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

Paulo J G Lisboa, Ian H. Jarman, Terence A. Etchells, Federico Ambrogi, Ilaria Ardoino, Marco Vignetti, Elia Biganzoli

Research output: Chapter in Book/Report/Conference proceedingChapter


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.

Original languageEnglish
Title of host publicationFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
Number of pages13
ISBN (Print)9781607500100
Publication statusPublished - 2009

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)09226389


  • Acute myeloid leukaemia
  • Cancer
  • Competing risks
  • Neural networks
  • Survival modelling

ASJC Scopus subject areas

  • Artificial Intelligence


Dive into the research topics of 'Short-term time-to-event model of response to treatment following the GIMEMA protocol for Acute Myeloid Leukaemia'. Together they form a unique fingerprint.

Cite this