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• Clinical Article •
Differentiation of high-grade glioma and metastatic tumor based on MRI radiomics and semantic features
XU Zichao  ZHANG Ya  LIU Qing  SHI Zhaoxia  WANG Jing  WEI Hongyang  PENG Xingzhen  ZONG Huiqian 

Cite this article as: XU Z C, ZHANG Y, LIU Q, et al. Differentiation of high-grade glioma and metastatic tumor based on MRI radiomics and semantic features[J]. Chin J Magn Reson Imaging, 2024, 15(8): 103-109, 123. DOI:10.12015/issn.1674-8034.2024.08.016.


[Abstract] Objective To combine traditional MRI sequences and enhancement scans, extract multimodal high-throughput radiomics features along with semantic features, and use different learning classifiers to construct various models and draw Normogragh for the differentiation of high-grade glioma (HGG) and solitary brain metastasis (SBM).Materials and Methods This study retrospectively analyzed multiparametric MRI images of 101 patients. Tumor region of interest (ROI) were delineated by two experienced physicians, and 107 sets of radiomic features for each sequence were extracted using the Pyradiomics software package. To eliminate variability in manual delineation, an intraclass correlation coefficient (ICC) consistency test was carried out. The features with the highest relevance were selected using the maximum relevance minimum redundancy algorithm, and then redundant features were further eliminated using the least absolute shrinkage and selection operator method. Classification models were established using four algorithms: support vector machine, logistic regression, random forest, and K-nearest neighbors. Combining seven semantic features evaluated by radiologists, chi-square test and multivariate analysis were used to remove semantically irrelevant features. Then, a comprehensive model incorporating both radiomics and semantic features was formed and illustrated using nomogram. Finally, the diagnostic capability of each model was evaluated to determine the optimal classifier.Results Among the radiomics models for HGG and SBM patients, the model with the highest area under the curve (AUC) value was logistic regression, with AUC values of 0.90 for both the training set and test set. In models constructed using semantic features, the random forest model exhibited the best performance, with AUC values of 0.82 and 0.87 for the training and test sets, respectively. After combining semantic features with radiomics scores, the model constructed using logistic regression demonstrated optimal performance, with AUC values of 0.91 and 0.92 for the training and test sets, respectively.Conclusions The non-invasive approach proposed in this study that utilizes radiomics machine learning classifiers and combines image semantic features to draw nomogram for differentiating between HGG and SBM, demonstrates good accuracy and provides significant assistance for clinical decision-making and practice.
[Keywords] high-grade glioma；solitary brain metastasis；magnetic resonance imaging；radiomics；machine learning；semantic features；nomogram

XU Zichao1   ZHANG Ya2   LIU Qing2   SHI Zhaoxia2   WANG Jing2   WEI Hongyang2   PENG Xingzhen2   ZONG Huiqian2*  

1 Department of Medical Imaging, the Second Hospital of Hebei Medical University, Shijiazhuang 050000, China

2 Department of Medical Equipment, the Second Hospital of Hebei Medical University, Shijiazhuang 050000, China

Corresponding author: ZONG H Q, E-mail: zonghuiqian@sina.com

Conflicts of interest   None.

Received  2024-03-01
Accepted  2024-08-02
DOI: 10.12015/issn.1674-8034.2024.08.016
Cite this article as: XU Z C, ZHANG Y, LIU Q, et al. Differentiation of high-grade glioma and metastatic tumor based on MRI radiomics and semantic features[J]. Chin J Magn Reson Imaging, 2024, 15(8): 103-109, 123. DOI:10.12015/issn.1674-8034.2024.08.016.

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