远程转移的自动检测 在西班牙语的放射学报告中提到远程转移
Ricardo Ahumada1, Jocelyn Dunstan2, Matías Rojas3
1Center of Medical Informatics and Telemedicine, Faculty of Medicine, University of Chile, Santiago, Chile.
JCO clinical cancer informatics
|January 9, 2024
概括
这项研究开发了一种模型,可以在癌症报告中自动检测远程转移,从而在分类患者状态方面实现高准确性. 这些发现有助于结构化瘤学数据,以改善治疗和研究.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
背景情况:
- 从电子健康记录中提取癌症转移信息对于患者护理和研究至关重要.
- 非结构化的放射学报告对自动信息提取构成了挑战.
- 准确识别远程转移对于治疗计划和预后至关重要.
研究的目的:
- 开发一种模型,从自由文本成像和核医学报告中提取远程转移的发现.
- 根据远程转移的存在或不存在来分类患者的状态.
- 从西班牙语成像报告中生成结构化的远程转移信息.
主要方法:
- 从前列腺癌,结直肠癌和乳腺癌的PET-CT和CT报告中创建了一个远程转移注释体.
- 使用命名实体识别模型 (双向LSTM-CRF) 来识别转移实体 (M1/M0).
- 根据已识别的实体将整个报告分类为M1 (转移存在) 或M0 (转移不存在).
主要成果:
- 该模型在检测远程转移时获得了0.84的加权平均F1得分.
- 整份报告被分类为F1分数,M0分数为0.92分,M1分数为0.90分.
- 在三种癌症类型 (前列腺,结直肠,乳腺) 中表现出有效性.
结论:
- 开发的模型有效地检测到远程转移的发现,并对报告进行分类.
- 这项工作有助于从非结构化文本生成结构化瘤学数据.
- 标注的语料库,指导方针和代码公开发布给研究界.
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