Scholarlink

Objectives <p>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).</p> Methods <p>The data of 146 patients with pathologically or clinically proven HBM.cbh (<i>n</i> = 55) and nn-ICH (<i>n</i> = 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.</p> Results <p>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, <i>p</i> &lt; 0.05 and 0.90 vs. 0.84, 0.62, <i>p</i> &lt; 0.05, respectively).</p> Conclusions <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Improving differentiation of hemorrhagic brain metastases from non-neoplastic hematomas using radiomics and clinical feature fusion

  • 期刊论文

  • 发表日期：

    2025年3月25日

查看全文
  • Linyang Cui,
  • Luyue Yu,
  • Sai Shao,
  • Liping Zuo,
  • Hongjun Hou,
  • Jie Liu,
  • Wenjun Zhang,
  • Ju Liu,
  • Qiang Wu,
  • Dexin Yu

摘要

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.