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

IF 4.6 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

iScience Pub Date : 2025-06-13 DOI:10.1016/j.isci.2025.112888

Xianjing Zhao , Zhengxiang Zhang , Juntao Shui , Hui Xu , Yulong Yang , Lequn Zhu , Lei Chen , Shixin Chang , Chunzhong Du , Zhenwei Yao , Xiangming Fang , Lei Shi

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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.

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可解释的基于ct的深度学习模型预测血肿扩张包括脑室内出血增长

血肿扩张（HE），包括脑室内出血（IVH）的增长，显著影响脑出血（ICH）患者的预后。本研究旨在开发、验证和解释一个深度学习模型HENet，用于预测HE的三种定义。利用来自三家医院的718名脑出血患者的CT扫描和临床数据，这项多中心回顾性研究的重点是修订的血肿扩张（RHE）定义1和2，以及传统的HE （CHE）。使用两个外部验证集将HENet的性能与2D模型和医生预测进行比较。结果显示，HENet对RHE1、RHE2和CHE预测的AUC值较高，在RHE1和RHE2结果的净重分类指数和综合判别指数上优于医生预测和2D模型。Grad-CAM技术提供了对模型决策过程的可视化洞察。这些发现表明，将HENet纳入临床实践可以提高脑出血病例的预测准确性和患者预后。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

iScience Multidisciplinary-Multidisciplinary

CiteScore

7.20

自引率

1.70%

发文量

1972

审稿时长

6 weeks

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