{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T17:30:55Z","timestamp":1774805455538,"version":"3.50.1"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Communications techniology planning and Evaluation grant","award":["2020-0-01361"],"award-info":[{"award-number":["2020-0-01361"]}]},{"name":"asan cancer institute of asan medical center"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Extracting metastatic information from previous radiologic-text reports is important, however, laborious annotations have limited the usability of these texts. We developed a deep-learning model for extracting primary lung cancer sites and metastatic lymph nodes and distant metastasis information from PET-CT reports for determining lung cancer stages.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>PET-CT reports, fully written in English, were acquired from two cohorts of patients with lung cancer who were diagnosed at a tertiary hospital between January 2004 and March 2020. One cohort of 20,466 PET-CT reports was used for training and the validation set, and the other cohort of 4190 PET-CT reports was used for an additional-test set. A pre-processing model (Lung Cancer Spell Checker) was applied to correct the typographical errors, and pseudo-labelling was used for training the model. The deep-learning model was constructed using the Convolutional-Recurrent Neural Network. The performance metrics for the prediction model were accuracy, precision, sensitivity, micro-AUROC, and AUPRC.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>For the extraction of primary lung cancer location, the model showed a micro-AUROC of 0.913 and 0.946 in the validation set and the additional-test set, respectively. For metastatic lymph nodes, the model showed a sensitivity of 0.827 and a specificity of 0.960. In predicting distant metastasis, the model showed a micro-AUROC of 0.944 and 0.950 in the validation and the additional-test set, respectively.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Our deep-learning method could be used for extracting lung cancer stage information from PET-CT reports and may facilitate lung cancer studies by alleviating laborious annotation by clinicians.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-022-01975-7","type":"journal-article","created":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T15:04:46Z","timestamp":1662044686000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Automated extraction of information of lung cancer staging from unstructured reports of PET-CT interpretation: natural language processing with deep-learning"],"prefix":"10.1186","volume":"22","author":[{"given":"Hyung Jun","family":"Park","sequence":"first","affiliation":[]},{"given":"Namu","family":"Park","sequence":"additional","affiliation":[]},{"given":"Jang Ho","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Myeong Geun","family":"Choi","sequence":"additional","affiliation":[]},{"given":"Jin-Sook","family":"Ryu","sequence":"additional","affiliation":[]},{"given":"Min","family":"Song","sequence":"additional","affiliation":[]},{"given":"Chang-Min","family":"Choi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,1]]},"reference":[{"key":"1975_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-021-08132-0","author":"DA Wood","year":"2021","unstructured":"Wood DA, Kafiabadi S, Al Busaidi A, Guilhem EL, Lynch J, Townend MK, et al. 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Also, the ethics committee of Asan Medical Center (approval number 2020-0212) waived the informed consent due to the retrospective observational nature of the study. The clinical data extracted using the ABLE system at Asan Medical Center were indexed by de-identified encrypted patient ID numbers so that the individual patients could not be identified.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"229"}}