ArticleInternational journal of molecular sciences2021
Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
88 citing papers in PubMed, 5 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
- Quality and accuracy of radiomics models in predicting KRAS status in lung cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.Frontiers in oncology · 2025Pooled it
- AI models for the identification of prognostic and predictive biomarkers in lung cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled 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
- Interpretable habitat radiomics based nomogram for predicting T790M mutation in non-small cell lung cancer with brain metastases.Quantitative imaging in medicine and surgery · 2026Article
- CT radiomics for noninvasive prediction of histologic differentiation in gastric cancer.Medical physics · 2026Article
- Development and validation of a PET/CT radiomics and dual-task learning model for the prediction of pathological subtypes and EGFR mutation in non-small cell lung cancer.Translational lung cancer research · 2026Article
- Body composition radiomics integrated with machine learning to predict prognosis in advanced NSCLC treated with first-line immunochemotherapy and concurrent radiotherapy.BMC pulmonary medicine · 2026Article
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- A radio-genomics biomarker for precision epidermal growth factor receptor mutation targeting therapy in non-small cell lung cancer.Scientific reports · 2026Article
- Pathomic model to predict the expression of UQCRH and overall survival of lung adenocarcinoma patients.Diagnostic pathology · 2026Article
- Applications of AI/ML in accelerating the development of pulmonary drug delivery system.Acta pharmaceutica Sinica. B · 2026Review
- A CT imaging-based deep learning model for predicting EGFR and KRAS mutations in non-small cell lung cancer: toward personalized treatment approaches.Translational lung cancer research · 2026Article
- Prediction of EGFR mutation status and mutation sites in lung cancer based on radiomics and deep learning.Journal of thoracic disease · 2025Article
- Radiology-based artificial intelligence for predicting targeted therapy response in pan-cancer: a comprehensive review.Journal of translational medicine · 2025Review
- Explainable Artificial Intelligence: A Perspective on Drug Discovery.Pharmaceutics · 2025Review
- Construction of a radiogenomics predictive model for KRAS mutation status in patients with non-small cell lung cancer.Journal of thoracic disease · 2025Article
- Radiogenomics-based prediction ofZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Article
- Machine learning algorithms for predicting malignancy grades of lung adenocarcinoma and guiding treatments: CT radiomics-based comparisons.Journal of thoracic disease · 2025Article
28 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Early identification of epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations is crucial for selecting a therapeutic strategy for patients with non-small-cell lung cancer (NSCLC). We proposed a machine learning-based model for feature selection and prediction of EGFR and KRAS mutations in patients with NSCLC by including the least number of the most semantic radiomics features. We included a cohort of 161 patients from 211 patients with NSCLC from The Cancer Imaging Archive (TCIA) and analyzed 161 low-dose computed tomography (LDCT) images for detecting EGFR and KRAS mutations. A total of 851 radiomics features, which were classified into 9 categories, were obtained through manual segmentation and radiomics feature extraction from LDCT. We evaluated our models using a validation set consisting of 18 patients derived from the same TCIA dataset. The results showed that the genetic algorithm plus XGBoost classifier exhibited the most favorable performance, with an accuracy of 0.836 and 0.86 for detecting EGFR and KRAS mutations, respectively. We demonstrated that a noninvasive machine learning-based model including the least number of the most semantic radiomics signatures could robustly predict EGFR and KRAS mutations in patients with NSCLC.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.