ArticleScientific reports2024
Machine learning-based radiomics strategy for prediction of acquired EGFR T790M mutation following treatment with EGFR-TKI in NSCLC.
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 16 papers, 3 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
16 citing papers in PubMed, 3 syntheses or guidelines pooled it, 13 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
- Predicting T790M mutation status in non-small cell lung cancer based on radiomics: A systematic review and meta-analysis.PloS one · 2026Pooled 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
- CT-based deep learning for survival stratification in EGFR-mutant lung adenocarcinoma after EGFR-TKI resistance: A multicenter study.iScience · 2026Article
- Review
- Radiomics to understand pre-treatment tumor biology for resectable non-small cell lung cancer.Frontiers in oncology · 2026Review
- 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
- Multi-algorithm radiomics machine learning models integrating ultrasound imaging and inflammation-immune features for hepatic metastases identification.BMC medical imaging · 2025Article
- Real-world treatment outcomes in South Korean patients with epidermal growth factor receptor-mutant non-small cell lung cancer.The Korean journal of internal medicine · 2025Article
- Artificial intelligence in medical education for pulmonary nodule management: a narrative review.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
- MRI-based radiomics for noninvasive prediction of T790M resistance mutation in lung cancer spinal metastases: an exploratory study.Frontiers in cell and developmental biology · 2025Article
- Multimodal omics analysis of the EGFR signaling pathway in non-small cell lung cancer and emerging therapeutic strategies.Oncology research · 2025Review
- High throughput virtual screening and validation of Plant-Based EGFR L858R kinase inhibitors against Non-Small cell lung Cancer: An integrated approach Utilizing GC-MS, network Pharmacology, Docking, and molecular dynamics.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The epidermal growth factor receptor (EGFR) Thr790 Met (T790M) mutation is responsible for approximately half of the acquired resistance to EGFR-tyrosine kinase inhibitor (TKI) in non-small-cell lung cancer (NSCLC) patients. Identifying patients at diagnosis who are likely to develop this mutation after first- or second-generation EGFR-TKI treatment is crucial for better treatment outcomes. This study aims to develop and validate a radiomics-based machine learning (ML) approach to predict the T790M mutation in NSCLC patients at diagnosis. We collected retrospective data from 210 positive EGFR mutation NSCLC patients, extracting 1316 radiomics features from CT images. Using the LASSO algorithm, we selected 10 radiomics features and 2 clinical features most relevant to the mutations. We built models with 7 ML approaches and assessed their performance through the receiver operating characteristic (ROC) curve. The radiomics model and combined model, which integrated radiomics features and relevant clinical factors, achieved an area under the curve (AUC) of 0.80 (95% confidence interval [CI] 0.79-0.81) and 0.86 (0.87-0.88), respectively, in predicting the T790M mutation. Our study presents a convenient and noninvasive radiomics-based ML model for predicting this mutation at the time of diagnosis, aiding in targeted treatment planning for NSCLC patients with EGFR mutations.
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.