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用于从自由文本放射学报告分期肺瘤学的自然语言处理算法:"包括PET-CT和临床使用的验证"

J Martijn Nobel^1,2, Sander Puts^3,4, Jasenko Krdzalic⁵

¹Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Postbox 5800, 6202 AZ, Maastricht, Netherlands. martijn.nobel@mumc.nl.

Journal of imaging informatics in medicine

|February 12, 2024

概括

这项研究开发了一种使用自然语言处理 (NLP) 的新算法 (TN-PET-CT),以从放射学报告中改善瘤分期. 改进的算法显示了在肺瘤学中更准确的T和N分期的潜力.

科学领域:

放射学和医学成像学医学成像学
计算语言学计算语言学
在瘤学瘤学.

背景情况:

放射学报告需要准确的临床分期,特别是在肺瘤学.
自然语言处理 (NLP) 可以对自由文本报告进行结构化,有助于瘤分期.
瘤结节转移 (TNM) 分类系统对于癌症分期至关重要.

研究的目的:

评估一种新的TN-PET-CT算法,将代谢活动整合到用于瘤分期的NLP方法中.
评估该算法在测试胸部CT和PET-CT扫描中的能力.
执行先前TN-CT算法的外部验证.

主要方法:

通过增强现有的TN-CTNLP算法以代谢活动数据开发TN-PET-CT算法.
使用pyContextNLP,SpaCy和正则表达式来提取和匹配信息.
进行TN-CT算法的外部验证的子组分析.

主要成果:

TN-PET-CT算法在训练集中实现了0.73的整体TN准确度,在验证集中达到0.62.
外部验证TN-CT分类器的准确度为0.72.
这项研究证明了将TN-CT算法适应TN-PET-CT的可行性.

结论:

可以通过调整TN-CT算法来开发TN-PET-CT算法.
算法性能受到报告准确性,词汇和不确定性的上下文表达的影响.
这些发现适用于调整后的PET-CT算法和CT算法在不同的医院环境中.

关键词:

分类系统的分类系统.这是一个自由文本.机器学习是机器学习.自然语言处理自然语言处理.放射学放射学是一门学科.报告报告报告报告报告

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