{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T00:35:39Z","timestamp":1793147739761,"version":"4.5.0"},"reference-count":36,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T00:00:00Z","timestamp":1641513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>A targeted and timely treatment can be a beneficial tool for patients with hematological emergencies (particularly acute leukemias). The key challenges in the early diagnosis of leukemias and related hematological disorders are their symptom-sharing nature and prolonged turnaround time as well as the expertise needed in reporting confirmatory tests. The present study made use of the potential morphological and immature fraction-related parameters (research items or cell population data) generated during complete blood cell count (CBC), through artificial intelligence (AI)\/machine learning (ML) predictive modeling for early (at the pre-microscopic level) differentiation of various types of leukemias: acute from chronic as well as myeloid from lymphoid. The routine CBC parameters along with research CBC items from a hematology analyzer in the diagnosis of 1577 study subjects with hematological neoplasms were collected. The statistical and data visualization tools, including heat-map and principal component analysis (PCA,) helped in the evaluation of the predictive capacity of research CBC items. Next, research CBC parameter-driven artificial neural network (ANN) predictive modeling was developed to use the hidden trend (disease\u2019s signature) by increasing the auguring accuracy of these potential morphometric parameters in differentiation of leukemias. The classical statistics for routine and research CBC parameters showed that as a whole, all study items are significantly deviated among various types of leukemias (study groups). The CPD parameter-driven heat-map gave clustering (separation) of myeloid from lymphoid leukemias, followed by the segregation (nodding) of the acute from the chronic class of that particular lineage. Furthermore, acute promyelocytic leukemia (APML) was also well individuated from other types of acute myeloid leukemia (AML). The PCA plot guided by research CBC items at notable variance vindicated the aforementioned findings of the CPD-driven heat-map. Through training of ANN predictive modeling, the CPD parameters successfully differentiate the chronic myeloid leukemia (CML), AML, APML, acute lymphoid leukemia (ALL), chronic lymphoid leukemia (CLL), and other related hematological neoplasms with AUC values of 0.937, 0.905, 0.805, 0.829, 0.870, and 0.789, respectively, at an agreeably significant (10.6%) false prediction rate. Overall practical results of using our ANN model were found quite satisfactory with values of 83.1% and 89.4.7% for training and testing datasets, respectively. We proposed that research CBC parameters could potentially be used for early differentiation of leukemias in the hematology\u2013oncology unit. The CPD-driven ANN modeling is a novel practice that substantially strengthens the predictive potential of CPD items, allowing the clinicians to be confident about the typical trend of the \u201cdisease fingerprint\u201d shown by these automated potential morphometric items.<\/jats:p>","DOI":"10.3390\/diagnostics12010138","type":"journal-article","created":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T03:46:27Z","timestamp":1641527187000},"page":"138","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Beyond the In-Practice CBC: The Research CBC Parameters-Driven Machine Learning Predictive Modeling for Early Differentiation among Leukemias"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2287-3895","authenticated-orcid":false,"given":"Rana Zeeshan","family":"Haider","sequence":"first","affiliation":[{"name":"Baqai Institute of Hematology, Baqai Medical University, Karachi 75340, Pakistan"},{"name":"National Institute of Blood Disease (NIBD), Karachi 75300, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ikram Uddin","family":"Ujjan","sequence":"additional","affiliation":[{"name":"Department of Pathology, Liaquat University of Medical and Health Sciences, Jamshoro 76090, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Najeed Ahmed","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, NED University of Engineering and Technology, Karachi 75270, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9610-7392","authenticated-orcid":false,"given":"Eloisa","family":"Urrechaga","sequence":"additional","affiliation":[{"name":"Core Laboratory, Galdakao-Usansolo Hospital, 48960 Galdakao, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tahir Sultan","family":"Shamsi","sequence":"additional","affiliation":[{"name":"National Institute of Blood Disease (NIBD), Karachi 75300, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"80","DOI":"10.7326\/0003-4819-108-1-80","article-title":"Artificial Intelligence in Medical Diagnosis","volume":"108","author":"Szolovits","year":"1988","journal-title":"Ann. Intern. Med."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Farrugia, A., Al-Jumeily, P.D., Al-Jumaily, M., Hussain, A., and Lamb, D. (2013, January 16\u201318). Medical Diagnosis: Are Artificial Intelligence Systems Able to Diagnose the Underlying Causes of Specific Headaches?. Proceedings of the 2013 Sixth International Conference on Developments in eSystems Engineering, Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/DeSE.2013.72"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s12194-017-0394-5","article-title":"Fifty years of computer analysis in chest imaging: Rule-based, machine learning, deep learning","volume":"10","year":"2017","journal-title":"Radiol. Phys. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.media.2016.06.032","article-title":"Machine learning approaches in medical image analysis: From detection to diagnosis","volume":"33","year":"2016","journal-title":"Med. Image Anal."},{"key":"ref_6","first-page":"363","article-title":"The Future of Medical Diagnostics: Large Digitized Databases","volume":"85","author":"Kerr","year":"2012","journal-title":"Yale J. Boil. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.artmed.2011.04.009","article-title":"Modern parameterization and explanation techniques in diagnostic decision support system: A case study in diagnostics of coronary artery disease","volume":"52","author":"Kukar","year":"2011","journal-title":"Artif. Intell. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e75","DOI":"10.1016\/j.cmpb.2010.06.021","article-title":"Image processing and machine learning for fully automated probabilistic evaluation of medical images","volume":"104","author":"Sajn","year":"2011","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"46732","DOI":"10.1038\/srep46732","article-title":"Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach","volume":"7","author":"Yamamoto","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_10","first-page":"43","article-title":"Evidence-Based Laboratory Medicine","volume":"34","author":"Badrick","year":"2013","journal-title":"Clin. Biochem. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1093\/ajcp\/aqw064","article-title":"Using Machine Learning to Predict Laboratory Test Results","volume":"145","author":"Luo","year":"2016","journal-title":"Am. J. Clin. Pathol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1093\/clinchem\/46.8.1215","article-title":"The CBC at the turn of the millennium: An overview","volume":"46","author":"Ward","year":"2000","journal-title":"Clin. Chem."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1002\/jcla.21493","article-title":"Leukocyte Cell Population Analysis from the Coulter Automatic Blood Cell Analyzer DxH800 to Monitor the Effect of G-CSF","volume":"26","author":"Lee","year":"2012","journal-title":"J. Clin. Lab. Anal."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"777","DOI":"10.3109\/00365548.2011.585179","article-title":"Comparison of neutrophil volume distribution width to C-reactive protein and procalcitonin as a proposed new marker of acute infection","volume":"43","author":"Charafeddine","year":"2011","journal-title":"Scand. J. Infect. Dis."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1097\/SHK.0b013e3182454acf","article-title":"Delta neutrophil index: A promising diagnostic and prognostic marker for sepsis","volume":"37","author":"Seok","year":"2012","journal-title":"Shock"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1002\/jcla.21656","article-title":"Novel Parameters of Extended Complete Blood Cell Count under Fluorescence Flow Cytometry in Patients with Sepsis","volume":"28","author":"Arneth","year":"2014","journal-title":"J. Clin. Lab. Anal."},{"key":"ref_17","first-page":"244","article-title":"Extended leukocyte differential count and C-reactive protein in septic patients with liver impairment: Diagnostic approach to evaluate sepsis in intensive care unit","volume":"3","author":"Buoro","year":"2015","journal-title":"Ann. Transl. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"085","DOI":"10.4103\/JLP.JLP_65_17","article-title":"Volume, conductance, and scatter parameters of neoplastic and nonneoplastic lymphocytes using Coulter LH780","volume":"10","author":"Choccalingam","year":"2018","journal-title":"J. Lab. Physicians"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e123","DOI":"10.1111\/ijlh.12372","article-title":"Contribution of the new XN-1000 parameters NEUT-RI and NEUT-WY for managing patients with immature granulocytes","volume":"37","author":"Cornet","year":"2015","journal-title":"Int. J. Lab. Hematol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3211","DOI":"10.1038\/s41598-017-03222-w","article-title":"Changes in novel haematological parameters following thermal injury: A prospective observational cohort study","volume":"7","author":"Dinsdale","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_21","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."},{"key":"ref_22","first-page":"85","article-title":"A new high-throughput screening method for the detection of chronic lymphatic leukemia and myelodysplastic syndrome","volume":"46","author":"Vockenhuber","year":"2008","journal-title":"Clin. Chem. Lab. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1111\/ijlh.12562","article-title":"New parameters on the hematology analyzer XN-10 (SysmexTM) allow to distinguish childhood bacterial and viral infections","volume":"39","author":"Henriot","year":"2017","journal-title":"Int. J. Lab. Hematol."},{"key":"ref_24","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."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"607","DOI":"10.3343\/alm.2016.36.6.607","article-title":"Cell Population Data NE-SFL and MO-WX From Sysmex XN-3000 Can Provide Additional Information for Exclusion of Acute Promyelocytic Leukemia from Other Acute Myeloid Leukemias: A Preliminary Study","volume":"36","author":"Park","year":"2016","journal-title":"Ann. Lab. Med."},{"key":"ref_26","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":"2014","journal-title":"Int. J. Lab. Hematol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1365-2257.2006.00748.x","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","journal-title":"Int. J. Lab. Hematol."},{"key":"ref_28","first-page":"98","article-title":"Automated Neutrophil Morphology and Its Utility in the Assessment of Neutrophil Dysplasia","volume":"13","author":"Miguel","year":"2007","journal-title":"Lab. Hematol. Off. Publ. Int. Soc. Lab. Hematol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"250","DOI":"10.5045\/br.2013.48.4.250","article-title":"Comparison of laboratory characteristics between acute promyelocytic leukemia and other subtypes of acute myeloid leukemia with disseminated intravascular coagulation","volume":"48","author":"Lee","year":"2013","journal-title":"Blood Res."},{"key":"ref_30","first-page":"1875","article-title":"Management of acute promyelocytic leukemia: Recommendations from an expert panel on behalf of the European Leukemia","volume":"113","author":"Sanz","year":"2009","journal-title":"Net. Blood J. Am. Soc. Hematol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1111\/ijlh.12201","article-title":"ICSH guidelines for the evaluation of blood cell analysers including those used for differential leucocyte and reticulocyte counting","volume":"36","author":"Briggs","year":"2014","journal-title":"Int. J. Lab. Hematol."},{"key":"ref_32","unstructured":"CLSI (2007). Reference Leukocyte (WBC) Differential Count (Proportional) and Evaluation of Instrumental Methods, CLSI."},{"key":"ref_33","first-page":"19","article-title":"Determination of acute leukemia lineage with new morphologic parameters available in the complete blood cell count","volume":"44","author":"Yang","year":"2014","journal-title":"Ann. Clin. Lab. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e22679","DOI":"10.1002\/jcla.22679","article-title":"Utility of cell population data (VCS parameters) as a rapid screening tool for Acute Myeloid Leukemia (AML) in resource-constrained laboratories","volume":"33","author":"Virk","year":"2019","journal-title":"J. Clin. Lab. Anal."},{"key":"ref_35","first-page":"1","article-title":"Artificial intelligence based models for screening of hematologic malignancies using cell population data","volume":"10","author":"Firdani","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.tranon.2019.09.009","article-title":"Cell Population Data\u2013Driven Acute Promyelocytic Leukemia Flagging Through Artificial Neural Network Predictive Modeling","volume":"13","author":"Haider","year":"2020","journal-title":"Transl. Oncol."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/12\/1\/138\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:36:37Z","timestamp":1760362597000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/12\/1\/138"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,7]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["diagnostics12010138"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics12010138","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,7]]}}}