ArticleScientific reports2024
Deep learning-radiomics integrated noninvasive detection of epidermal growth factor receptor mutations in non-small cell lung cancer patients.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 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
24 citing papers in PubMed, 2 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
- Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.Frontiers in oncology · 2025Pooled it
- MRI-based deep learning combined with radiomics for the preoperative prediction of lymphovascular invasion in patients with bladder cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Artificial Intelligence Tools in Precision Lung Cancer Care: From Early Detection to Clinical Decision Support.Cancers · 2026Review
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Charting the blueprint: a bibliometric analysis reveals future strategies in lung cancer targeted therapy (2003-2025).Journal of thoracic disease · 2026Article
- Artificial intelligence in non-small cell lung cancer: transforming diagnosis, treatment, and prognostic evaluation.Frontiers in medicine · 2026Review
- Article
- Noninvasive assessment of core metastatic genes in lung adenocarcinoma: development of a predictive model integrating single-cell transcriptomics and radiomics.Frontiers in oncology · 2026Article
- Research progress in computer-aided diagnosis systems for lung cancer.NPJ digital medicine · 2025Review
- Deep learning enhances precision diagnosis and treatment of non-small cell lung cancer: future prospects.Translational lung cancer research · 2025Review
- Reproducibility of methodological radiomics score (METRICS): an intra- and inter-rater reliability study endorsed by EuSoMII.European radiology · 2025Article
- Radiomic Analysis and Liquid Biopsy in Preoperative CT of NSCLC: An Explorative Experience.Thoracic cancer · 2025Article
- Comparative Evaluation of Machine Learning-Based Radiomics and Deep Learning for Breast Lesion Classification in Mammography.Diagnostics (Basel, Switzerland) · 2025Article
- Prediction of EGFR-TP53 genes co-mutations in patients with lung adenocarcinoma (LUAD) byClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- MVASA-HGN: multi-view adaptive semantic-aware heterogeneous graph network forQuantitative imaging in medicine and surgery · 2025Article
- Article
- CT-based radiomics and deep learning to predict EGFR mutation status in lung adenocarcinoma.Frontiers in oncology · 2025Article
- Machine learning model for predicting epidermal growth factor receptor expression status in breast cancer using ultrasound radiomics.Frontiers in oncology · 2025Article
- Predicting Ki-67 expression levels in non-small cell lung cancer using an explainable CT-based deep learning radiomics model.Frontiers in oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
This study focused on a novel strategy that combines deep learning and radiomics to predict epidermal growth factor receptor (EGFR) mutations in patients with non-small cell lung cancer (NSCLC) using computed tomography (CT). A total of 1280 patients with NSCLC who underwent contrast-enhanced CT scans and EGFR mutation testing before treatment were selected for the final study. Regions of interest were segmented from the CT images to extract radiomics features and obtain tumor images. These tumor images were input into a convolutional neural network model to extract 512 image features, which were combined with radiographic features and clinical data to predict the EGFR mutation. The generalization performance of the model was evaluated using external institutional data. The internal and external datasets contained 324 and 130 EGFR mutants, respectively. Sex, height, weight, smoking history, and clinical stage were significantly different between the EGFR-mutant patient groups. The EGFR mutations were predicted by combining the radiomics and clinical features, and an external validation dataset yielded an area under the curve (AUC) value of 0.7038. The model utilized 1280 tumor images, radiomics features, and clinical characteristics as input data and exhibited an AUC of approximately 0.81 and 0.78 during the primary cohort and external validation, respectively. These results indicate the feasibility of integrating radiomics analysis with deep learning for predicting EGFR mutations. CT-image-based genetic testing is a simple EGFR mutation prediction method, which can improve the prognosis of NSCLC patients and help establish personalized treatment strategies.
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.