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Updated: May 21, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

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

Published on: October 10, 2018

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机器学习可以解释头部和部状细胞癌的生存结果

Rasheed Omobolaji Alabi1, Antti A Mäkitie2, Mohammed Elmusrati3

  • 1Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.

International journal of medical informatics
|March 23, 2025
PubMed
概括

机器学习模型准确地预测了头部和部状细胞癌 (HNSCC) 患者的整体存活率. 关键因素包括瘤特征,患者健康状况和生活方式,使个性化治疗策略成为可能.

关键词:
可以解释的可解释性.头部和部状细胞癌 (HNSCC)机器学习 (ML) 是指机器学习.总体生存率 总体生存率

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Intramucosal Inoculation of Squamous Cell Carcinoma Cells in Mice for Tumor Immune Profiling and Treatment Response Assessment07:29

Intramucosal Inoculation of Squamous Cell Carcinoma Cells in Mice for Tumor Immune Profiling and Treatment Response Assessment

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Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

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