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Updated: Jun 28, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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机器学习作为诊断和预后工具,用于预测癌症患者的血栓形成:系统性审查

Adham H El-Sherbini1, Stefania Coroneos1, Ali Zidan1

  • 1Department of Biomedical and Molecular Sciences, School of Medicine, Queen's University, Kingston, Ontario, Canada.

Seminars in thrombosis and hemostasis
|April 11, 2024
PubMed
概括

机器学习模型在预测与癌症相关的血栓形成方面表现有希望,通常表现优于传统的Khorana评分. 在这个系统性审查中,极端梯度增强成为了表现最好的算法.

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Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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