{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T21:05:22Z","timestamp":1781557522066,"version":"3.54.5"},"reference-count":34,"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":[[2025,6]]},"abstract":"<jats:sec>\n            <jats:title>PURPOSE<\/jats:title>\n            <jats:p>Collecting information on clinical outcomes (recurrence\/progression) from complex treatment courses in non\u2013muscle invasive bladder cancer (NMIBC) is challenging and time-consuming. We developed a deep learning natural language processing model to assess outcomes in patients with NMIBC using vast data from electronic health records (EHRs).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>METHODS<\/jats:title>\n            <jats:p>This retrospective study analyzed data from Japanese adults with NMIBC who started Bacillus Calmette-Gu\u00e9rin (BCG) induction therapy between April 2016 and June 2022. A Bidirectional Encoder Representations from Transformers (BERT) model was trained to classify outcomes, supported by human review for past history records. The model's performance was assessed by precision, recall, and F1 scores. We compared the effectiveness of BCG therapy between completion (patients who completed therapy) and non-completion groups.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>RESULTS<\/jats:title>\n            <jats:p>Of 372 patients studied, 79.3% and 20.7% were in the completion group and the non-completion group, respectively. The final BERT model achieved average F1 scores of 0.91 and 0.98 for time to recurrence (TTR), and 0.74 and 0.94 for time to progression (TTP) before and after human support, respectively. The hazard ratio for TTR in BCG completion versus non-completion groups was 0.40 (95% CI, 0.26 to 0.62) by a multivariate Cox proportional hazard model and 0.41 (95% CI, 0.26 to 0.63) by inverse probability of treatment weighting.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>CONCLUSION<\/jats:title>\n            <jats:p>The developed model could compare the clinical outcomes between treatments in patients with NMIBC using EHRs. Human support, although required, was needed in only 10% documents and was deemed feasible. The model was able to demonstrate the difference in TTR and TTP between BCG completion and non-completion groups.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1200\/cci-24-00249","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T20:00:16Z","timestamp":1751054416000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning Model for Natural Language to Assess Effectiveness of Patients With Non\u2013Muscle Invasive Bladder Cancer Receiving Intravesical Bacillus Calmette-Gu\u00e9rin Therapy"],"prefix":"10.1200","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9503-7356","authenticated-orcid":true,"given":"Makito","family":"Miyake","sequence":"first","affiliation":[{"name":"Department of Urology, Nara Medical University, Kashihara, Nara, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3637-2095","authenticated-orcid":true,"given":"Naohiro","family":"Yonemoto","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Faculty of Medicine, University of Toyama. Toyama-shi, Toyama, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3964-4009","authenticated-orcid":true,"given":"Kanae","family":"Togo","sequence":"additional","affiliation":[{"name":"Access &amp; Value, Pfizer Japan Inc, Shibuya-ku, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0466-5993","authenticated-orcid":true,"given":"Linghua","family":"Xu","sequence":"additional","affiliation":[{"name":"Access &amp; Value, Pfizer Japan Inc, Shibuya-ku, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7655-1517","authenticated-orcid":true,"given":"Tomoyo","family":"Oguri","sequence":"additional","affiliation":[{"name":"Oncology Medical Affairs, Pfizer Japan Inc, Shibuya-ku, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7778-6844","authenticated-orcid":true,"given":"Masayuki","family":"Tanaka","sequence":"additional","affiliation":[{"name":"Oncology Medical Affairs, Pfizer Japan Inc, Shibuya-ku, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2105-3975","authenticated-orcid":true,"given":"Yoshiyuki","family":"Hasegawa","sequence":"additional","affiliation":[{"name":"System Integration Sector, NTT DATA Corporation, Koto-ku, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshinobu","family":"Izawa","sequence":"additional","affiliation":[{"name":"System Integration Sector, NTT DATA Corporation, Koto-ku, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenji","family":"Araki","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University of Miyazaki Hospital, Miyazaki-shi, Miyazaki, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"233","reference":[{"key":"e_1_3_6_2_2","volume-title":"EAU Guidelines on Non-Muscle-Invasive Bladder Cancer","author":"Gontero P","year":"2024","unstructured":"Gontero P, Birtle A, Comp\u00e9rat E, et al: EAU Guidelines on Non-Muscle-Invasive Bladder Cancer, 2024. https:\/\/d56bochluxqnz.cloudfront.net\/documents\/full-guideline\/EAU-Guidelines-on-Non-muscle-Invasive-Bladder-Cancer-2024.pdf"},{"key":"e_1_3_6_3_2","doi-asserted-by":"publisher","DOI":"10.1111\/iju.14281"},{"key":"e_1_3_6_4_2","doi-asserted-by":"publisher","DOI":"10.1097\/JU.0000000000003846"},{"key":"e_1_3_6_5_2","doi-asserted-by":"publisher","DOI":"10.31083\/j.fbl2908295"},{"key":"e_1_3_6_6_2","doi-asserted-by":"publisher","DOI":"10.3390\/cells11030357"},{"key":"e_1_3_6_7_2","doi-asserted-by":"publisher","DOI":"10.3389\/fsurg.2022.912438"},{"key":"e_1_3_6_8_2","doi-asserted-by":"publisher","DOI":"10.1002\/wics.1549"},{"key":"e_1_3_6_9_2","article-title":"Assessment of electronic health record for cancer research and patient care through a scoping review of cancer natural language processing","author":"Wang L","unstructured":"Wang L, Fu S, Wen A, et al: Assessment of electronic health record for cancer research and patient care through a scoping review of cancer natural language processing. 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