{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T19:45:01Z","timestamp":1793216701579,"version":"4.5.0"},"reference-count":14,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2019,2,5]],"date-time":"2019-02-05T00:00:00Z","timestamp":1549324800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Med Imag Rad Onc"],"published-print":{"date-parts":[[2019,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>Natural language processing (<jats:styled-content style=\"fixed-case\">NLP<\/jats:styled-content>) is an emerging tool which has the ability to automate data extraction from large volumes of unstructured text. One of the main described uses of <jats:styled-content style=\"fixed-case\">NLP<\/jats:styled-content> in radiology is cohort building for epidemiological studies. This study aims to assess the accuracy of <jats:styled-content style=\"fixed-case\">NLP<\/jats:styled-content> in identifying a group of patients positive for ureteric stones on Computed Tomography \u2013 Kidneys, Ureter, Bladder (<jats:styled-content style=\"fixed-case\">CT KUB<\/jats:styled-content>) reports.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Retrospective review of all <jats:styled-content style=\"fixed-case\">CT KUB<\/jats:styled-content> reports in a single calendar year. A locally available <jats:styled-content style=\"fixed-case\">NLP<\/jats:styled-content> tool was used to automatically classify the reports based on positivity for ureteric stones. This was validated by manual review and refined to maximize the accuracy of stone detection.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 1874 <jats:styled-content style=\"fixed-case\">CT KUB<\/jats:styled-content> reports were identified. Manual classification of ureteric stone positivity was 36% compared with 27% using <jats:styled-content style=\"fixed-case\">NLP<\/jats:styled-content>. The accuracy of <jats:styled-content style=\"fixed-case\">NLP<\/jats:styled-content> was 85% with a sensitivity of 66% and specificity of 95%. Incorrect classification was due to misspellings, variable syntax, terminology, pluralization and the inability to exclude clinical request details from the search algorithm.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our <jats:styled-content style=\"fixed-case\">NLP<\/jats:styled-content> tool demonstrated high specificity but low sensitivity at identifying <jats:styled-content style=\"fixed-case\">CT KUB<\/jats:styled-content> reports that are positive for ureteric stones. This was attributable to the lack of feature extraction tools tailored for analysing radiology text, incompleteness of the medical lexicon database and heterogeneity of unstructured reports. Improvements in these areas will help improve data extraction accuracy.<\/jats:p><\/jats:sec>","DOI":"10.1111\/1754-9485.12861","type":"journal-article","created":{"date-parts":[[2019,2,5]],"date-time":"2019-02-05T13:58:13Z","timestamp":1549375093000},"page":"307-310","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Natural language processing to identify ureteric stones in radiology reports"],"prefix":"10.1111","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0921-0402","authenticated-orcid":false,"given":"Andrew Yu","family":"Li","sequence":"first","affiliation":[{"name":"Department of Radiology Canterbury District Health Board Christchurch New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikki","family":"Elliot","sequence":"additional","affiliation":[{"name":"The Canterbury Initiative Canterbury District Health Board Christchurch New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,2,5]]},"reference":[{"key":"e_1_2_6_2_1","volume-title":"ACR BI\u2010RADS\u00ae Atlas, Breast Imaging Reporting and Data System","author":"D'Orsi CJ","year":"2013"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1148\/rg.2016150080"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.16142770"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.13122633"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/pds.3418"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1097\/JTO.0b013e31825bd9f5"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2014.08.001"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1177\/1535370213508172"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-011-9425-7"},{"key":"e_1_2_6_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvs.2016.11.031"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ahj.2017.10.013"},{"key":"e_1_2_6_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2012.12.005"},{"key":"e_1_2_6_14_1","unstructured":"SNOMED International.SNOMED International Browser [Internet]. Browser.ihtsdotools.org. 2018 [Cited 7 September 2018.] Available from URL:http:\/\/browser.ihtsdotools.org\/?"},{"key":"e_1_2_6_15_1","first-page":"17","article-title":"Identifying inconsistencies in SNOMED CT problem lists using structural indicators","volume":"2013","author":"Agrawal A","year":"2013","journal-title":"AMIA Annual Symp Proceed"}],"container-title":["Journal of Medical Imaging and Radiation Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/1754-9485.12861","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/1754-9485.12861","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/1754-9485.12861","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,10]],"date-time":"2023-09-10T12:09:54Z","timestamp":1694347794000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/1754-9485.12861"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,5]]},"references-count":14,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,6]]}},"alternative-id":["10.1111\/1754-9485.12861"],"URL":"https:\/\/doi.org\/10.1111\/1754-9485.12861","archive":["Portico"],"relation":{},"ISSN":["1754-9477","1754-9485"],"issn-type":[{"value":"1754-9477","type":"print"},{"value":"1754-9485","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,5]]},"assertion":[{"value":"2018-10-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-01-16","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-02-05","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}