相关实验视频

Updated: May 7, 2025

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

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

Published on: October 10, 2018

协作机器学习引导的口腔状细胞癌的整体生存预测

Rasheed Omobolaji Alabi^1,2, Mohammed Elmusrati², Ilmo Leivo³

¹Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.

Acta oto-laryngologica

|December 31, 2024

概括

协作机器学习 (cML) 在预测口腔状细胞癌 (OSCC) 患者的整体存活率 (OS) 中表现有希望. 这种方法可能会导致更可靠的预测和个性化的治疗策略.

科学领域:

在瘤学瘤学.
生物统计学生物统计学
人工智能的人工智能

背景情况:

口腔状细胞癌 (OSCC) 缺乏对整体存活 (OS) 的可靠预测指标.
准确的预后对于OSCC中有效的患者管理和治疗规划至关重要.

研究的目的:

评估协作机器学习 (cML) 在预测OS中OSCC患者的有效性.
为了确定OSCC的显著临床病理预后因素.

主要方法:

利用了来自美国监测,流行病学和最终结果数据库的9439名OSCC患者的数据.
开发并比较了五种机器学习 (ML) 模型:投票组合,堆叠组合,极端梯度增强,光增强和后勤回归.
通过组合三个ML算法形成cML模型,并将其性能与最佳单独的ML模型进行比较.

主要成果:

单个ML模型的准确度在69.1%至70.2%之间.
在时间验证中,cML的性能准确性与最佳个体模型 (投票组合) 相比较.
确定的关键预后因素包括年龄,T阶段,瘤等级,婚姻状况,性别,初级部位,手术,N阶段,放射治疗,种族,化疗和M阶段.

结论:

cML提高了预测可靠性,表明在OSCC预测中转向合作建模.
这种协作方法有可能改善OSCC患者的个性化治疗策略.

关键词:

人工智能的人工智能是人工智能.监测,流行病学和最终结果 (SEER)合作型机器学习是什么口腔癌是口腔癌的一种疾病.总体存活率总体存活率投票组合是一个投票组合.

更多相关视频

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

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

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

Published on: April 22, 2019

相关概念视频

Cancer Survival Analysis

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...