ArticleClinical lung cancer2016
Radiomic Features Are Associated With EGFR Mutation Status in Lung Adenocarcinomas.
Article in Clinical lung cancer, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 179 papers, 3 of them syntheses that pooled it.
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Who cites it
179 citing papers in PubMed, 3 syntheses or guidelines pooled 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
- 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
- CT and clinical characteristics that predict risk of EGFR mutation in non-small cell lung cancer: a systematic review and meta-analysis.International journal of clinical oncology · 2019Pooled it
- Standard deviation of CT radiomic features among malignancies in each individual: prognostic ability in lung cancer patients.Journal of cancer research and clinical oncology · 2023Trial
- Noninvasive Method for Predicting the Expression of Ki67 and Prognosis in Non-Small-Cell Lung Cancer Patients: Radiomics.Journal of healthcare engineering · 2022Trial
- Trial
- Quality over quantity: biopsy-anchored CT radiogenomics models outperform all-lesion training in a multi-tumour cohort despite a smaller sample size.European radiology · 2026Article
- A perioperative multi-modal fusion and deep learning-based prognostic system for upper tract urothelial carcinoma: a multi-institutional study.Insights into imaging · 2026Article
- Development and evaluation of multi-strategy fusion machine learning models for predicting postoperative recurrence in non-small cell lung cancer.BMC medical imaging · 2026Article
- A radio-genomics biomarker for precision epidermal growth factor receptor mutation targeting therapy in non-small cell lung cancer.Scientific reports · 2026Article
- TabPFN-driven ternary classification of stage IA lung adenocarcinoma subtypes using AI-derived histogram features a retrospective multicenter cohort study.International journal of surgery (London, England) · 2026Article
- Article
- Interpretable machine learning model integrating CT radiomics, CTR, and clinical features for EGFR mutation prediction in ≤3 cm lung adenocarcinoma nodules.Annals of medicine · 2025Article
- Radiogenomics: transforming lung cancer care through non-invasive imaging and genomic integration.Medical oncology (Northwood, London, England) · 2025Review
- An Integrated Clinical-Radiomics-Deep Learning Model Based onCancer medicine · 2025Article
- Early Lung Cancer Detection via AI-Enhanced CT Image Processing Software.Diagnostics (Basel, Switzerland) · 2025Article
- [Application Value of an AI-based Imaging Feature Parameter Model for Predicting the Malignancy of Part-solid Pulmonary Nodule].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025Article
- Machine Learning-Based Radiomics Analysis for Identifying KRAS Mutations in Non-Small-Cell Lung Cancer from CT Images: Challenges, Insights and Implications.Life (Basel, Switzerland) · 2025Article
- Deep learning on brain metastasis for predicting EGFR genotype and EGFR-TKI therapy response in metastatic NSCLC: a multicenter study.Frontiers in bioengineering and biotechnology · 2025Article
- On the robustness of [18F]-FDG-PET radiomic features to variations in image acquisition and reconstruction settings: A phantom study.PloS one · 2025Article
119 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
Abstract
backgroundIn this study we retrospectively evaluated the capability of computed tomography (CT)-based radiomic features to predict epidermal growth factor receptor (EGFR) mutation status in surgically-resected peripheral lung adenocarcinomas in an Asian cohort of patients. PATIENTS AND
methodsTwo hundred ninety-eight patients with surgically resected peripheral lung adenocarcinomas were investigated in this institutional review board-approved retrospective study with requirement waived to obtain informed consent. Two hundred nineteen quantitative 3-D features were extracted from segmented volumes of each tumor, and 59 of these, which were considered independent features, were included in the analysis. Clinical and pathological information was obtained from the institutional database.
resultsMutant EGFR was significantly associated with female sex (P = .0005); never smoker status (P < .0001), lepidic predominant adenocarcinomas (P = .017), and low or intermediate pathologic grade (P = .0002). Statistically significant differences were found in 11 radiomic features between EGFR mutant and wild type groups in univariate analysis. Mutant EGFR status could be predicted by a set of 5 radiomic features that fell into 3 broad groups: CT attenuation energy, tumor main direction, and texture defined according to wavelets and Laws (area under the curve [AUC], 0.647). A multiple logistic regression model showed that adding radiomic features to a clinical model resulted in a significant improvement of predicting power, because the AUC increased from 0.667 to 0.709 (P < .0001).
conclusionComputed tomography-based radiomic features of peripheral lung adenocarcinomas can capture useful information regarding tumor phenotype, and the model we built can be useful to predict the presence of EGFR mutations in peripheral lung adenocarcinoma in Asian patients when mutational profiling is not available or possible.
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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.