偶然的肺结节:对放射学报告的自然语言处理分析
Emmanuel Grolleau1, Sébastien Couraud2, Emilien Jupin Delevaux3
1University of Lyon, Claude Bernard University, 43 boulevard du 11 Novembre 1918, 69100 Villeurbanne, France; Acute Respiratory Disease and Thoracic Oncology Department, Lyon Sud Hospital, Hospices Civils de Lyon, 165 Chemin du Grand Revoyet, 69495 Oullins-Pierre-Bénite, France.
Respiratory medicine and research
|September 4, 2024
概括
一项研究发现,2%的患者在CT扫描上有偶发性肺结节 (IPN),但只有41%接受了随访. 这凸显了需要更好的IPN管理程序的需要.
科学领域:
- 放射学 放射学是一门学科.
- 肺部病理学 肺部病理学
- 医疗信息学 医疗信息学
背景情况:
- 偶然的肺结节 (IPN) 是胸部CT扫描的常见发现,通常与肺癌查 (LCS) 无关.
- 评估IPN的患病率,随访率 (FUP) 和相关特征对于患者管理至关重要.
- 了解影响FUP的因素可以改善潜在恶性病变的诊断途径.
研究的目的:
- 确定一年内在一个大型医院群体中IPN的发病率.
- 评估已识别的IPN的FUP率.
- 为了确定IPN的FUP的临床和放射学预测因素.
主要方法:
- 开发了一种自然语言处理 (NLP) 工具,用于识别CT扫描报告中的肺结节.
- 使用关键词分析来提取结节的特征.
- 手动审查和电子健康记录的分析证实了FUP和癌症诊断.
主要成果:
- 1,991名 (2%) 患者从101,703个CT转录中鉴定出IPN.
- NLP在结节检测方面表现出99%的准确性.
- 只有41%的患者接受了推的FUP;年龄较大,结节尺寸较大,以及在印象中提及与更高的FUP率有关.
结论:
- 观察到IPN的严重流行,加上低FUP率,表明需要结构化的IPN管理.
- 在研究中,NLP工具显示出对大型临床数据集的有效分析的前景.
- 在随后的癌症病例中,早期的肺癌被诊断为45%.
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