在计算机断层扫描报告中识别偶然的肺结节的自然语言处理:质量控制工具
Rodrigo Basilio1, Alysson Roncally Carvalho1, Rosana Rodrigues1
1D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil.
JCO global oncology
|September 28, 2023
概括
一个自然语言处理 (NLP) 模型在胸部CT报告中准确地检测出偶然的肺结节 (ILN). 这项技术有助于及时召回患者和潜在的早期肺癌诊断,防止错过的随访.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 偶然的肺结节 (ILN) 经常在胸部CT扫描中被发现.
- 准确及时识别ILN对于患者管理和早期疾病检测至关重要.
- 非结构化的放射学报告对自动数据提取构成了挑战.
研究的目的:
- 评估自然语言处理 (NLP) 模型的诊断性能,用于检测非结构化胸部计算机断层扫描 (CT) 报告中的偶然肺结节 (ILN).
- 评估NLP工具在识别ILN时的准确性,敏感性和特异性.
主要方法:
- 对21542个非结构化胸部CT报告进行了回顾性审查,用于NLP模型训练.
- 内部验证使用300个胸部CT扫描与放射科医生参考读数.
- 对来自57家医院的977份胸部CT报告进行了外部验证,胸部放射科医生是黄金标准.
主要成果:
- 在内部和外部验证队列中,NLP工具实现了高性能.
- 内部验证 (n=300):灵敏度为100.0%,特异性为95.9%,准确性为98.0%.
- 外部验证 (n=977):灵敏度为98.4%,特异性为98.6%,准确性为98.6%.
结论:
- 自然语言处理 (NLP) 在从非结构化胸部CT报告中识别偶然的肺结节方面表现出高度准确性.
- NLP模型有助于及时召回患者,并有助于早期肺癌的潜在诊断.
- 这项技术可以帮助防止ILN患者在随访中丢失,从而改善患者的治疗结果.
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