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作者

李 泽 李哲闻 刘广存

作者单位

山东第一医科大学(山东省医学科学院)研究生部，山东 济南 山东第一医科大学第一附属医院神经外科，山东 济南

刊名

Advances in Clinical Medicine

年份

2026

卷号

Vol.16 No.1

页码

2006-2013

ISSN

2161-8712

关键词

影像组学 动脉瘤性蛛网膜下腔出血 预后预测 机器学习 临床研究 Radiomics Aneurysmal Subarachnoid Hemorrhage Prognosis Prediction Machine Learning Clinical Research

摘要

Aneurysmal subarachnoid hemorrhage poses a significant challenge in neurological clinical practice due to its sudden onset and high disability rate. Accurate prognosis assessment is critical for optimizing treatment strategies and improving patients’ quality of life. In recent years, radiomics, an emerging technology that combines high-throughput imaging feature extraction with advanced machine learning algorithms, has provided novel perspectives and tools for predicting aSAH prognosis. Current...更多

Aneurysmal subarachnoid hemorrhage poses a significant challenge in neurological clinical practice due to its sudden onset and high disability rate. Accurate prognosis assessment is critical for optimizing treatment strategies and improving patients’ quality of life. In recent years, radiomics, an emerging technology that combines high-throughput imaging feature extraction with advanced machine learning algorithms, has provided novel perspectives and tools for predicting aSAH prognosis. Current research primarily focuses on utilizing radiomics to extract multidimensional features from imaging data such as CT and MRI, and constructing machine learning models to achieve precise assessment of patients’ prognostic risks. However, radiomics approaches in the field of aSAH still face multiple challenges, including feature stability, model generalizability, and clinical translation. This article systematically reviews the feature extraction techniques, model construction strategies, and research progress in clinical applications of radiomics, aiming to promote the standardized use of this technology in aSAH prognosis prediction, advance the development of personalized medicine, and provide robust support for future clinical decision-making.收起

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期刊

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