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Background <p>The location of the hemorrhagic of spontaneous intracerebral hemorrhage (sICH) is clinically pivotal for both identifying its etiology and prognosis, but comprehensive and quantitative modeling approach has yet to be thoroughly explored.</p> Methods <p>We employed lesion-symptom mapping to extract the location features of sICH. We registered patients’ non-contrast computed tomography image and hematoma masks with standard human brain templates to identify specific affected brain regions. Then, we generated hemorrhage probabilistic maps of different etiologies and prognoses. By integrating radiomics and clinical features into multiple logistic regression models, we developed and validated optimal etiological and prognostic models across three centers, comprising 1162 sICH patients.</p> Results <p>Hematomas of different etiology have unique spatial distributions. The location-based features demonstrated robust classification of the etiology of spontaneous intracerebral hemorrhage (sICH), with a mean area under the curve (AUC) of 0.825 across diverse datasets. These features provided significant incremental value when integrated into predictive models (fusion model mean AUC = 0.915), outperforming models relying solely on clinical features (mean AUC = 0.828). In prognostic assessments, both hematoma location (mean AUC = 0.762) and radiomic features (mean AUC = 0.837) contributed substantial incremental predictive value, as evidenced by the fusion model’s mean AUC of 0.873, compared to models utilizing clinical features alone (mean AUC = 0.771).</p> Conclusions <p>Our results show that location features were more intrinsically robust, generalizable relative, strong interpretability to the complex modeling of radiomics, our approach demonstrated a novel interpretable, streamlined, comprehensive etiologic classification and prognostic prediction framework for sICH.</p>

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Prediction of etiology and prognosis based on hematoma location of spontaneous intracerebral hemorrhage: a multicenter diagnostic study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月3日

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  • Jingjing Liang,
  • Weixiong Tan,
  • Shijia Xie,
  • Lijuan Zheng,
  • Chuyan Li,
  • Yi Zhong,
  • Jianrui Li,
  • Changsheng Zhou,
  • Zhiqiang Zhang,
  • Zhen Zhou,
  • Ping Gong,
  • Xingzhi Chen,
  • Longjiang Zhang,
  • Xiaoqing Cheng,
  • Qirui Zhang,
  • Guangming Lu

摘要

Background

The location of the hemorrhagic of spontaneous intracerebral hemorrhage (sICH) is clinically pivotal for both identifying its etiology and prognosis, but comprehensive and quantitative modeling approach has yet to be thoroughly explored.

Methods

We employed lesion-symptom mapping to extract the location features of sICH. We registered patients’ non-contrast computed tomography image and hematoma masks with standard human brain templates to identify specific affected brain regions. Then, we generated hemorrhage probabilistic maps of different etiologies and prognoses. By integrating radiomics and clinical features into multiple logistic regression models, we developed and validated optimal etiological and prognostic models across three centers, comprising 1162 sICH patients.

Results

Hematomas of different etiology have unique spatial distributions. The location-based features demonstrated robust classification of the etiology of spontaneous intracerebral hemorrhage (sICH), with a mean area under the curve (AUC) of 0.825 across diverse datasets. These features provided significant incremental value when integrated into predictive models (fusion model mean AUC = 0.915), outperforming models relying solely on clinical features (mean AUC = 0.828). In prognostic assessments, both hematoma location (mean AUC = 0.762) and radiomic features (mean AUC = 0.837) contributed substantial incremental predictive value, as evidenced by the fusion model’s mean AUC of 0.873, compared to models utilizing clinical features alone (mean AUC = 0.771).

Conclusions

Our results show that location features were more intrinsically robust, generalizable relative, strong interpretability to the complex modeling of radiomics, our approach demonstrated a novel interpretable, streamlined, comprehensive etiologic classification and prognostic prediction framework for sICH.