ArticleJournal of thoracic disease2021
Value of radiomics model based on multi-parametric magnetic resonance imaging in predicting epidermal growth factor receptor mutation status in patients with lung adenocarcinoma.
Article in Journal of thoracic disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 19 citations in OpenAlex.
- Prediction of EGFR mutation status in non-small cell lung cancer based on multiparametric MRI radiomics.BMC medical imaging · 2025Article
- Identification of the pathological subtypes of lung cancer brain metastases with multiparametric MRI radiomics: A feasibility study.Scientific reports · 2025Article
- Radiomics based on dual-layer spectral detector CT for predicting EGFR mutation status in non-small cell lung cancer.Journal of applied clinical medical physics · 2025Article
- The diagnostic value of vascular architecture in solid solitary pulmonary nodules quantified by dynamic contrast enhanced MRI.Journal of thoracic disease · 2025Article
- T1 mapping-based radiomics in the identification of histological types of lung cancer: a reproducibility and feasibility study.BMC medical imaging · 2024Article
- Predicting the T790M mutation in non-small cell lung cancer (NSCLC) using brain metastasis MR radiomics: a study with an imbalanced dataset.Discover oncology · 2024Article
- Differentiating inflammatory and malignant pulmonary lesions on 3T lung MRI with radiomics of apparent diffusion coefficient maps and T2w derived radiomic feature maps.Journal of thoracic disease · 2024Article
- Applying multisequence MRI radiomics of the primary tumor and lymph node to predict HPV-related p16 status in patients with oropharyngeal squamous cell carcinoma.Quantitative imaging in medicine and surgery · 2023Article
- Radiomics models based on multi-sequence MRI for preoperative evaluation of MUC4 status in pancreatic ductal adenocarcinoma: a preliminary study.Quantitative imaging in medicine and surgery · 2022Article
- Predicting Kirsten Rat Sarcoma Virus Gene Mutation Status in Patients With Colorectal Cancer by Radiomics Models Based on Multiphasic CT.Frontiers in oncology · 2022Article
- Preliminary study of 3 T-MRI native T1-mapping radiomics in differential diagnosis of non-calcified solid pulmonary nodules/masses.Cancer cell international · 2021Article
- A predictive model of computed tomography and clinical features of EGFR gene mutation in lung adenocarcinoma.Science progressArticle
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe epidermal growth factor receptor (EGFR) is an important therapeutic target for patients with non-small-cell lung cancer (NSCLC). Radiomics and radiogenomics have emerged as attractive research topics aiming to extract mineable high-dimensional features from medical images and show potential to correlate with the gene mutation. Herein, we aim to develop a magnetic resonance imaging (MRI)-based radiomics model for pretreatment prediction of the EGFR status in patients with lung adenocarcinoma.
methodsA total of 92 patients with pathologically confirmed lung adenocarcinoma were retrospectively enrolled in this study. EGFR genotype was analyzed by sequence testing. All patients were randomized into training and test group in a 7:3 ratio using the R software. Radiomics features were extracted from T2 weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC); radiomics signatures were built using the least absolute shrinkage and selection operator (LASSO) and logistic regression. Preoperative clinical factors and image features associated with EGFR were also evaluated. A nomogram including sex, smoking status, and radiomics signatures was constructed. A total of five radiomics models were built, and the area under the curve (AUC) was used to evaluate their performance of EGFR mutation prediction.
resultsAmong the three single-sequence models, the ADC model showed the best prediction performance. The AUCs of the ADC, DWI, T2WI prediction model in the test cohort were 0.805 (95% CI: 0.610 to 1.000), 0.722 (95% CI: 0.519 to 0.924), and 0.655 (95% CI: 0.438 to 0.872), respectively. Compared with the single-sequence model, the multi-sequence prediction model showed better performed [AUC
conclusionsThe radiomics model based on MRI might have the potential to predict EGFR mutation in patients with lung adenocarcinoma. The multi-sequence model had better performance than other models.
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