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Clinical and Translational Oncology

, Volume 17, Issue 8, pp 612–619 | Cite as

On the prediction of Hodgkin lymphoma treatment response

  • E. J. deAndrés-Galiana
  • J. L. Fernández-MartínezEmail author
  • O. Luaces
  • J. J. del Coz
  • R. Fernández
  • J. Solano
  • E. A. Nogués
  • Y. Zanabilli
  • J. M. Alonso
  • A. R. Payer
  • J. M. Vicente
  • J. Medina
  • F. Taboada
  • M. Vargas
  • C. Alarcón
  • M. Morán
  • A. González-Ordóñez
  • M. A. Palicio
  • S. Ortiz
  • C. Chamorro
  • S. Gonzalez
  • A. P. González-Rodríguez
Research Article

Abstract

Purpose

The cure rate in Hodgkin lymphoma is high, but the response along with treatment is still unpredictable and highly variable among patients. Detecting those patients who do not respond to treatment at early stages could bring improvements in their treatment. This research tries to identify the main biological prognostic variables currently gathered at diagnosis and design a simple machine learning methodology to help physicians improve the treatment response assessment.

Methods

We carried out a retrospective analysis of the response to treatment of a cohort of 263 Caucasians who were diagnosed with Hodgkin lymphoma in Asturias (Spain). For that purpose, we used a list of 35 clinical and biological variables that are currently measured at diagnosis before any treatment begins. To establish the list of most discriminatory prognostic variables for treatment response, we designed a machine learning approach based on two different feature selection methods (Fisher’s ratio and maximum percentile distance) and backwards recursive feature elimination using a nearest-neighbor classifier (k-NN). The weights of the k-NN classifier were optimized using different terms of the confusion matrix (true- and false-positive rates) to minimize risk in the decisions.

Results and conclusions

We found that the optimum strategy to predict treatment response in Hodgkin lymphoma consists in solving two different binary classification problems, discriminating first if the patient is in progressive disease; if not, then discerning among complete and partial remission. Serum ferritin turned to be the most discriminatory variable in predicting treatment response, followed by alanine aminotransferase and alkaline phosphatase. The importance of these prognostic variables suggests a close relationship between inflammation, iron overload, liver damage and the extension of the disease.

Keywords

Hodgkin lymphoma Treatment response Machine learning Serum ferritin (SF) Alanine aminotransferase (ALT) Alkaline phosphatase (ALP) 

Notes

Acknowledgments

Enrique J. de Andrés was supported by the Spanish Ministerio de Economía y Competitividad (Grant TIN2011-23558), and the medical analysis was supported by the Fondo de Investigaciones Sanitarias (Instituto Carlos III-Grant PI12/01280). No other financial support has been received to perform this retrospective analysis.

Conflict of interest

None.

Supplementary material

12094_2015_1285_MOESM1_ESM.doc (138 kb)
Supplementary material 1 (DOC 138 kb)
12094_2015_1285_MOESM2_ESM.xls (156 kb)
Supplementary material 2 (XLS 156 kb)
12094_2015_1285_MOESM3_ESM.xls (64 kb)
Supplementary material 3 (XLS 64.5 kb)

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Copyright information

© Federación de Sociedades Españolas de Oncología (FESEO) 2015

Authors and Affiliations

  • E. J. deAndrés-Galiana
    • 1
    • 2
  • J. L. Fernández-Martínez
    • 1
    Email author
  • O. Luaces
    • 2
  • J. J. del Coz
    • 2
  • R. Fernández
    • 3
  • J. Solano
    • 4
  • E. A. Nogués
    • 4
  • Y. Zanabilli
    • 5
  • J. M. Alonso
    • 6
  • A. R. Payer
    • 4
  • J. M. Vicente
    • 7
  • J. Medina
    • 5
  • F. Taboada
    • 8
  • M. Vargas
    • 9
  • C. Alarcón
    • 5
  • M. Morán
    • 5
  • A. González-Ordóñez
    • 5
  • M. A. Palicio
    • 9
  • S. Ortiz
    • 9
  • C. Chamorro
    • 10
  • S. Gonzalez
    • 11
  • A. P. González-Rodríguez
    • 4
  1. 1.Department of MathematicsUniversity of OviedoOviedoSpain
  2. 2.Artificial Intelligence CenterUniversity of OviedoOviedoSpain
  3. 3.Hematology DepartmentHospital de CabueñesGijonSpain
  4. 4.Hematology DepartmentHospital Universitario Central de AsturiasOviedoSpain
  5. 5.Hematology DepartmentHospital San AgustinAvilesSpain
  6. 6.Hematology DepartmentHospital Valle del NalonLangreoSpain
  7. 7.Hematology DepartmentHospital de MieresMieresSpain
  8. 8.Hematology DepartmentHospital Cangas de NarceaCangas de NarceaSpain
  9. 9.Hematology DepartmentHospital de JarrioJarrioSpain
  10. 10.Hematology DepartmentHospital de ArriondasArriondasSpain
  11. 11.Instituto Universitario Oncológico del Principado de Asturias (IUOPA)University of OviedoOviedoSpain

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