ArticleDiagnostic and interventional radiology (Ankara, Turkey)2022
Thoracic CT radiomics analysis for predicting synchronous brain metastasis in patients with lung cancer.
Article in Diagnostic and interventional radiology (Ankara, Turkey), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Prediction of brain metastasis in patients with epidermal growth factor receptor-positive lung adenocarcinoma based on lung computed tomography-derived radiomics features.BMC medical imaging · 2025Article
- Computed tomography-based radiomics and clinical-genetic features for brain metastasis prediction in patients with stage III/IV epidermal growth factor receptor-mutant non-small-cell lung cancer.Thoracic cancer · 2024Article
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6 authors.
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Abstract
purposeWe aimed to assess the feasibility of radiomics analysis based on non-contrast-enhanced thoracic CT images in predicting synchronous brain metastasis (SBM) in lung cancer patients at initial diagnosis.
methodsThis retrospective study enrolled 371 lung cancer patients (with SBM n=147, without SBM n=224) confirmed by histopathology. Patients were allocated to the training set (n=258) and testing set (n=113). The optimal radiomics features were selected by using the least absolute shrinkage and selection operator (LASSO) algorithm. The radiomics, clinicoradiologic, and combined models were developed to predict SBM using multivariable logistic regression. Then the discrimination ability of the models was assessed. Furthermore, the prediction performance of the abovementioned three models for oligometastatic (1-3 lesions) or multiple (>3 lesions) brain metastases in SBM, metachronous brain metastasis (MBM), and total (SBM and MBM) groups were investigated.
resultsSix radiomics features and two clinicoradiologic characteristics were chosen for predicting SBM. Both the radiomics model (area under the receiver operating characteristic curve [AUC] = 0.870 and 0.824 in the training and testing sets, respectively) and the combined model (AUC = 0.912 and 0.859, respectively) presented better predictive ability for SBM than the clinicoradiologic model (AUC = 0.712 and 0.692, respectively). The decision curve analysis (DCA) demonstrated the clinical usefulness of the radiomics-based models. The radiomics model can also be used to predict oligometastatic or multiple brain metastases in SBM, MBM, and total groups (P = .045, P = .022, and P = .030, respectively).
conclusionThe radiomics model and the combined model we presented can be used as valuable imaging markers for predicting patients at high risk of SBM at the initial diagnosis of lung cancer. Furthermore, the radiomics model can also be utilized as an indicator for identifying oligometastatic or multiple brain metastases.
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