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Classification of Incident Types of Hematologic Malignancy Using Discriminant Analysis at Kinshasa University Clinics, DR Congo

Received: 29 November 2018     Accepted: 29 January 2019     Published: 22 July 2019
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Abstract

The objective of this study was to identify important biomarker differences between absence of HM and expected morphopathologic types of HM. A retrospective analysis study of adult patients aged ≥ 20 years was managed by cytologic aspects such as normal myelogram vs. HM types between 2009 and 2015. Out of 105 patients, 63 (60%) experienced incident HM while 42, 14, 18, 10, 10, 6, and 5 patients had normal myelogram, multiple myeloma (MM), acute myeloid leukaemia (AML), myelodysplastic syndromes (MDS), chronic myeloid leukaemia (CML), acute myeloid leukaemia (CLL) and acute lymphoid leukaemia (ALL), respectively. In Discriminant Analysis (DA), only levels of transfusion, Hb, and WCC discriminated significantly (Wilks lambda =0.159; P < 0.0001) the study groups through Function 1 [Eigen value (EV) = 2.591; cumulative variance (CV) = 78, 7% and Canonical correlation (CC) = 0.849], Function 2 (EV = 0.619; CV = 97.5%; CC = 0.618), and Function 3 (EV = 0.081; CV = 100%; CC = 0.274). The highest Mahalanobis distance (Min D Squared = 0.162) was observed between CML and MDS. For early diagnosis, precise medicine, and good practice in hematologic oncology, DA separated CML, MDS, MM, AML, CLL, and ALL from normal myelogram in Congolese patients.

Published in Clinical Medicine Research (Volume 8, Issue 3)
DOI 10.11648/j.cmr.20190803.11
Page(s) 56-62
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Hematologic Malignancy Types, Classification, Statistics, Congolese Patients

References
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Cite This Article
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    Mireille Solange Nkanga Nganga, Benjamin Longo-Mbenza, Fons Verdonck, Branly Kilola Mbunga, Aimé Tshiyamu Mbaya, et al. (2019). Classification of Incident Types of Hematologic Malignancy Using Discriminant Analysis at Kinshasa University Clinics, DR Congo. Clinical Medicine Research, 8(3), 56-62. https://doi.org/10.11648/j.cmr.20190803.11

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    ACS Style

    Mireille Solange Nkanga Nganga; Benjamin Longo-Mbenza; Fons Verdonck; Branly Kilola Mbunga; Aimé Tshiyamu Mbaya, et al. Classification of Incident Types of Hematologic Malignancy Using Discriminant Analysis at Kinshasa University Clinics, DR Congo. Clin. Med. Res. 2019, 8(3), 56-62. doi: 10.11648/j.cmr.20190803.11

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    AMA Style

    Mireille Solange Nkanga Nganga, Benjamin Longo-Mbenza, Fons Verdonck, Branly Kilola Mbunga, Aimé Tshiyamu Mbaya, et al. Classification of Incident Types of Hematologic Malignancy Using Discriminant Analysis at Kinshasa University Clinics, DR Congo. Clin Med Res. 2019;8(3):56-62. doi: 10.11648/j.cmr.20190803.11

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  • @article{10.11648/j.cmr.20190803.11,
      author = {Mireille Solange Nkanga Nganga and Benjamin Longo-Mbenza and Fons Verdonck and Branly Kilola Mbunga and Aimé Tshiyamu Mbaya and Christian Lueme Lokatola and Teke Apalata and Jean-Marie Ntumba Kayembe and Georges Lelo Mvumbi and Blaise Matondo Ma Nzambi Sumbu and Alain Nzonzila Nganga and David Muballe and Cecile Roth Laure Miakassissa Mapapa and Paul Roger Kazadi Beia and Aurore Cecilia Orphée Mbombo Beia and Donatien Nzongola Nkasu Kayembe},
      title = {Classification of Incident Types of Hematologic Malignancy Using Discriminant Analysis at Kinshasa University Clinics, DR Congo},
      journal = {Clinical Medicine Research},
      volume = {8},
      number = {3},
      pages = {56-62},
      doi = {10.11648/j.cmr.20190803.11},
      url = {https://doi.org/10.11648/j.cmr.20190803.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cmr.20190803.11},
      abstract = {The objective of this study was to identify important biomarker differences between absence of HM and expected morphopathologic types of HM. A retrospective analysis study of adult patients aged ≥ 20 years was managed by cytologic aspects such as normal myelogram vs. HM types between 2009 and 2015. Out of 105 patients, 63 (60%) experienced incident HM while 42, 14, 18, 10, 10, 6, and 5 patients had normal myelogram, multiple myeloma (MM), acute myeloid leukaemia (AML), myelodysplastic syndromes (MDS), chronic myeloid leukaemia (CML), acute myeloid leukaemia (CLL) and acute lymphoid leukaemia (ALL), respectively. In Discriminant Analysis (DA), only levels of transfusion, Hb, and WCC discriminated significantly (Wilks lambda =0.159; P < 0.0001) the study groups through Function 1 [Eigen value (EV) = 2.591; cumulative variance (CV) = 78, 7% and Canonical correlation (CC) = 0.849], Function 2 (EV = 0.619; CV = 97.5%; CC = 0.618), and Function 3 (EV = 0.081; CV = 100%; CC = 0.274). The highest Mahalanobis distance (Min D Squared = 0.162) was observed between CML and MDS. For early diagnosis, precise medicine, and good practice in hematologic oncology, DA separated CML, MDS, MM, AML, CLL, and ALL from normal myelogram in Congolese patients.},
     year = {2019}
    }
    

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  • TY  - JOUR
    T1  - Classification of Incident Types of Hematologic Malignancy Using Discriminant Analysis at Kinshasa University Clinics, DR Congo
    AU  - Mireille Solange Nkanga Nganga
    AU  - Benjamin Longo-Mbenza
    AU  - Fons Verdonck
    AU  - Branly Kilola Mbunga
    AU  - Aimé Tshiyamu Mbaya
    AU  - Christian Lueme Lokatola
    AU  - Teke Apalata
    AU  - Jean-Marie Ntumba Kayembe
    AU  - Georges Lelo Mvumbi
    AU  - Blaise Matondo Ma Nzambi Sumbu
    AU  - Alain Nzonzila Nganga
    AU  - David Muballe
    AU  - Cecile Roth Laure Miakassissa Mapapa
    AU  - Paul Roger Kazadi Beia
    AU  - Aurore Cecilia Orphée Mbombo Beia
    AU  - Donatien Nzongola Nkasu Kayembe
    Y1  - 2019/07/22
    PY  - 2019
    N1  - https://doi.org/10.11648/j.cmr.20190803.11
    DO  - 10.11648/j.cmr.20190803.11
    T2  - Clinical Medicine Research
    JF  - Clinical Medicine Research
    JO  - Clinical Medicine Research
    SP  - 56
    EP  - 62
    PB  - Science Publishing Group
    SN  - 2326-9057
    UR  - https://doi.org/10.11648/j.cmr.20190803.11
    AB  - The objective of this study was to identify important biomarker differences between absence of HM and expected morphopathologic types of HM. A retrospective analysis study of adult patients aged ≥ 20 years was managed by cytologic aspects such as normal myelogram vs. HM types between 2009 and 2015. Out of 105 patients, 63 (60%) experienced incident HM while 42, 14, 18, 10, 10, 6, and 5 patients had normal myelogram, multiple myeloma (MM), acute myeloid leukaemia (AML), myelodysplastic syndromes (MDS), chronic myeloid leukaemia (CML), acute myeloid leukaemia (CLL) and acute lymphoid leukaemia (ALL), respectively. In Discriminant Analysis (DA), only levels of transfusion, Hb, and WCC discriminated significantly (Wilks lambda =0.159; P < 0.0001) the study groups through Function 1 [Eigen value (EV) = 2.591; cumulative variance (CV) = 78, 7% and Canonical correlation (CC) = 0.849], Function 2 (EV = 0.619; CV = 97.5%; CC = 0.618), and Function 3 (EV = 0.081; CV = 100%; CC = 0.274). The highest Mahalanobis distance (Min D Squared = 0.162) was observed between CML and MDS. For early diagnosis, precise medicine, and good practice in hematologic oncology, DA separated CML, MDS, MM, AML, CLL, and ALL from normal myelogram in Congolese patients.
    VL  - 8
    IS  - 3
    ER  - 

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Author Information
  • Faculty of Medicine, Department of Medical Biology, University of Kinshasa, Kinshasa, DR Congo

  • Faculty of Medicine, Department of Research, Walter Sisulu University, Mthatha, South Africa

  • UZ Gasthuisberg/ KU Leuven, Department of Education, Leuven, Belgium

  • Public Health School, Department of Nutrition, University of Kinshasa, Faculty of Medicine, Kinshasa, DR Congo

  • Faculty of Medicine, Department of Medical Biology, University of Kinshasa, Kinshasa, DR Congo

  • Faculty of Medicine, Department of Research, Walter Sisulu University, Mthatha, South Africa

  • Faculty of Medicine, Department of Research, Walter Sisulu University, Mthatha, South Africa

  • Faculty of Medicine, Department of Medical Biology, University of Kinshasa, Kinshasa, DR Congo

  • Faculty of Medicine, Department of Medical Biology, University of Kinshasa, Kinshasa, DR Congo

  • Faculty of Medicine, Department of Medical Biology, University of Kinshasa, Kinshasa, DR Congo

  • Saint Joseph Hospital, Department of Reanimation, Kinshasa, DR Congo

  • Faculty of Medicine, Department of Research, Walter Sisulu University, Mthatha, South Africa

  • Service of Pediatry, Department of Neonatology, University Hospital Center of Brazzaville, Brazzaville, Republic of Congo

  • OVD Hospital Centre, Department of Health, Kinshasa, DR Congo

  • Faculty of Medicine, Biomedical School of Medicine, University of Kinshasa, Kinshasa, DR Congo

  • Faculty of Medicine, Department of Medical Biology, University of Kinshasa, Kinshasa, DR Congo

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