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Objectives <p>This study aimed to develop and validate a prognostic model integrating hematoma (R1), perilesional (R2), and clinical features to predict 90-day outcomes.</p> Methods <p>A total of 759 ICH patients from two centers were enrolled and allocated to training, internal validation, and external test sets. The primary endpoint was a poor 90-day outcome, defined as a modified Rankin Scale (mRS) score &gt; 3. Independent clinical risk factors were identified via univariate and multivariate logistic regression analyses. Subsequently, seven prognostic models were constructed using R1, R2, clinical features, and their combinations. Model discrimination was compared using the DeLong test for the Area Under the Curve (AUC). Calibration and clinical utility were evaluated using calibration curves and Decision Curve Analysis (DCA).</p> Results <p>Multivariate analysis identified four independent risk factors for poor outcome: hematoma volume (OR 1.042; 95% CI 1.025–1.058; p &lt; 0.001), mean hematoma density (OR 0.916; 95% CI 0.863–0.973; p = 0.004), age (OR 1.078; 95% CI 1.054–1.103; p &lt; 0.001), and admission Glasgow Coma Scale (GCS) score (OR 0.777; 95% CI 0.708–0.853; p &lt; 0.001). Among the seven models constructed, the tri-combined model (R1 + R2+Clinical) demonstrated the most stable and relatively better performance across all datasets, with an AUC of 0.791 (95% CI: 0.716–0.867) in the external test set. This model exhibited good calibration and favorable statistical net benefit on DCA.</p> Conclusion <p>The integrated prognostic model combining hematoma and perilesional radiomic features with clinical data provides stable and incremental prognostic value for 90-day functional outcomes in patients with ICH.</p>

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Perilesional radiomics enhances 90-day outcome prediction in intracerebral hemorrhage: development and validation of a combined model with hematoma radiomics and clinical features

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月14日

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  • Kangwei Zhang,
  • Baoqing Yang,
  • Tianzhi Yan,
  • Kaixuan Wang,
  • Mingyu Tan,
  • Peijun Wang,
  • Zhongling Wang

摘要

Objectives

This study aimed to develop and validate a prognostic model integrating hematoma (R1), perilesional (R2), and clinical features to predict 90-day outcomes.

Methods

A total of 759 ICH patients from two centers were enrolled and allocated to training, internal validation, and external test sets. The primary endpoint was a poor 90-day outcome, defined as a modified Rankin Scale (mRS) score > 3. Independent clinical risk factors were identified via univariate and multivariate logistic regression analyses. Subsequently, seven prognostic models were constructed using R1, R2, clinical features, and their combinations. Model discrimination was compared using the DeLong test for the Area Under the Curve (AUC). Calibration and clinical utility were evaluated using calibration curves and Decision Curve Analysis (DCA).

Results

Multivariate analysis identified four independent risk factors for poor outcome: hematoma volume (OR 1.042; 95% CI 1.025–1.058; p < 0.001), mean hematoma density (OR 0.916; 95% CI 0.863–0.973; p = 0.004), age (OR 1.078; 95% CI 1.054–1.103; p < 0.001), and admission Glasgow Coma Scale (GCS) score (OR 0.777; 95% CI 0.708–0.853; p < 0.001). Among the seven models constructed, the tri-combined model (R1 + R2+Clinical) demonstrated the most stable and relatively better performance across all datasets, with an AUC of 0.791 (95% CI: 0.716–0.867) in the external test set. This model exhibited good calibration and favorable statistical net benefit on DCA.

Conclusion

The integrated prognostic model combining hematoma and perilesional radiomic features with clinical data provides stable and incremental prognostic value for 90-day functional outcomes in patients with ICH.