自然语言处理在直肠癌MRI报告后期结构化中的应用。

IF 2.1 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
W. Liu , L. Cai , Y. Li
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引用次数: 0

摘要

目的:评价一种自然语言处理(NLP)系统从中文直肠癌磁共振成像(MRI)报告的自由格式文本中提取结构化信息。材料与方法:利用2015 - 2021年358份直肠癌MRI报告,构建了一个基于规则的NLP模型,该模型可以提取出直肠癌的11个关键图像特征。以2015年之前的50份报告和2021年之后的50份报告作为测试数据集,参考标准由两名放射科医生人工提取信息确定。比较了2015年前和2021年后数据集图像特征的长度和报告率,以及NLP系统特征提取的准确率、精密度、召回率和F1分。将NLP提取数据所需的时间与放射科医生所需的时间进行比较。结果:与2015年之前的报告相比,2021年之后撰写的报告的诊断印象切片更长。2015年前直肠癌关键影像学特征报告率为36.55%,2021年后报告率为79.82%。NLP对报告中值正确提取的正确率、精密度、召回率和F1分数在2015年前分别为93.82%、95.63%、87.06%和91.15%,在2021年后分别为92.55%、98.53%、94.15%和96.29%。结论:基于规则模式匹配的NLP系统实现了对直肠癌MRI报告的快速、准确的结构化处理。具有结构化模板的MRI报告更适合基于nlp的信息提取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of natural language processing to post-structuring of rectal cancer MRI reports

AIM

To evaluate a natural language processing (NLP) system for extracting structured information from the free-form text of rectal cancer magnetic resonance imaging (MRI) reports written in Chinese.

MATERIALS AND METHODS

A rule-based NLP model that could extract 11 key image features of rectal cancer was constructed using 358 MRI reports of rectal cancer written between 2015 and 2021. Fifty reports written before 2015 and 50 written after 2021 were used as test datasets, and the reference standard was determined by manual extraction of information by two radiologists. The length and reporting rate of image features in pre-2015 and post-2021 datasets, as well as the accuracy, precision, recall, and F1 score of feature extraction by the NLP system, were compared. The time required for the NLP to extract data was compared with that required by the radiologists.

RESULTS

Reports written after 2021 had longer diagnostic impression sections than reports written before 2015. The reporting rate of key imaging features of rectal cancer was 36.55% before 2015 and 79.82% after 2021. The accuracy, precision, recall, and F1 score of NLP for correct extraction of values from reports were 93.82%, 95.63%, 87.06%, and 91.15%, respectively, for pre-2015 reports, and 92.55%, 98.53%, 94.15%, and 96.29%, respectively, for post-2021 reports. NLP generated all the structured information in <1 second.

CONCLUSIONS

The NLP system with rule-based pattern matching achieved rapid and accurate structured processing of rectal cancer MRI reports. MRI reports with structured templates are more suitable for NLP-based extraction of information.

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来源期刊
Clinical radiology
Clinical radiology 医学-核医学
CiteScore
4.70
自引率
3.80%
发文量
528
审稿时长
76 days
期刊介绍: Clinical Radiology is published by Elsevier on behalf of The Royal College of Radiologists. Clinical Radiology is an International Journal bringing you original research, editorials and review articles on all aspects of diagnostic imaging, including: • Computed tomography • Magnetic resonance imaging • Ultrasonography • Digital radiology • Interventional radiology • Radiography • Nuclear medicine Papers on radiological protection, quality assurance, audit in radiology and matters relating to radiological training and education are also included. In addition, each issue contains correspondence, book reviews and notices of forthcoming events.
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