ArticleEuropean journal of nuclear medicine and molecular imaging2020
Value of pre-therapy
Article in European journal of nuclear medicine and molecular imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers, 9 of them syntheses that pooled it.
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Who cites it
119 citing papers in PubMed, 9 syntheses or guidelines pooled it.
- Diagnostic performance of machine learning-based radiomics models for predicting epidermal growth factor receptor mutation status in lung adenocarcinoma in Chinese patients: A systematic review and meta-analysis.The Journal of international medical research · 2026Pooled 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
- Correlation between carcinoembryonic antigen (CEA) expression and EGFR mutations in non-small-cell lung cancer: a meta-analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2024Pooled it
- Predictive value ofFrontiers in oncology · 2024Pooled 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
- CanBMJ open · 2021Pooled it
- Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review.The British journal of radiology · 2021Pooled it
- The Future of Cancer Diagnosis, Treatment and Surveillance: A Systemic Review on Immunotherapy and Immuno-PET Radiotracers.Molecules (Basel, Switzerland) · 2021Pooled it
- Preoperative Prediction Power of Imaging Methods for Microvascular Invasion in Hepatocellular Carcinoma: A Systemic Review and Meta-Analysis.Frontiers in oncology · 2020Pooled it
- Noninvasive Method for Predicting the Expression of Ki67 and Prognosis in Non-Small-Cell Lung Cancer Patients: Radiomics.Journal of healthcare engineering · 2022Trial
- Development and validation of a novel scoring system developed from a nomogram to identify malignant pleural effusion.EBioMedicine · 2020Trial
- Preoperative PET/CT phenotype of gastric-type endocervical adenocarcinoma: integrated morphological, metabolic, serological, and explainable machine-learning analysis.European journal of nuclear medicine and molecular imaging · 2026Article
- Development and validation of a nomogram for the non-invasive prediction of human epidermal growth factor receptor 2 expression in gastric adenocarcinoma: anJournal of gastrointestinal oncology · 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
- Association of Driver Oncogenic Alterations with SUVmax, Preoperative Serum Calcium, and Smoking Status in Surgically Resected Non-Small-Cell Lung Cancer: A Retrospective Single-Center Study.Journal of clinical medicine · 2026Article
- The role of radiomics in predicting the response to neoadjuvant chemotherapy for breast cancer.Cancer biology & medicine · 2026Review
- Multicenter peritumoral radiomics in oncology: advances, challenges, and future directions.Radiation oncology (London, England) · 2026Review
- Habitat-aware radiomics and adaptive 2.5D deep learning predict treatment response and long-term survival in ESCC patients undergoing neoadjuvant chemoimmunotherapy.European journal of nuclear medicine and molecular imaging · 2026Article
- Ensemble learning strategy-based 18 F-FDG PET/CT metabolic habitats radiomics for predicting EGFR mutation and prognosis in LA-NSCLC: a multi-center study.BMC medical imaging · 2026Article
- The Predictive Value ofCurrent medical imaging · 2026Article
59 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo assess the predictive power of pre-therapy
methodsTwo hundred and forty-eight lung cancer patients underwent pre-therapy diagnostic
resultsThe patients were divided into a training set (n = 175) and a validation set (n = 73). Ten radiomic features were selected to build the radiomics signature model. The model showed a significant ability to discriminate between EGFR mutation and EGFR wild type, with area under the ROC curve (AUC) equal to 0.79 in the training set, and 0.85 in the validation set, compared with 0.75 and 0.69 for the clinical model. When clinical variables and radiomics signature were combined, the AUC increased to 0.86 (95% CI [0.80-0.91]) in the training set and 0.87 (95% CI [0.79-0.95]) in the validation set, thus showing better performance in the prediction of EGFR mutations.
conclusionThe PET/CT-based radiomic features showed good performance in predicting EGFR mutation in non-small cell lung cancer, providing a useful method for the choice of targeted therapy in a clinical setting.
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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.