Natural Language Processing Algorithm Used for Staging Pulmonary Oncology from Free-Text Radiological Reports: "Including PET-CT and Validation Towards Clinical Use".
Natural language processing (NLP) can be used to process and structure free text, such as (free text) radiological reports. In radiology, it is important that reports are complete and accurate for clinical staging of, for instance, pulmonary oncology. A computed tomography (CT) or positron emission...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 1; pp. 3 - 13 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2024
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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=175966506&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175966506 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2024 vid: 37 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 175966506 175966506 175966506 10.1007/s10278-023-00913-x 175966506 ppf: 3 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Natural Language Processing Algorithm Used for Staging Pulmonary Oncology from Free-Text Radiological Reports: "Including PET-CT and Validation Towards Clinical Use". aug: au: Nobel, J. Martijn Puts, Sander Krdzalic, Jasenko Zegers, Karen M. L. Lobbes, Marc B. I. F. Robben, Simon G. Dekker, André L. A. J. affil: https://ror.org/02jz4aj89 Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Postbox 5800, 6202 AZ, Maastricht, Netherlands sug: subj: Natural Language Processing Algorithms Neoplasm Staging Lung Neoplasms Radiography Reports Positron Emission Tomography Computed Tomography Human Tomography, X-Ray Computed Descriptive Statistics Predictive Validity Machine Learning ab: Natural language processing (NLP) can be used to process and structure free text, such as (free text) radiological reports. In radiology, it is important that reports are complete and accurate for clinical staging of, for instance, pulmonary oncology. A computed tomography (CT) or positron emission tomography (PET)-CT scan is of great importance in tumor staging, and NLP may be of additional value to the radiological report when used in the staging process as it may be able to extract the T and N stage of the 8th tumor–node–metastasis (TNM) classification system. The purpose of this study is to evaluate a new TN algorithm (TN-PET-CT) by adding a layer of metabolic activity to an already existing rule-based NLP algorithm (TN-CT). This new TN-PET-CT algorithm is capable of staging chest CT examinations as well as PET-CT scans. The study design made it possible to perform a subgroup analysis to test the external validation of the prior TN-CT algorithm. For information extraction and matching, pyContextNLP, SpaCy, and regular expressions were used. Overall TN accuracy score of the TN-PET-CT algorithm was 0.73 and 0.62 in the training and validation set (N = 63, N = 100). The external validation of the TN-CT classifier (N = 65) was 0.72. Overall, it is possible to adjust the TN-CT algorithm into a TN-PET-CT algorithm. However, outcomes highly depend on the accuracy of the report, the used vocabulary, and its context to express, for example, uncertainty. This is true for both the adjusted PET-CT algorithm and for the CT algorithm when applied in another hospital. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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