Explainable CT-based deep learning model for predicting hematoma expansion including intraventricular hemorrhage growth.

Journal: iScience
Published Date: Jun 13, 2025

Abstract

Hematoma expansion (HE), including intraventricular hemorrhage (IVH) growth, significantly affects outcomes in patients with intracerebral hemorrhage (ICH). This study aimed to develop, validate, and interpret a deep learning model, HENet, for predicting three definitions of HE. Using CT scans and clinical data from 718 ICH patients across three hospitals, the multicenter retrospective study focused on revised hematoma expansion (RHE) definitions 1 and 2, and conventional HE (CHE). HENet's performance was compared with 2D models and physician predictions using two external validation sets. Results showed that HENet achieved high AUC values for RHE1, RHE2, and CHE predictions, surpassing physicians' predictions and 2D models in net reclassification index and integrated discrimination index for RHE1 and RHE2 outcomes. The Grad-CAM technique provided visual insights into the model's decision-making process. These findings suggest that integrating HENet into clinical practice could improve prediction accuracy and patient outcomes in ICH cases.

Authors

  • Xianjing Zhao
    Shanghai Institute of Medical Imaging, Shanghai, China.
  • Zhengxiang Zhang
    Yan'an Medical College of Yan'an University, Yan'an 716000, China.
  • Juntao Shui
    Department of Neurology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, China.
  • Hui Xu
    No 202 Hospital of People's Liberation Army, Liaoning 110003, China.
  • Yulong Yang
    Department of Radiology, The First People's Hospital of Hangzhou Lin'an District, Hangzhou, Zhejiang, China.
  • Lequn Zhu
    Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
  • Lei Chen
    Department of Chemistry, Stony Brook University Stony Brook NY USA.
  • Shixin Chang
    Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
  • Chunzhong Du
    Department of Radiology, The First People's Hospital of Hangzhou Lin'an District, Hangzhou, Zhejiang, China.
  • Zhenwei Yao
    Shanghai Institute of Medical Imaging, Shanghai, China. zwyao@fudan.edu.cn.
  • Xiangming Fang
    Imaging Center, Wuxi People's Hospital, Nanjing Medical University, Wuxi, 214000, China. Electronic address: drfxm@163.com.
  • Lei Shi

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals