Short-term time-to-event model of response to treatment following the GIMEMA protocol for Acute Myeloid Leukaemia

Authors:
Paulo J. G. Lisboa;Ian H. Jarman;Terence A. Etchells;Federico Ambrogi;Ilaria Ardoino;Marco Vignetti;Elia Biganzoli
Affiliations:
School of Computing and Mathematical Sciences, Liverpool John Moores, University, Byrom Street, Liverpool L3 3AF, UK, {T.A.Etchells, I.H.Jarman, P.J.Lisboa}@ljmu.ac.uk;School of Computing and Mathematical Sciences, Liverpool John Moores, University, Byrom Street, Liverpool L3 3AF, UK, {T.A.Etchells, I.H.Jarman, P.J.Lisboa}@ljmu.ac.uk;School of Computing and Mathematical Sciences, Liverpool John Moores, University, Byrom Street, Liverpool L3 3AF, UK, {T.A.Etchells, I.H.Jarman, P.J.Lisboa}@ljmu.ac.uk;Universita degli Studi di Milano, 20133 Milano, Italy, elia.biganzoli@unimi.it;Universita degli Studi di Milano, 20133 Milano, Italy, elia.biganzoli@unimi.it;GIMEMA Data Center, University La Sapienza, Rome, Italy, vignetti@bce.uniroma1.it;Universita degli Studi di Milano, 20133 Milano, Italy, elia.biganzoli@unimi.it
Venue:
Proceedings of the 2009 conference on Computational Intelligence and Bioengineering: Essays in Memory of Antonina Starita
Year:
2009

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Abstract

Acute Myeloid Leukaemia (AML) is a serious condition that may require aggressive systemic treatment. As a consequence of this it is important to characterize quantitatively response to treatment, differentiating patients across a range of clinical and laboratory indicators. This study follows the disease progression for a cohort of n=509 patients diagnosed with AML “de novo” and treated according to a strict protocol defined by the “Gruppo Italiano Malattie Ematologiche dell'Adulto” (GIMEMA). This protocol involves an induction therapy with health assessment typically within 60--90 days and three possible outcomes: complete remission (CR), resistance to induction therapy (Res) and induction death (ID). Accordingly, a time-to-event model with competing risks using the framework of partial logistic artificial neural networks with automatic relevance determination (PLANNCR-ARD) is applied. This results show a stratification of the mortality risk following therapy.