{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T08:31:12Z","timestamp":1768465872719,"version":"3.49.0"},"reference-count":10,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"7","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Clinical Cancer Informatics"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:sec><jats:title>PURPOSE<\/jats:title><jats:p> Treatment of non\u2013muscle-invasive bladder cancer (NMIBC) is guided by risk stratification using clinical and pathologic criteria. This study aimed to develop a natural language processing (NLP) model for identifying patients with high-risk NMIBC retrospectively from unstructured electronic medical records (EMRs) and to apply the model to describe patient and tumor characteristics. <\/jats:p><\/jats:sec><jats:sec><jats:title>METHODS<\/jats:title><jats:p> We used three independent EMR-derived data sets including adult patients with a bladder cancer diagnosis in 2011-2020 for NLP model development and training (n = 140), validation (n = 697), and application for the retrospective cohort analysis (n = 4,402). Deep learning methods were used to train NLP recognition of medical chart terminology to identify seven high-risk NMIBC criteria; model performance was assessed using the F1 score, weighted across features. An algorithm was then used to classify each patient as high-risk NMIBC (yes\/no). Manually reviewed records served as the gold standard. <\/jats:p><\/jats:sec><jats:sec><jats:title>RESULTS<\/jats:title><jats:p> The F1 scores after model training were &gt;0.7 for all but one uncommon feature (prostatic urethral involvement). The highest area under the receiver operating curves (AUC) was observed for Ta (0.897) and T1 (0.897); the lowest AUC was for carcinoma in situ (CIS; 0.617). For high-risk NMIBC classification, positive predictive value was 79.4%, negative predictive value was 93.2%, and false-positive rate was 8.9%. Sensitivity and specificity were 83.7% and 91.1%, respectively. Of 748 patients manually confirmed as having high-risk NMIBC, 196 (26%) had CIS (of whom 19% also had T1 and 23% also had Ta disease); 552 tumors (74%) had no associated CIS. <\/jats:p><\/jats:sec><jats:sec><jats:title>CONCLUSION<\/jats:title><jats:p> The NLP model, combined with a rule-based algorithm, identified high-risk NMIBC with good performance and will enable future work to study real-world treatment patterns and clinical outcomes for high-risk NMIBC. <\/jats:p><\/jats:sec>","DOI":"10.1200\/cci.23.00096","type":"journal-article","created":{"date-parts":[[2023,10,31]],"date-time":"2023-10-31T19:59:17Z","timestamp":1698782357000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":2,"title":["Evaluation of a Natural Language Processing Model to Identify and Characterize Patients in the United States With High-Risk Non\u2013Muscle-Invasive Bladder Cancer"],"prefix":"10.1200","author":[{"given":"Vikram M.","family":"Narayan","sequence":"first","affiliation":[{"name":"Emory University School of Medicine, Grady Memorial Hospital, Atlanta, GA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3756-6038","authenticated-orcid":false,"given":"Despina","family":"Siolas","sequence":"additional","affiliation":[{"name":"Weill Cornell Medical College, New York, NY"},{"name":"Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5420-5317","authenticated-orcid":false,"given":"Eric S.","family":"Meadows","sequence":"additional","affiliation":[{"name":"Ciox Health, Alpharetta, GA"},{"name":"Target RWE, Durham, NC"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8610-8730","authenticated-orcid":false,"given":"Vladimir","family":"Turzhitsky","sequence":"additional","affiliation":[{"name":"Merck & Co Inc, Rahway, NJ"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arthur","family":"Sillah","sequence":"additional","affiliation":[{"name":"Merck & Co Inc, Rahway, NJ"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kentaro","family":"Imai","sequence":"additional","affiliation":[{"name":"Merck & Co Inc, Rahway, NJ"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5604-0704","authenticated-orcid":false,"given":"Andrew J.","family":"McMurry","sequence":"additional","affiliation":[{"name":"Ciox Health, Alpharetta, GA"},{"name":"Boston Children's Hospital, Harvard Medical School, Boston, MA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haojie","family":"Li","sequence":"additional","affiliation":[{"name":"Merck & Co Inc, Rahway, NJ"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"233","reference":[{"key":"b3","first-page":"15","volume":"8","author":"Saginala K","year":"2020","journal-title":"Med Sci (Basel)"},{"key":"b4","doi-asserted-by":"publisher","DOI":"10.1016\/j.juro.2016.06.049"},{"key":"b6","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(22)01188-6"},{"key":"b7","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(16)30512-8"},{"key":"b8","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2022.3050"},{"key":"b9","doi-asserted-by":"publisher","DOI":"10.1097\/JU.0000000000000593"},{"key":"b10","doi-asserted-by":"publisher","DOI":"10.1016\/j.urology.2017.07.056"},{"key":"b11","doi-asserted-by":"publisher","DOI":"10.1200\/CCI.17.00128"},{"key":"b12","doi-asserted-by":"publisher","DOI":"10.1200\/CCI.21.00097"},{"key":"b17","unstructured":"The R Project for Statistical Computing. http:\/\/www.R-project.org\/"}],"container-title":["JCO Clinical Cancer 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