Short-Term Time-to-Event Model of Response to Treatment Following the GIMEMA Protocol for Acute Myeloid Leukemia
Capitolo
Data di Pubblicazione:
2009
Citazione:
Short-Term Time-to-Event Model of Response to Treatment Following the GIMEMA Protocol for Acute Myeloid Leukemia / P.J.G. Lisboa, I.H. Jarman, T.A. Etchells, F. Ambrogi, I. Ardoino, M. Vignetti, E. Biganzoli (FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS). - In: Computational Intelligence and Bioengineering: Essays in Memory of Antonina Starita / [a cura di] F. Masulli, A. Micheli, A. Sperduti. - Amsterdam : IOS Press, 2009. - ISBN 978-1-60750-010-0. - pp. 81-93 [10.3233/978-1-60750-010-0-81]
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.
Tipologia IRIS:
03 - Contributo in volume
Keywords:
Survival modelling; competing risks; acute myeloid leukaemia; cancer; neural networks;
Elenco autori:
P.J.G. Lisboa, I.H. Jarman, T.A. Etchells, F. Ambrogi, I. Ardoino, M. Vignetti, E. Biganzoli
Link alla scheda completa:
Titolo del libro:
Computational Intelligence and Bioengineering: Essays in Memory of Antonina Starita