基于深度转移学习的自然语言处理系列自由文本计算机断层扫描报告,用于预测胰腺癌患者的生存率
Sunkyu Kim1, Seung-Seob Kim2,3, Eejung Kim4,5
1Department of Computer Science and Engineering, Korea University, Seoul, Korea.
JCO clinical cancer informatics
|August 16, 2024
概括
自然语言处理 (NLP) 模型分析序列计算机断层扫描 (CT) 报告可以预测胰腺癌存活率. 这种方法为临床决策提供了宝贵的见解,仅从放射学报告中提取生存数据.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 胰腺癌生存预测仍然具有挑战性.
- 放射学报告包含了丰富的,非结构化的数据.
- 自然语言处理 (NLP) 提供了从文本中提取预后信息的潜力.
研究的目的:
- 评估序列计算机断层扫描 (CT) 放射学报告对胰腺癌存活率的预测能力.
- 为此,开发和验证基于深度转移学习的NLP模型.
主要方法:
- 追溯NLP模型的训练和测试,使用来自韩国三级医院的串行,自由文本CT报告.
- 对被诊断患有胰腺癌的患者的生存数据的提取.
- 外部验证使用来自美国独立第三级医院的数据.
- 计算一致性指数 (c-index) 和接收器操作特征曲线 (AUROC) 下的面积.
主要成果:
- 在串行CT报告上训练的ClinicalBERT模型实现了0.811的c指数和0.911的AUROC,用于预测整体存活率.
- 该模型在外部测试集上显示了AUROC为0.888的概括性.
- NLP模型显示了超越特定短语的上下文解释能力.
结论:
- 基于深度转移学习的NLP模型利用连续CT报告可以有效预测胰腺癌患者的生存率.
- 开发的模型可以通过直接从放射学报告中提取预后信息来支持临床决策.
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