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Differentiating the pathological subtypes of primary lung cancer for patients with brain metastases based on radiomics features from brain CT images



It is of high clinical importance to identify the primary lesion and its pathological types for patients with brain metastases (BM). The purpose of this study is to investigate the feasibility and accuracy of differentiating the primary adenocarcinoma (AD) and squamous cell carcinoma (SCC) of non-small-cell lung cancer (NSCLC) for patients with BM based on radiomics from brain contrast-enhanced computer tomography (CECT) images.


A total of 144 BM patients (94 male, 50 female) were enrolled in this study with 102 with primary lung AD and 42 with SCC, respectively. Radiomics features from manually contoured tumors were extracted using python. Mann–Whitney U test and the least absolute shrinkage and selection operator (LASSO) logistic regression were applied to select relative radiomics features. Binary logistic regression and support vector machines (SVM) were applied to build models with radiomics features alone and with radiomics features plus age and sex.


Fourteen features were selected from a total of 105 radiomics features for the final model building. The area under the curves (AUCs) and accuracy of SVM and binary logistic regression models were 0.765 vs. 0.769, 0.795 vs.0.828, and 0.716 vs. 0.726, 0.768 vs. 0.758, respectively, for models with radiomics features alone and models with radiomics features plus sex and age.


Brain CECT radiomics are promising in differentiating primary AD and SCC to achieve optimal therapeutic management in patients with BM from NSCLC.

Key Points

It is of high clinical importance to identify the primary lesion and its pathological types for patients with brain metastases (BM) to define the prognosis and treatment.

Few studies had investigated the feasibility and accuracy of differentiating the pathological subtypes of primary non-small-cell lung cancer between adenocarcinoma (AD) and squamous cell carcinoma (SCC) for patients with BM based on radiomics from brain contrast-enhanced CT (CECT) images, although CECT images are often the initial imaging modality to screen for metastases and are recommended on equal footing with MRI for the detection of cerebral metastases.

• Brain CECT radiomics are promising in differentiating primary AD and SCC to achieve optimal therapeutic management in patients with BM from NSCLC with a highest area under the curve (AUC) of 0.828 and an accuracy of 0.758, respectively.

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Fig. 1
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Area under the curve


Brain metastases


Contrast-enhanced computer tomography


Ethics Committee in Clinical Research


Gray-level co-occurrence matrix


Gray-level run-length matrix


Gray-level zone length matrix


Least absolute shrinkage and selection operator


Magnetic resonance imaging


Neighborhood gray-level different matrix


Non-small-cell lung cancer


Receiver operating characteristic


Squamous cell carcinoma


Small cell lung cancer


Support vector machines


Thyroid transcription factor-1


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This work was partially funded by Wenzhou Municipal Science and Technology Bureau (Nos. 2018ZY016 and H20180003) and National Natural Science Foundation of China under Grant (No. 11675122).

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Correspondence to Congying Xie or Xiance Jin.

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The scientific guarantor of this publication is Xiance Jin.

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The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

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Written informed consent was waived by the Institutional Review Board.

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Zhang, J., Jin, J., Ai, Y. et al. Differentiating the pathological subtypes of primary lung cancer for patients with brain metastases based on radiomics features from brain CT images. Eur Radiol 31, 1022–1028 (2021).

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  • Brain tumors
  • Metastasis
  • Non-small-cell lung cancer
  • Adenocarcinoma
  • Squamous cell carcinoma