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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

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

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.

Methods

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.

Results

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.

Conclusions

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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Abbreviations

AD:

Adenocarcinoma

AUC:

Area under the curve

BM:

Brain metastases

CECT:

Contrast-enhanced computer tomography

ECCR:

Ethics Committee in Clinical Research

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run-length matrix

GLZLM:

Gray-level zone length matrix

LASSO:

Least absolute shrinkage and selection operator

MRI:

Magnetic resonance imaging

NGLDM:

Neighborhood gray-level different matrix

NSCLC:

Non-small-cell lung cancer

ROC:

Receiver operating characteristic

SCC:

Squamous cell carcinoma

SCLC:

Small cell lung cancer

SVM:

Support vector machines

TTF-1:

Thyroid transcription factor-1

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Funding

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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Authors and Affiliations

  1. Department of Radiation and Medical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China

    Ji Zhang, Juebin Jin, Yao Ai, Kecheng Zhu, Chengjian Xiao, Congying Xie & Xiance Jin

  2. Department of Radiation and Medical Oncology, The Second Affiliated Hospital of Wenzhou Medical University, No. 109 West Xueyuan Road, Wenzhou, 325000, China

    Congying Xie

Authors
  1. Ji Zhang
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  2. Juebin Jin
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  3. Yao Ai
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  4. Kecheng Zhu
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  5. Chengjian Xiao
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  6. Congying Xie
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  7. Xiance Jin
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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). https://doi.org/10.1007/s00330-020-07183-z

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  • DOI: https://doi.org/10.1007/s00330-020-07183-z

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