{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T16:50:10Z","timestamp":1786985410842,"version":"build-2736575974"},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T00:00:00Z","timestamp":1695340800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100019779","name":"Qatar National Library","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100019779","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Blood Reviews"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1016\/j.blre.2023.101134","type":"journal-article","created":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T12:15:47Z","timestamp":1695384947000},"page":"101134","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":22,"special_numbering":"C","title":["Revolutionizing chronic lymphocytic leukemia diagnosis: A deep dive into the diverse applications of machine learning"],"prefix":"10.1016","volume":"62","author":[{"given":"Mohamed","family":"Elhadary","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amgad Mohamed","family":"Elshoeibi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Badr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Basel","family":"Elsayed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar","family":"Metwally","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed Mohamed","family":"Elshoeibi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mervat","family":"Mattar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khalil","family":"Alfarsi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salem","family":"AlShammari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Awni","family":"Alshurafa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Yassin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"5","key":"10.1016\/j.blre.2023.101134_bb0005","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.mayocp.2018.03.002","article-title":"Chronic lymphocytic Leukemia: diagnosis and treatment","volume":"93","author":"Strati","year":"2018","journal-title":"Mayo Clin Proc"},{"issue":"19","key":"10.1016\/j.blre.2023.101134_bb0010","doi-asserted-by":"crossref","first-page":"4358","DOI":"10.1182\/blood-2012-01-404681","article-title":"Monoclonal B-cell lymphocytosis: right track or red herring?","volume":"119","author":"Ghia","year":"2012","journal-title":"Blood"},{"issue":"1","key":"10.1016\/j.blre.2023.101134_bb0015","first-page":"17","article-title":"Cancer statistics, 2023","volume":"73","author":"Siegel","year":"2023","journal-title":"CA Cancer J Clin"},{"issue":"1","key":"10.1016\/j.blre.2023.101134_bb0020","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s12938-021-00973-6","article-title":"The global burden and attributable risk factors of chronic lymphocytic leukemia in 204 countries and territories from 1990 to 2019: analysis based on the global burden of disease study 2019","volume":"21","author":"Yao","year":"2022","journal-title":"Biomed Eng Online"},{"issue":"25","key":"10.1016\/j.blre.2023.101134_bb0025","doi-asserted-by":"crossref","first-page":"2745","DOI":"10.1182\/blood-2017-09-806398","article-title":"iwCLL guidelines for diagnosis, indications for treatment, response assessment, and supportive management of CLL","volume":"131","author":"Hallek","year":"2018","journal-title":"Blood"},{"issue":"11","key":"10.1016\/j.blre.2023.101134_bb0030","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1001\/jama.2023.1946","article-title":"Diagnosis and treatment of chronic lymphocytic leukemia: a review","volume":"329","author":"Shadman","year":"2023","journal-title":"JAMA"},{"issue":"2","key":"10.1016\/j.blre.2023.101134_bb0035","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1182\/blood.V46.2.219.219","article-title":"Clinical staging of chronic lymphocytic leukemia","volume":"46","author":"Rai","year":"1975","journal-title":"Blood"},{"issue":"1","key":"10.1016\/j.blre.2023.101134_bb0040","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1002\/1097-0142(19810701)48:1<198::AID-CNCR2820480131>3.0.CO;2-V","article-title":"A new prognostic classification of chronic lymphocytic leukemia derived from a multivariate survival analysis","volume":"48","author":"Binet","year":"1981","journal-title":"Cancer"},{"key":"10.1016\/j.blre.2023.101134_bb0045","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1182\/asheducation-2009.1.421","article-title":"Predicting clinical outcome in CLL: how and why","author":"Shanafelt","year":"2009","journal-title":"Hematology Am Soc Hematol Educ Program"},{"key":"10.1016\/j.blre.2023.101134_bb0050","doi-asserted-by":"crossref","first-page":"101019","DOI":"10.1016\/j.blre.2022.101019","article-title":"Artificial intelligence in hematological diagnostics: game changer or gadget?","volume":"58","author":"Walter","year":"2023","journal-title":"Blood Rev"},{"key":"10.1016\/j.blre.2023.101134_bb0055","doi-asserted-by":"crossref","first-page":"101102","DOI":"10.1016\/j.blre.2023.101102","article-title":"Artificial intelligence in sickle disease","author":"Elsabagh","year":"2023","journal-title":"Blood Rev"},{"issue":"9","key":"10.1016\/j.blre.2023.101134_bb0060","article-title":"Applications of artificial intelligence in Thalassemia: a comprehensive review","volume":"13","author":"Ferih","year":"2023","journal-title":"Diagnostics (Basel)"},{"issue":"7","key":"10.1016\/j.blre.2023.101134_bb0065","article-title":"Applications of machine learning in chronic myeloid Leukemia","volume":"13","author":"Elhadary","year":"2023","journal-title":"Diagnostics (Basel)"},{"issue":"2","key":"10.1016\/j.blre.2023.101134_bb0070","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1111\/bjh.16915","article-title":"Machine learning and artificial intelligence in haematology","volume":"192","author":"Shouval","year":"2021","journal-title":"Br J Haematol"},{"issue":"6","key":"10.1016\/j.blre.2023.101134_bb0075","article-title":"Applications of artificial intelligence in Philadelphia-Negative myeloproliferative neoplasms","volume":"13","author":"Elsayed","year":"2023","journal-title":"Diagnostics (Basel)"},{"issue":"6","key":"10.1016\/j.blre.2023.101134_bb0080","article-title":"Applications of artificial intelligence in Thrombocytopenia","volume":"13","author":"Elshoeibi","year":"2023","journal-title":"Diagnostics (Basel)"},{"issue":"1","key":"10.1016\/j.blre.2023.101134_bb0085","first-page":"51","article-title":"Differential diagnosis of hematologic and solid tumors using targeted transcriptome and Artificial Intelligence","volume":"193","author":"Zhang","year":"2023"},{"key":"10.1016\/j.blre.2023.101134_bb0090","series-title":"Identification of Six Diagnostic Biomarkers for Chronic Lymphocytic Leukemia Based on Machine Learning Algorithms","first-page":"3652107","volume":"2022","author":"Zhu","year":"2022"},{"issue":"12","key":"10.1016\/j.blre.2023.101134_bb0095","first-page":"1774","article-title":"DNA methylation-based classification of small B-Cell Lymphomas: a proof-of-principle study","volume":"23","author":"Xia","year":"2021"},{"issue":"2","key":"10.1016\/j.blre.2023.101134_bb0100","first-page":"40","article-title":"A review of deep transfer learning and recent advancements","volume":"11","author":"Iman","year":"2023"},{"key":"10.1016\/j.blre.2023.101134_bb0105","series-title":"Automated detection and classification of leukemia on a subject-independent test dataset using deep transfer learning supported by Grad-CAM visualization","volume":"83","author":"Abhishek","year":"2023"},{"issue":"11","key":"10.1016\/j.blre.2023.101134_bb0110","doi-asserted-by":"crossref","first-page":"e903","DOI":"10.1016\/j.clml.2021.06.025","article-title":"Accurate machine-learning-based classification of leukemia from blood smear images","volume":"21","author":"Dese","year":"2021","journal-title":"Clin Lymphoma Myeloma Leuk"},{"issue":"1","key":"10.1016\/j.blre.2023.101134_bb0115","article-title":"Toward leveraging big value from data: chronic lymphocytic leukemia cell classification","volume":"6","author":"Mohammed","year":"2017"},{"issue":"3","key":"10.1016\/j.blre.2023.101134_bb0120","first-page":"443","article-title":"Potential for process improvement of clinical flow cytometry by incorporating real-time automated screening of data to expedite addition of antibody panels","volume":"157","author":"Simonson","year":"2022"},{"issue":"4","key":"10.1016\/j.blre.2023.101134_bb0125","first-page":"597","article-title":"Augmented human intelligence and automated diagnosis in flow cytometry for hematologic malignancies","volume":"155","author":"Ng","year":"2021"},{"issue":"10","key":"10.1016\/j.blre.2023.101134_bb0130","first-page":"1073","article-title":"Hematologist-level classification of mature B-cell neoplasm using deep learning on multiparameter flow cytometry data","volume":"97","author":"Zhao","year":"2020"},{"issue":"1","key":"10.1016\/j.blre.2023.101134_bb0135","first-page":"1","article-title":"Lymphocyte volume and conductivity indices of the haematology analyser Coulter\u00ae GEN.STM in lymphoproliferative disorders and viral diseases","volume":"28","author":"Silva","year":"2006"},{"issue":"2","key":"10.1016\/j.blre.2023.101134_bb0140","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1111\/ijlh.12261","article-title":"Sepsis affects most routine and cell population data (CPD) obtained using the Sysmex XN-2000 blood cell analyzer: neutrophil-related CPD NE-SFL and NE-WY provide useful information for detecting sepsis","volume":"37","author":"Park","year":"2015","journal-title":"Int J Lab Hematol"},{"issue":"3","key":"10.1016\/j.blre.2023.101134_bb0145","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1111\/j.1751-553X.2011.01392.x","article-title":"Evaluation of cell population data on the UniCel DxH 800 coulter cellular analysis system as a screening for viral infection in children","volume":"34","author":"Jung","year":"2012","journal-title":"Int J Lab Hematol"},{"issue":"3","key":"10.1016\/j.blre.2023.101134_bb0150","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1111\/j.1751-553X.2009.01194.x","article-title":"The utility of the Sysmex XE-2100 analyzer\u2019s NEUT-X and NEUT-Y parameters for detecting neutrophil dysplasia in myelodysplastic syndromes","volume":"32","author":"Furundarena","year":"2010","journal-title":"Int J Lab Hematol"},{"issue":"1","key":"10.1016\/j.blre.2023.101134_bb0155","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1515\/CCLM.2008.012","article-title":"A new high-throughput screening method for the detection of chronic lymphatic leukemia and myelodysplastic syndrome","volume":"46","author":"Haschke-Becher","year":"2008","journal-title":"Clin Chem Lab Med"},{"issue":"1","key":"10.1016\/j.blre.2023.101134_bb0160","article-title":"Beyond the in-practice CBC: the research CBC parameters-driven machine learning predictive modeling for early differentiation among leukemias","volume":"12","author":"Haider","year":"2022"},{"issue":"10","key":"10.1016\/j.blre.2023.101134_bb0165","article-title":"Deep learning for the classification of non-Hodgkin Lymphoma on histopathological images","volume":"13","author":"Steinbuss","year":"2021"},{"key":"10.1016\/j.blre.2023.101134_bb0170","series-title":"Lymphoma images analysis using morphological and non-morphological descriptors for classification","first-page":"65","volume":"163","author":"Do Nascimento","year":"2018"},{"issue":"4","key":"10.1016\/j.blre.2023.101134_bb0175","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s10278-017-9983-4","article-title":"Deep learning for brain MRI segmentation: state of the art and future directions","volume":"30","author":"Akkus","year":"2017","journal-title":"J Digit Imaging"},{"issue":"10","key":"10.1016\/j.blre.2023.101134_bb0180","doi-asserted-by":"crossref","first-page":"1502","DOI":"10.1038\/s41379-018-0073-z","article-title":"Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies","volume":"31","author":"Ehteshami Bejnordi","year":"2018","journal-title":"Mod Pathol"},{"issue":"1","key":"10.1016\/j.blre.2023.101134_bb0185","doi-asserted-by":"crossref","first-page":"15415","DOI":"10.1038\/s41598-017-15720-y","article-title":"Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning","volume":"7","author":"Wang","year":"2017","journal-title":"Sci Rep"},{"issue":"5","key":"10.1016\/j.blre.2023.101134_bb0190","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans Med Imaging"},{"key":"10.1016\/j.blre.2023.101134_bb0195","doi-asserted-by":"crossref","first-page":"29","DOI":"10.4103\/2153-3539.186902","article-title":"Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases","volume":"7","author":"Janowczyk","year":"2016","journal-title":"J Pathol Inform"},{"issue":"9","key":"10.1016\/j.blre.2023.101134_bb0200","first-page":"4241","article-title":"Classification of digital pathological images of non-Hodgkin's lymphoma subtypes based on the fusion of transfer learning and principal component analysis","volume":"47","author":"Zhang","year":"2020"},{"issue":"4","key":"10.1016\/j.blre.2023.101134_bb0205","first-page":"949","article-title":"Implementation of a classification strategy of Raman data collected in different clinical conditions: application to the diagnosis of chronic lymphocytic leukemia","volume":"412","author":"F\u00e9r\u00e9","year":"2020"},{"issue":"4","key":"10.1016\/j.blre.2023.101134_bb0210","first-page":"160","article-title":"Repeated double cross validation","volume":"23","author":"Filzmoser","year":"2009"},{"issue":"1","key":"10.1016\/j.blre.2023.101134_bb0215","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1093\/jalm\/jfac108","article-title":"Hematology and machine learning","volume":"8","author":"Obstfeld","year":"2023","journal-title":"J Appl Lab Med"},{"issue":"1","key":"10.1016\/j.blre.2023.101134_bb0220","doi-asserted-by":"crossref","first-page":"13","DOI":"10.2991\/chi.k.201130.001","article-title":"Machine learning applications in the diagnosis of benign and malignant hematological diseases","volume":"3","author":"Muhsen","year":"2021","journal-title":"Clin Hematol Int"},{"issue":"3","key":"10.1016\/j.blre.2023.101134_bb0225","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s11899-020-00575-4","article-title":"Artificial intelligence in hematology: current challenges and opportunities","volume":"15","author":"Radakovich","year":"2020","journal-title":"Curr Hematol Malig Rep"},{"issue":"5","key":"10.1016\/j.blre.2023.101134_bb0230","doi-asserted-by":"crossref","first-page":"e262","DOI":"10.1016\/S1470-2045(19)30149-4","article-title":"Big data and machine learning algorithms for health-care delivery","volume":"20","author":"Ngiam","year":"2019","journal-title":"Lancet Oncol"},{"issue":"7","key":"10.1016\/j.blre.2023.101134_bb0235","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1007\/s00234-023-03152-7","article-title":"Artificial intelligence tools in clinical neuroradiology: essential medico-legal aspects","volume":"65","author":"Hedderich","year":"2023","journal-title":"Neuroradiology"},{"issue":"23","key":"10.1016\/j.blre.2023.101134_bb0240","doi-asserted-by":"crossref","first-page":"6077","DOI":"10.1182\/bloodadvances.2020002997","article-title":"Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects","volume":"4","author":"Eckardt","year":"2020","journal-title":"Blood Adv"}],"container-title":["Blood Reviews"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0268960X23000954?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0268960X23000954?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T20:33:19Z","timestamp":1759437199000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0268960X23000954"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11]]},"references-count":48,"alternative-id":["S0268960X23000954"],"URL":"https:\/\/doi.org\/10.1016\/j.blre.2023.101134","relation":{},"ISSN":["0268-960X"],"issn-type":[{"value":"0268-960X","type":"print"}],"subject":[],"published":{"date-parts":[[2023,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Revolutionizing chronic lymphocytic leukemia diagnosis: A deep dive into the diverse applications of machine learning","name":"articletitle","label":"Article Title"},{"value":"Blood Reviews","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.blre.2023.101134","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"101134"}}