Wu Te-Chang, Chan Mao-Hsiang, Lin Kuan-Hung, Liu Chung-Feng, Chen Jeon-Hor, Chang Ruey-Feng
Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.
Department of Medical Sciences Industry, Chang Jung Christian University, Tainan, Taiwan.
Neurol Sci. 2025 Sep 16. doi: 10.1007/s10072-025-08497-w.
This study aims to enhance the prognostic prediction of spontaneous intracerebral hemorrhage (sICH) by comparing the accuracy of three models: a CT-based deep learning model, a clinical variable-based machine learning model, and a hybrid model that integrates both approaches. The goal is to evaluate their performance across different outcome thresholds, including poor outcome (mRS 3-6), loss of independence (mRS 4-6), and severe disability or death (mRS 5-6).
A retrospective analysis was conducted on 1,853 sICH patients from a stroke center database (2008-2021). Patients were divided into two datasets: Dataset A (958 patients) for training/testing the clinical and hybrid models, and Dataset B (895 patients) for training the deep learning model. The imaging model used a 3D ResNet-50 architecture with attention modules, while the clinical model incorporated 19 clinical variables. The hybrid model combined clinical data with prediction probability from the imaging model. Performance metrics were compared using the DeLong test.
The hybrid model consistently outperformed the other models across all outcome thresholds. For predicting severe disability and death, loss of independence, and poor outcome, the hybrid model achieved accuracies of 82.6%, 79.5%, 80.6% with AUC values of 0.897, 0.871, 0.0873, respectively. GCS scores and imaging model prediction probability were the most significant predictors.
The hybrid model, combining CT-based deep learning with clinical variables, offers superior prognostic prediction for sICH outcomes. This integrated approach shows promise for improving clinical decision-making, though further validation in prospective studies is needed.
Not applicable because this is a retrospective study, not a clinical trial.
本研究旨在通过比较三种模型的准确性来加强对自发性脑出血（sICH）的预后预测，这三种模型分别是：基于CT的深度学习模型、基于临床变量的机器学习模型以及整合了两种方法的混合模型。目标是评估它们在不同结局阈值下的表现，包括不良结局（改良Rankin量表评分3 - 6分）、失去独立能力（改良Rankin量表评分4 - 6分）以及严重残疾或死亡（改良Rankin量表评分5 - 6分）。
对一家卒中中心数据库（2008 - 2021年）中的1853例sICH患者进行回顾性分析。患者被分为两个数据集：数据集A（958例患者）用于训练/测试临床模型和混合模型，数据集B（895例患者）用于训练深度学习模型。影像模型采用带有注意力模块的3D ResNet - 50架构，而临床模型纳入了19个临床变量。混合模型将临床数据与影像模型的预测概率相结合。使用DeLong检验比较性能指标。
在所有结局阈值下，混合模型始终优于其他模型。对于预测严重残疾和死亡、失去独立能力以及不良结局，混合模型的准确率分别为82.6%、79.5%、80.6%，曲线下面积（AUC）值分别为0.897、0.871、0.873。格拉斯哥昏迷量表（GCS）评分和影像模型预测概率是最显著的预测因素。
将基于CT的深度学习与临床变量相结合的混合模型，为sICH结局提供了卓越的预后预测。这种综合方法显示出改善临床决策的前景，不过仍需要在前瞻性研究中进一步验证。
不适用，因为这是一项回顾性研究，而非临床试验。
