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• Clinical Article •
Efficacy of different machine learning models for contrast-enhanced T1-weighted image radiomics in classifying brain metastases by their primary site of origin
SUI Lianyu  REN Jialiang  WANG Jianing  YIN Xiaoping 

Cite this article as: Sui LY, Ren JL, Wang JN, et al. Efficacy of different machine learning models for contrast-enhanced T1-weighted image radiomics in classifying brain metastases by their primary site of origin[J]. Chin J Magn Reson Imaging, 2022, 13(12): 74-80. DOI:10.12015/issn.1674-8034.2022.12.013.


[Abstract] Objective To establish radiomics of different machine learning algorithms models based on enhanced T1-weighted images,and to explore the value of the models for distinguishing lung cancer brain metastases and non-Lung cancer brain metastases.Materials and Methods Totally 728 patients with lung cancer brain metastases and 126 patients with non-Lung cancer brain metastases were randomly divided into training set (n=599) and testing set (n=255) according to the ratio of 7∶3. Enhanced MRI data were imported into ITK-SNAP software, and the tumor's region of interest (ROI) in the enhanced T1WI was manually delineated to ROI. Radiomics feature extraction and screening using the least absolute shrinkage selection operator based on ROI. Support vector machine (SVM) model, random forest model and logistic regression model based on salient features were established respectively. Receiver operating characteristic (ROC) curve was used to assess the diagnostic efficiency of the models for distinguishing lung cancer brain metastases and non-Lung cancer brain metastases.Results After feature screening, 5 salient features were finally retained. The most effective radiomics model was the SVM model. In the training set. The area under the curve (AUC) of SVM was 0.796, accuracy value of 85.3%, sensitivity value of 87.8%, specificity value of 70.8%. In the testing set, AUC value of 0.789, accuracy of 90.2%, sensitivity of 95.4%, specificity of 59.5%.Conclusions Radiomics models based on enhanced MRI can be used for effectively predicting the lung cancer and non-lung cancer primary focus of brain metastatic cancer with unknown primary tumor. SVM model has higher diagnostic value than random forest model and logistic regression models.
[Keywords] brain metastases；lung cancer；radiomics；contrast-enhanced T1-weighted images；magnetic resonance imaging

SUI Lianyu1   REN Jialiang2   WANG Jianing1   YIN Xiaoping1*  

1 Department of Radiology, Affiliated Hospital of Hebei University, Baoding 071000, China

2 General Electric Pharmaceutical (Shanghai) Co., Ltd., Shanghai 200203, China

Yin XP, E-mail: yinxiaoping78@sina.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Post-graduate's Innovation Fund Project of Hebei University (No. HBU2022ss024).
Received  2022-07-17
Accepted  2022-11-14
DOI: 10.12015/issn.1674-8034.2022.12.013
Cite this article as: Sui LY, Ren JL, Wang JN, et al. Efficacy of different machine learning models for contrast-enhanced T1-weighted image radiomics in classifying brain metastases by their primary site of origin[J]. Chin J Magn Reson Imaging, 2022, 13(12): 74-80. DOI:10.12015/issn.1674-8034.2022.12.013.

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