ArticleScientific reports2021
Value of CT features for predicting EGFR mutations and ALK positivity in patients with lung adenocarcinoma.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 14 citations in OpenAlex.
- Deep learning-based CT radiomics for ALK rearrangement status prediction in lung adenocarcinoma.BMC cancer · 2026Article
- HER2-Mutant Non-Small Cell Lung Cancer Podcast: Knowledge Is Power.Targeted oncology · 2026Article
- Relationship between qualitative and quantitative parameters of three-dimensional computed tomography, EGFR gene mutation, and ALK gene rearrangement in GGO-associated lung adenocarcinoma and their prognostic value.Pathology oncology research : POR · 2025Article
- Prediction of EGFR mutations in non-small cell lung cancer: a nomogram based onFrontiers in oncology · 2025Article
- Imaging patterns and recommendations for diagnosis, staging, and management of lung cancer.BJR open · 2025Review
- The clinical, radiological, postoperative pathological, and genetic features of nodular lung adenocarcinoma: a real-world single-center data.Journal of thoracic disease · 2024Article
- Article
- CT-based decision tree model for predicting EGFR mutation status in synchronous multiple primary lung cancers.Journal of thoracic disease · 2023Article
- Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges.Journal of personalized medicine · 2022Review
- Deep learning analysis to predictFrontiers in oncology · 2022Article
- A commentary on the "Clinical and CT patterns to predict EGFR mutation in patients with non-small cell lung cancer: A systematic literature review and meta-analysis".European journal of radiology open · 2022Article
- Clinicopathologic Features and Molecular Biomarkers as Predictors of Epidermal Growth Factor Receptor Gene Mutation in Non-Small Cell Lung Cancer Patients.Current oncology (Toronto, Ont.) · 2021Review
- Molecular typing of lung adenocarcinoma with computed tomography and CT image-based radiomics: a narrative review of research progress and prospects.Translational cancer research · 2021Review
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to identify the relationships of epidermal growth factor receptor (EGFR) mutations and anaplastic large-cell lymphoma kinase (ALK) status with CT characteristics in adenocarcinoma using the largest patient cohort to date. In this study, preoperative chest CT findings prior to treatment were retrospectively evaluated in 827 surgically resected lung adenocarcinomas. All patients were tested for EGFR mutations and ALK status. EGFR mutations were found in 489 (59.1%) patients, and ALK positivity was found in 57 (7.0%). By logistic regression, the most significant independent prognostic factors of EGFR effective mutations were female sex, nonsmoker status, GGO air bronchograms and pleural retraction. For EGFR mutation prediction, receiver operating characteristic (ROC) curves yielded areas under the curve (AUCs) of 0.682 and 0.758 for clinical only or combined CT features, respectively, with a significant difference (p < 0.001). Furthermore, the exon 21 mutation rate in GGO was significantly higher than the exon 19 mutation rate(p = 0.029). The most significant independent prognostic factors of ALK positivity were age, solid-predominant-subtype tumours, mucinous lung adenocarcinoma, solid tumours and no air bronchograms on CT. ROC curve analysis showed that for predicting ALK positivity, the use of clinical variables combined with CT features (AUC = 0.739) was superior to the use of clinical variables alone (AUC = 0.657), with a significant difference (p = 0.0082). The use of CT features for patients may allow analyses of tumours and more accurately predict patient populations who will benefit from therapies targeting treatment.
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