Neuroradiology · 2025
Improving differentiation of hemorrhagic brain metastases from non-neoplastic hematomas using radiomics and clinical feature fusion.
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Cui Linyang, Yu Luyue, Shao Sai, Zuo Liping, Hou Hongjun, Liu Jie, Zhang Wenjun, Liu Ju, Wu Qiang, Yu Dexin
Аннотация
OBJECTIVES: This study aimed to develop and validate a fusion model combining multi-sequence MRI radiomics and clinico-radiological features to distinguish hemorrhagic brain metastasis covered by hematoma (HBM.cbh) from non-neoplastic intracranial hematomas (nn-ICH).
METHODS: The data of 146 patients with pathologically or clinically proven HBM.cbh (n = 55) and nn-ICH (n = 91) were collected from two clinical institutions. Radiomics features were extracted from various regions (hemorrhage and/or edema) based on T2-weighted, T1-weighted, fluid-attenuated inversion-recovery, and T1 contrast-enhanced imaging. Synthetic minority over-sampling technique (SMOTE) was performed to balance the minority group (HBM.cbh). Logistic regression (LR) and k-nearest neighbors (KNN) were utilized to construct the models based on clinico-radiological factors (clinical model), radiomic features from various modalities of MRI (radiomics model), and their combination (fusion model). The area under the curve (AUC) values of different models on the external dataset were compared using DeLong's test.
RESULTS: The 4-sequence radiomics model based on the entire region performed the best in all radiomics models, with or without SMOTE, where the AUCs were 0.83 and 0.84, respectively. The AUC of clinical mode was 0.71 with SMOTE, and 0.62 without SMOTE. The fusion model demonstrated excellent predictive value with or without SMOTE (AUC: 0.93 and 0.90, respectively), outperforming both the radiomics and clinical model (0.93 vs. 0.83, 0.71, p < 0.05 and 0.90 vs. 0.84, 0.62, p < 0.05, respectively).
CONCLUSIONS: The multi-sequence radiomics model is an effective method for differentiating HBM.cbh from nn-ICH. It can yield the best diagnostic performance prediction model when combined with clinico-radiological features.
METHODS: The data of 146 patients with pathologically or clinically proven HBM.cbh (n = 55) and nn-ICH (n = 91) were collected from two clinical institutions. Radiomics features were extracted from various regions (hemorrhage and/or edema) based on T2-weighted, T1-weighted, fluid-attenuated inversion-recovery, and T1 contrast-enhanced imaging. Synthetic minority over-sampling technique (SMOTE) was performed to balance the minority group (HBM.cbh). Logistic regression (LR) and k-nearest neighbors (KNN) were utilized to construct the models based on clinico-radiological factors (clinical model), radiomic features from various modalities of MRI (radiomics model), and their combination (fusion model). The area under the curve (AUC) values of different models on the external dataset were compared using DeLong's test.
RESULTS: The 4-sequence radiomics model based on the entire region performed the best in all radiomics models, with or without SMOTE, where the AUCs were 0.83 and 0.84, respectively. The AUC of clinical mode was 0.71 with SMOTE, and 0.62 without SMOTE. The fusion model demonstrated excellent predictive value with or without SMOTE (AUC: 0.93 and 0.90, respectively), outperforming both the radiomics and clinical model (0.93 vs. 0.83, 0.71, p < 0.05 and 0.90 vs. 0.84, 0.62, p < 0.05, respectively).
CONCLUSIONS: The multi-sequence radiomics model is an effective method for differentiating HBM.cbh from nn-ICH. It can yield the best diagnostic performance prediction model when combined with clinico-radiological features.