Beyond the In-Practice CBC: The Research CBC Parameters-Driven Machine Learning Predictive Modeling for Early Differentiation among Leukemias.
| Title: | Beyond the In-Practice CBC: The Research CBC Parameters-Driven Machine Learning Predictive Modeling for Early Differentiation among Leukemias. |
|---|---|
| Authors: | Haider, Rana Zeeshan1,2 (AUTHOR) zeeshan3335@yahoo.com, Ujjan, Ikram Uddin3 (AUTHOR) ikramujjan1973@yahoo.es, Khan, Najeed Ahmed4 (AUTHOR) najeed@neduet.edu.pk, Urrechaga, Eloisa5 (AUTHOR) eloisamaria.urrechagaigartua@osakidetza.eus, Shamsi, Tahir Sultan2 (AUTHOR) |
| Source: | Diagnostics (2075-4418). Jan2022, Vol. 12 Issue 1, p138. 1p. |
| Subject Terms: | *LEUKEMIA, *BLOOD cell count, *LYMPHOCYTIC leukemia, *MACHINE learning, *CHRONIC lymphocytic leukemia, *ACUTE promyelocytic leukemia |
| Abstract: | 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's 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–oncology 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 "disease fingerprint" shown by these automated potential morphometric items. [ABSTRACT FROM AUTHOR] |
| Copyright of Diagnostics (2075-4418) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Academic Search Complete |
| Full text is not displayed to guests. | Login for full access. |
| FullText | Links: – Type: pdflink Text: Availability: 1 CustomLinks: – Url: https://resolver.ebsco.com/c/xy5jbn/result?sid=EBSCO:a9h&genre=article&issn=20754418&ISBN=&volume=12&issue=1&date=20220101&spage=138&pages=138-138&title=Diagnostics (2075-4418)&atitle=Beyond%20the%20In-Practice%20CBC%3A%20The%20Research%20CBC%20Parameters-Driven%20Machine%20Learning%20Predictive%20Modeling%20for%20Early%20Differentiation%20among%20Leukemias.&aulast=Haider%2C%20Rana%20Zeeshan&id=DOI:10.3390/diagnostics12010138 Name: Full Text Finder (for New FTF UI) (s8985755) Category: fullText Text: Find It @ SCU Libraries MouseOverText: Find It @ SCU Libraries |
|---|---|
| Header | DbId: a9h DbLabel: Academic Search Complete An: 154816685 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Beyond the In-Practice CBC: The Research CBC Parameters-Driven Machine Learning Predictive Modeling for Early Differentiation among Leukemias. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Haider%2C+Rana+Zeeshan%22">Haider, Rana Zeeshan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> zeeshan3335@yahoo.com</i><br /><searchLink fieldCode="AR" term="%22Ujjan%2C+Ikram+Uddin%22">Ujjan, Ikram Uddin</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> ikramujjan1973@yahoo.es</i><br /><searchLink fieldCode="AR" term="%22Khan%2C+Najeed+Ahmed%22">Khan, Najeed Ahmed</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> najeed@neduet.edu.pk</i><br /><searchLink fieldCode="AR" term="%22Urrechaga%2C+Eloisa%22">Urrechaga, Eloisa</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> eloisamaria.urrechagaigartua@osakidetza.eus</i><br /><searchLink fieldCode="AR" term="%22Shamsi%2C+Tahir+Sultan%22">Shamsi, Tahir Sultan</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Diagnostics+%282075-4418%29%22">Diagnostics (2075-4418)</searchLink>. Jan2022, Vol. 12 Issue 1, p138. 1p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22LEUKEMIA%22">LEUKEMIA</searchLink><br />*<searchLink fieldCode="DE" term="%22BLOOD+cell+count%22">BLOOD cell count</searchLink><br />*<searchLink fieldCode="DE" term="%22LYMPHOCYTIC+leukemia%22">LYMPHOCYTIC leukemia</searchLink><br />*<searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink><br />*<searchLink fieldCode="DE" term="%22CHRONIC+lymphocytic+leukemia%22">CHRONIC lymphocytic leukemia</searchLink><br />*<searchLink fieldCode="DE" term="%22ACUTE+promyelocytic+leukemia%22">ACUTE promyelocytic leukemia</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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's 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–oncology 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 "disease fingerprint" shown by these automated potential morphometric items. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Diagnostics (2075-4418) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://login.libproxy.scu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=a9h&AN=154816685 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/diagnostics12010138 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: 138 Subjects: – SubjectFull: LEUKEMIA Type: general – SubjectFull: BLOOD cell count Type: general – SubjectFull: LYMPHOCYTIC leukemia Type: general – SubjectFull: MACHINE learning Type: general – SubjectFull: CHRONIC lymphocytic leukemia Type: general – SubjectFull: ACUTE promyelocytic leukemia Type: general Titles: – TitleFull: Beyond the In-Practice CBC: The Research CBC Parameters-Driven Machine Learning Predictive Modeling for Early Differentiation among Leukemias. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Haider, Rana Zeeshan – PersonEntity: Name: NameFull: Ujjan, Ikram Uddin – PersonEntity: Name: NameFull: Khan, Najeed Ahmed – PersonEntity: Name: NameFull: Urrechaga, Eloisa – PersonEntity: Name: NameFull: Shamsi, Tahir Sultan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 20754418 Numbering: – Type: volume Value: 12 – Type: issue Value: 1 Titles: – TitleFull: Diagnostics (2075-4418) Type: main |
| ResultId | 1 |