of EGFR status and its subtypes in lung adenocarcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Clinical Utility of Dual-Energy CT for Detection, Characterization, and Staging of Lung Tumors: A Rapid Review.Diagnostics (Basel, Switzerland) · 2026Review
- Review
- The predictive value ofFrontiers in medicine · 2026Article
- Intranodular and perinodular radiomics features based on non-contrast CT to distinguish pulmonary cryptococcosis from lung adenocarcinoma: a two-center study.Frontiers in oncology · 2026Article
- Review of the application of dual-energy CT combined with radiomics in the diagnosis and analysis of lung cancer.Journal of applied clinical medical physics · 2025Review
- Radiomics based on dual-layer spectral detector CT for predicting EGFR mutation status in non-small cell lung cancer.Journal of applied clinical medical physics · 2025Article
- Application of Chest CT Imaging Feature Model in Distinguishing Squamous Cell Carcinoma and Adenocarcinoma of the Lung.Cancer management and research · 2024Article
Corrections and comments
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Patients with epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma (LUAD) can benefit from individualized targeted therapy. This study aims to develop, compare, analyse prediction models based on dual-energy spectral computed tomography (DESCT) and CT-based radiomic features to non-invasively predict EGFR mutation status in LUAD. Materials and methods: Patients with LUAD (n = 175), including 111 patients with and 64 patients without EGFR mutations, were enrolled in the current study. All patients were randomly divided into a training dataset (122 cases) and validation dataset (53 cases) at a ratio of 7:3. After extracting CT-based radiomic, DESCT and clinical features, we built seven prediction models and a nomogram of the best prediction. Receiver operating characteristic (ROC) curves and the mean area under the curve (AUC) values were used for comparisons among the models to obtain the best prediction model for predicting EGFR mutations. Results: The best distinguishing ability is the combined model incorporating radiomic, DESCT and clinical features for predicting the EGFR mutation status with an AUC of 0.86 (95 % CI: 0.79-0.92) in the training group and an AUC value of 0.83 (95 % CI: 0.73, 0.96) in the validation group. Conclusions: Our study provides a predictive nomogram non-invasively with a combination of CT-based radiomic, DESCT and clinical features, which can provide image-based biological information for targeted therapy of LUAD with EGFR mutations.
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Registered trials
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