Automated Detection of Measurements and Their Descriptors in Radiology Reports Using a Hybrid Natural Language Processing Algorithm.
Radiological measurements are reported in free text reports, and it is challenging to extract such measures for treatment planning such as lesion summarization and cancer response assessment. The purpose of this work is to develop and evaluate a natural language processing (NLP) pipeline that can ex...
| Publicado en: | Journal of Digital Imaging Vol. 32; no. 4; pp. 544 - 554 |
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| Autores principales: | , , , |
| Formato: | computer program research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137642040&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137642040 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2019 vid: 32 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137642040 137642040 137642040 10.1007/s10278-019-00237-9 137642040 ppf: 544 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated Detection of Measurements and Their Descriptors in Radiology Reports Using a Hybrid Natural Language Processing Algorithm. aug: au: Bozkurt, Selen Alkim, Emel Banerjee, Imon Rubin, Daniel L. affil: Department of Biomedical Data Science, Stanford University School of Medicine, Medical School Office Building (MSOB), Room X-335, MC 5464, 1265 Welch Road, 94305-5479, Stanford, CA, USA sug: subj: Natural Language Processing Algorithms Radiography Reports Automation Software Design Evaluation Human Magnetic Resonance Imaging Tomography, X-Ray Computed Mammography Computer-Aided Design Descriptive Statistics Neoplasms Physiopathology Neoplasms Radiography Professional Practice ab: Radiological measurements are reported in free text reports, and it is challenging to extract such measures for treatment planning such as lesion summarization and cancer response assessment. The purpose of this work is to develop and evaluate a natural language processing (NLP) pipeline that can extract measurements and their core descriptors, such as temporality, anatomical entity, imaging observation, RadLex descriptors, series number, image number, and segment from a wide variety of radiology reports (MR, CT, and mammogram). We created a hybrid NLP pipeline that integrates rule-based feature extraction modules and conditional random field (CRF) model for extraction of the measurements from the radiology reports and links them with clinically relevant features such as anatomical entities or imaging observations. The pipeline was trained on 1117 CT/MR reports, and performance of the system was evaluated on an independent set of 100 expert-annotated CT/MR reports and also tested on 25 mammography reports. The system detected 813 out of 806 measurements in the CT/MR reports; 784 were true positives, 29 were false positives, and 0 were false negatives. Similarly, from the mammography reports, 96% of the measurements with their modifiers were extracted correctly. Our approach could enable the development of computerized applications that can utilize summarized lesion measurements from radiology report of varying modalities and improve practice by tracking the same lesions along multiple radiologic encounters. pubtype: Academic Journal doctype: computer program research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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