{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:00:36Z","timestamp":1754154036596,"version":"3.41.2"},"reference-count":44,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"9","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Clin Cancer Inform"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:sec>\n            <jats:title>PURPOSE<\/jats:title>\n            <jats:p>\n              Despite its routine use to monitor patients with lung cancer (LC), real-world evaluations of the impact of computed tomography (CT) surveillance on overall survival (OS) have been inconsistent. A major confounder is the absence of imaging indications because patients undergo CT scans for purposes beyond surveillance, like symptom evaluations (eg, cough) linked to poor survival. We propose a novel natural language processing model to predict CT imaging indications (surveillance\n              <jats:italic toggle=\"yes\">v<\/jats:italic>\n              others).\n            <\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>METHODS<\/jats:title>\n            <jats:p>We used electronic health records of 585 long-term LC survivors (\u22655 years) at Stanford, followed for up to 22 years. Their 3,362 post\u20135-year CT reports (including 1,672 manually annotated) were used for modeling by integrating structured variables (eg, CT intervals) with key-phrase analysis of radiology reports. Na\u00efve analysis compared OS in patients with CT for any indications (including symptoms) versus those without post\u20135-year CT, as in previous studies. Using model-predicted indications, we conducted exploratory analyses to compare OS between those with post\u20135-year surveillance CT and those without.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>RESULTS<\/jats:title>\n            <jats:p>\n              The model showed high discrimination (AUC, 0.86), with key predictors including a longer interval (\u22656-month) from the previous CT (odds ratios [OR], 5.50;\n              <jats:italic toggle=\"yes\">P<\/jats:italic>\n              &lt; .001) and surveillance-related key phrases (OR, 1.37;\n              <jats:italic toggle=\"yes\">P<\/jats:italic>\n              = .03). Propensity-adjusted survival analysis indicated better OS for patients with any post\u20135-year surveillance CT versus those without (adjusted hazard ratio, 0.60;\n              <jats:italic toggle=\"yes\">P<\/jats:italic>\n              = .016). By contrast, no significant survival difference was found (\n              <jats:italic toggle=\"yes\">P<\/jats:italic>\n              = .53) between patients with any CT versus those without post\u20135-year CT.\n            <\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>CONCLUSION<\/jats:title>\n            <jats:p>Our model abstracted CT indications from real-world data with high discrimination. Exploratory analyses revealed the obscured imaging-OS association when considering indications, highlighting the model's potential for future real-world studies.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1200\/cci-24-00279","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T20:00:10Z","timestamp":1753300810000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":0,"title":["Automatic Abstraction of Computed Tomography Imaging Indication Using Natural Language Processing for Evaluation of Surveillance Patterns in Long-Term Lung Cancer Survivors"],"prefix":"10.1200","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0415-9070","authenticated-orcid":true,"given":"Aparajita","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi, India"},{"name":"Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1315-1433","authenticated-orcid":true,"given":"Eunji","family":"Choi","sequence":"additional","affiliation":[{"name":"Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, CA"},{"name":"Population Health Sciences, Weill Cornell Medical College, New York, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2204-3314","authenticated-orcid":true,"given":"Chloe","family":"Su","sequence":"additional","affiliation":[{"name":"Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, CA"},{"name":"Department of Epidemiology and Population Health, Stanford University, Stanford, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6281-3977","authenticated-orcid":true,"given":"Anna","family":"Graber-Naidich","sequence":"additional","affiliation":[{"name":"Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1591-1897","authenticated-orcid":true,"given":"Solomon","family":"Henry","sequence":"additional","affiliation":[{"name":"Department of Biomedical Data Science, Stanford University, Stanford, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3068-4020","authenticated-orcid":true,"given":"Mina L.","family":"Satoyoshi","sequence":"additional","affiliation":[{"name":"Research Informatics Center, Stanford University, Stanford, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5726-6663","authenticated-orcid":true,"given":"Archana","family":"Bhat","sequence":"additional","affiliation":[{"name":"Research Informatics Center, Stanford University, Stanford, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6175-9470","authenticated-orcid":true,"given":"Allison W.","family":"Kurian","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Population Health, Stanford University, Stanford, CA"},{"name":"Department of Medicine, Stanford University School of Medicine, Stanford, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6616-5970","authenticated-orcid":true,"given":"Su-Ying","family":"Liang","sequence":"additional","affiliation":[{"name":"Palo Alto Medical Foundation Research Institute, Sutter Health, Palo Alto, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3119-9334","authenticated-orcid":true,"given":"Joel","family":"Neal","sequence":"additional","affiliation":[{"name":"Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Gould","sequence":"additional","affiliation":[{"name":"Department of Health Systems Science, Kaiser Permanente Bernard J. 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