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

Mireille Solange Nkanga Nganga; Benjamin Longo-Mbenza; Fons Verdonck; Branly Kilola Mbunga; Aimé Tshiyamu Mbaya; Christian Lueme Lokatola; Teke Apalata; Jean-Marie Ntumba Kayembe; Georges Lelo Mvumbi; Blaise Matondo Ma Nzambi Sumbu; Alain Nzonzila Nganga; David Muballe; Cecile Roth Laure Miakassissa Mapapa; Paul Roger Kazadi Beia; Aurore Cecilia Orphée Mbombo Beia; Donatien Nzongola Nkasu Kayembe

doi:doi:10.11648/j.cmr.20190803.11

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

Mireille Solange Nkanga Nganga, Benjamin Longo-Mbenza, Fons Verdonck, Branly Kilola Mbunga, Aimé Tshiyamu Mbaya, Christian Lueme Lokatola, Teke Apalata, Jean-Marie Ntumba Kayembe, Georges Lelo Mvumbi, Blaise Matondo Ma Nzambi Sumbu, Alain Nzonzila Nganga, David Muballe, Cecile Roth Laure Miakassissa Mapapa, Paul Roger Kazadi Beia, Aurore Cecilia Orphée Mbombo Beia, Donatien Nzongola Nkasu Kayembe

Published in Clinical Medicine Research (Volume 8, Issue 3)

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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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

[1]	Satish Gopal, William A. Wood, Stephanie J. Lee, and al. Meeting the challenge of hematologic malignancies in sub-Saharan Africa. BLOOD, 2012; 119 (22): 5078-5087.
[2]	OMS. Cancer. Aide-mémoire n 297. Mars 2017.
[3]	Pierre Aubry, Bernard-Alex Gaüzère. Les cancers dans les pays en développement Actualités 2017. www.medecinetropicale.com. 16/01/2018.
[4]	Facts 2015-2016 The incidence, prevalence and mortality data in Facts 2015-2016 Cancers Facts and Figures 2016 Atlanta, GA.
[5]	Olaleye O., Ekrikpo U. Epidemiology of Cancers in Sub-Saharan Africa. In: Adedeji O. (eds) Cancer in Sub-Saharan Africa. Springer, 2017; 3-19.
[6]	Schonfeld SJ, Erdmann F, Wiggill T, Singh E, Kellett P, and Babb C, Schüz J. Hematologic malignancies in South Africa-2006: Analysis of data reported to the National Cancer Registry. Cancer Med 2016; 5 (4): 728-738.
[7]	Marshall A. Litchman Battling the hematological Malignancies: The 200 years’ War. The oncologist. 2008; 13 (2): 126-138.
[8]	Mohammad Sorowar Hossain, Mohd S Iqbal, Mohiuddin Ahmed Khan and al. Diagnosed hematological malignancies in Bangladesh - a retrospective analysis of over 5000 cases from 10 specialized hospitals BMC Cancer 2014, 14: 438.
[9]	Mounia Elidrissi Errahhali, Manal Elidrissi Errahhali, Redouane Boulouiz, Meryem Ourzame and Mohammed Bellaoui Distribution and features of hematological malignancies in Easten Morroco: a retrospective multicenter study over 5 years BMC Cancer 2016, 159.
[10]	Makani j, Roberts DJ. Hematology in Africa. Hematol Oncol Clin North Am 2016 Apr; 30 (2): 457-75.
[11]	Abel GA, Klepin HD. Frailty and the management of hematologic malignancies. Blood. 2018; 131 (5): 515-524.
[12]	D A Diallo, L. S Sissoko, Y Sissoko et al. Epidémiologie actuelle des hémopathies malignes dans les services d’hématologie oncologie médicale et de médecine interne de l’hôpital du Point G, Bamako, Mali, Mali Médical 2005 T XX N 4.
[13]	Mashinda KD, Kayembe KP, and Mapatano MA. Prévalence du cancer en République Démocratique du Congo: données anatomopathologiques recueillies aux Cliniques Universitaires de Kinshasa et à l’Hôpital Général de Référence de Kinshasa Ann. Afr. Med. 2012; 6 (4): 1087-1093.
[14]	Hassan M, Abedi-Valugerdi M. Hematologic malignancies in elderly patients. Haematologica. 2014; 99 (7): 1124-7.
[15]	Mufuta N JP, Mbayo K, Kayembe NZ, Gini EK, and Mbuyi M. Cytologie des hémopathies malignes dans deux formations médicales de Kinshasa /DRCongo Ann. Afr. Med, 2013; 6 (4): 1499-1505.
[16]	David P. Steensma. New challenges in evaluating anemia in older persons in the era of molecular testing. ASH Education Book, 2016; 2016 (1): 67-73.
[17]	Dominique Bron, Lionel Ades, Tamas Fulop Valentin Goede, and Reinhar Stauder. Aging and Blood Disorders: New Perspectives, New Challenges. Haematologica April 2015 100: 415-417.
[18]	Liguori I, Russo G, Curcio F, et al. Oxidative stress, aging, and diseases. Clin Interv Aging. 2018; 13: 757-772.
[19]	Angelica de Souza Batista Maia Alves, Fernanda Barbi Bataglia, Luciene de Oliveira Conterno, Rosimeire Segato, and Spencer Luiz Marques Payão. Epidemiological and cytogenetic profiles of patients with hematological malignancies and their relationship with aging. Hematol Transfus Cell Ther. 2018 Jul-Sep; 40 (3): 200–206.
[20]	Ouédraogo SM, Hien F, Bazié W, Millogo A, Drabo YJ Place des Hémopathies Malignes en Service de Médecine Interne du CHU de Souro Sanou (Burkina Faso) Mali Médical 2011 Tome XXVI (3): 17-21.
[21]	Elazar J. Pedhazur. Multiple regression in behavioral research: explanation and prediction. Fort Worth: Harcourt Brace College Publishers, c1997.

Cite This Article

Plain Text BibTeX RIS

APA Style

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download

Author Information

Mireille Solange Nkanga Nganga

Faculty of Medicine, Department of Medical Biology, University of Kinshasa, Kinshasa, DR Congo
Benjamin Longo-Mbenza

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

UZ Gasthuisberg/ KU Leuven, Department of Education, Leuven, Belgium
Branly Kilola Mbunga

Public Health School, Department of Nutrition, University of Kinshasa, Faculty of Medicine, Kinshasa, DR Congo
Aimé Tshiyamu Mbaya

Faculty of Medicine, Department of Medical Biology, University of Kinshasa, Kinshasa, DR Congo
Christian Lueme Lokatola

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

Faculty of Medicine, Department of Research, Walter Sisulu University, Mthatha, South Africa
Jean-Marie Ntumba Kayembe

Faculty of Medicine, Department of Medical Biology, University of Kinshasa, Kinshasa, DR Congo
Georges Lelo Mvumbi

Faculty of Medicine, Department of Medical Biology, University of Kinshasa, Kinshasa, DR Congo
Blaise Matondo Ma Nzambi Sumbu

Faculty of Medicine, Department of Medical Biology, University of Kinshasa, Kinshasa, DR Congo
Alain Nzonzila Nganga

Saint Joseph Hospital, Department of Reanimation, Kinshasa, DR Congo
David Muballe

Faculty of Medicine, Department of Research, Walter Sisulu University, Mthatha, South Africa
Cecile Roth Laure Miakassissa Mapapa

Service of Pediatry, Department of Neonatology, University Hospital Center of Brazzaville, Brazzaville, Republic of Congo
Paul Roger Kazadi Beia

OVD Hospital Centre, Department of Health, Kinshasa, DR Congo
Aurore Cecilia Orphée Mbombo Beia

Faculty of Medicine, Biomedical School of Medicine, University of Kinshasa, Kinshasa, DR Congo
Donatien Nzongola Nkasu Kayembe

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

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download