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Open Access 01-12-2023 | Glioma | Research

Using machine learning-based radiomics to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes

Authors: Feng-Ying Zhu, Yu-Feng Sun, Xiao-Ping Yin, Yu Zhang, Li-Hong Xing, Ze-Peng Ma, Lin-Yan Xue, Jia-Ning Wang

Published in: Discover Oncology | Issue 1/2023

Abstract

Objective

To establish a machine learning-based radiomics model to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes, thereby achieving accurate preoperative classification.

Materials and methods

A retrospective analysis was conducted on MRI T1WI-enhanced images of 105 patients with glioma and 172 patients with solitary brain metastasis from lung cancer, which were confirmed pathologically. The patients were divided into the training group and validation group in an 8:2 ratio for image segmentation, extraction, and filtering; multiple layer perceptron (MLP), support vector machine (SVM), random forest (RF), and logistic regression (LR) were used for modeling; fivefold cross-validation was used to train the model; the validation group was used to evaluate and assess the predictive performance of the model, ROC curve was used to calculate the accuracy, sensitivity, and specificity of the model, and the area under curve (AUC) was used to assess the predictive performance of the model.

Results

The accuracy and AUC of the MLP differentiation model for high-grade glioma and solitary brain metastasis in the validation group was 0.992, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.968, respectively. The accuracy and AUC for the MLP and SVM differentiation model for high-grade glioma and small cell lung cancer brain metastasis in the validation group was 0.966, 1.000, respectively, while the sensitivity and specificity were 1.000, 0.929, respectively. The accuracy and AUC for the MLP differentiation model for high-grade glioma and non-small cell lung cancer brain metastasis in the validation group was 0.982, 0.999, respectively, while the sensitivity and specificity were 0.958, 1.000, respectively.

Conclusion

The application of machine learning-based radiomics has a certain clinical value in differentiating glioma from solitary brain metastasis from lung cancer and its subtypes. In the HGG/SBM and HGG/NSCLC SBM validation groups, the MLP model had the best diagnostic performance, while in the HGG/SCLC SBM validation group, the MLP and SVM models had the best diagnostic performance.

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Title: Using machine learning-based radiomics to differentiate between glioma and solitary brain metastasis from lung cancer and its subtypes
Authors: Feng-Ying Zhu
Yu-Feng Sun
Xiao-Ping Yin
Yu Zhang
Li-Hong Xing
Ze-Peng Ma
Lin-Yan Xue
Jia-Ning Wang
Publication date: 01-12-2023
Publisher: Springer US
Keywords: Glioma
Glioma
Glioma
Lung Cancer
Lung Cancer
Lung Cancer
Metastasis
NSCLC
NSCLC
SCLC
SCLC
Magnetic Resonance Imaging
Magnetic Resonance Imaging
Published in: Discover Oncology / Issue 1/2023
Print ISSN: 1868-8497
Electronic ISSN: 2730-6011
DOI: https://doi.org/10.1007/s12672-023-00837-6

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