Journal
DIAGNOSTIC AND INTERVENTIONAL RADIOLOGY
Volume 28, Issue 1, Pages 39-49Publisher
TURKISH SOC RADIOLOGY
DOI: 10.5152/dir.2021.21677
Keywords
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Funding
- National Natural Science Foundation of China [81830053, 92059202]
- Key Project of Scientific Research Fund of Wannan Medical College [WK2019ZF05]
- Scientific Research Project of the Chinese Red Cross foundation [XM_ HR_YXFN_2021_05_24]
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The radiomics model and the combined model can serve as valuable imaging markers for predicting lung cancer patients at high risk of synchronous brain metastasis (SBM) at initial diagnosis. Additionally, the radiomics model can also be utilized for identifying oligometastatic or multiple brain metastases in SBM.
PURPOSE We 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. METHODS This 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. RESULTS Six 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). CONCLUSION The radiomics model and the combined model 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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