{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T09:11:05Z","timestamp":1780045865713,"version":"3.53.1"},"reference-count":18,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"1","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Clin Cancer Inform"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>PURPOSE<\/jats:title>\n                    <jats:p>Chronic lymphocytic leukemia (CLL) treatment paradigms have evolved significantly, yet real-world evidence (RWE) on guideline implementation and patient characteristics remains limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>MATERIALS AND METHODS<\/jats:title>\n                    <jats:p>This multicenter retrospective study leveraged artificial intelligence (AI) to analyze structured and unstructured data from four Belgian hospitals (January 1, 2018-October 31, 2021). Structured data including diagnosis codes, laboratory results, treatment records, and national registries were standardized using the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Unstructured clinical notes and reports were processed using a transformer-based natural language processing (NLP) pipeline. We examined clinical characteristics, diagnostic testing, and treatment patterns among patients with newly diagnosed CLL.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>\n                      Of 22 variable groups analyzed, 50.0% was derived from structured data only, 36.4% from unstructured data only (NLP-extracted), and 13.6% from mixed sources. Five hundred eighty-six patients with CLL were identified, with a median age of 74 years. One hundred seventy-four patients (29.7%) initiated first-line (1L) treatment, and 41 progressed to second-line treatment. Of 1L treated patients, 68.4% had at least one prespecified comorbidity, including 12.1% with significant cardiovascular disease.\n                      <jats:italic toggle=\"yes\">TP53<\/jats:italic>\n                      \/del17p testing was documented in 34.3% of patients before 1L treatment, with aberrations detected in 42.8%. Bruton's tyrosine kinase inhibitors (BTKi; 35.6%) were the most common 1L treatment, followed by chemoimmunotherapy (CIT; 25.9%). CIT use declined (30.6% to 17.5%), whereas BTKi use remained stable (34.2% to 38.1%) between 2018 and 2021.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION<\/jats:title>\n                    <jats:p>This AI-augmented study demonstrates the feasibility and scalability of combining NLP-derived insights with OMOP-standardized structured data to generate reproducible RWE in hematology. Our results highlight an elderly CLL population with significant comorbidities and a shift toward targeted therapies. While treatment patterns aligned with guidelines, data quality depended on source documentation accessibility. Improved integration of molecular testing into electronic health records is essential for enhancing clinical decision making, patient outcomes, and future research.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1200\/cci-25-00159","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T20:00:14Z","timestamp":1773864014000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":1,"title":["Leveraging Digital Technology and Artificial Intelligence to Describe the Real-World Belgian Chronic Lymphocytic Leukemia Patient Population: The BE-CLLEAR Study"],"prefix":"10.1200","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0785-5904","authenticated-orcid":true,"given":"Matthias","family":"Vanderkerken","sequence":"first","affiliation":[{"name":"Division of Hematology and Center for Cell Therapy &amp; Regenerative Medicine, Antwerp University Hospital, Edegem, Belgium"},{"name":"Laboratory of Experimental Hematology, Vaccine &amp; Infectious Disease Institute, Faculty of Medicine and Health Sciences, University of Antwerp, Edegem, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8285-906X","authenticated-orcid":true,"given":"Koen","family":"Van Eygen","sequence":"additional","affiliation":[{"name":"Department of Hematology, AZ Groeninge, Kortrijk, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Veerle","family":"Galle","sequence":"additional","affiliation":[{"name":"Department of Hematology, AZ Maria Middelares, Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9024-863X","authenticated-orcid":true,"given":"Annelies","family":"Verbiest","sequence":"additional","affiliation":[{"name":"Department of Oncology, Multidisciplinary Oncological Center Antwerp, Antwerp University Hospital, Edegem, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ann","family":"Janssens","sequence":"additional","affiliation":[{"name":"Department of Hematology, UZ Leuven Gasthuisberg, Leuven, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Imke","family":"Masuy","sequence":"additional","affiliation":[{"name":"LynxCare Clinical Informatics, Leuven, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0242-7000","authenticated-orcid":true,"given":"Kristof","family":"Theys","sequence":"additional","affiliation":[{"name":"Kintiga Ltd, Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tine","family":"Cuppens","sequence":"additional","affiliation":[{"name":"AstraZeneca Belux, Groot-Bijgaarden, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1117-6709","authenticated-orcid":true,"given":"Katoo","family":"Muylle","sequence":"additional","affiliation":[{"name":"AstraZeneca Belux, Groot-Bijgaarden, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0947-8708","authenticated-orcid":true,"given":"Ann","family":"De Becker","sequence":"additional","affiliation":[{"name":"Department of Hematology, UZ Brussel, Jette, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"233","reference":[{"key":"e_1_3_9_2_2","first-page":"124","article-title":"Is it possible to predict who may never need treatment for B-CLL?","volume":"9","author":"Galle V","year":"2018","unstructured":"Galle V, Vlummens P, Offner F: Is it possible to predict who may never need treatment for B-CLL? Belg J Hematol 9:124-129, 2018","journal-title":"Belg J Hematol"},{"key":"e_1_3_9_3_2","unstructured":"OHDSI: Standardized data: The OMOP common data model. https:\/\/www.ohdsi.org\/data-standardization\/#:\u223c:text=The%20Observational%20Medical%20Outcomes%20Partnership that%20can%20produce%20reliable%20evidence"},{"key":"e_1_3_9_4_2","doi-asserted-by":"publisher","DOI":"10.1002\/ehf2.14381"},{"key":"e_1_3_9_5_2","doi-asserted-by":"publisher","DOI":"10.1111\/jns.12523"},{"key":"e_1_3_9_6_2","doi-asserted-by":"publisher","DOI":"10.1002\/ehf2.14517"},{"key":"e_1_3_9_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arth.2019.07.039"},{"key":"e_1_3_9_8_2","volume-title":"Biomedical LLMs Pretrained on Non-EHR Data Underperform in Multilingual Real-World Settings","author":"Kramchaninova A","year":"2025","unstructured":"Kramchaninova A, Oeste CL, Farokhshad N, et al: Biomedical LLMs Pretrained on Non-EHR Data Underperform in Multilingual Real-World Settings. 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