Prediction of etiology and prognosis based on hematoma location of spontaneous intracerebral hemorrhage: a multicenter diagnostic study.
Authors
Affiliations (8)
Affiliations (8)
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
- Department of Radiology, Lu'an Hospital of Anhui Medical University, Lu'an, China.
- Deepwise AI Lab, Beijing Deepwise & League of PhD Technology Co.Ltd, Beijing, China.
- Department of Radiology, Yijishan Hospital of Wannan Medical College, Wuhu, China.
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China. [email protected].
- Farber Institute for Neuroscience, Department of Neurology, Thomas Jefferson University, Philadelphia, United States. [email protected].
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China. [email protected].
Abstract
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. 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. 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). 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.