• Roni Shouval, Myriam Labopin, Ori Bondi, Hila Mishan-Shamay, Avichai Shimoni, Fabio Ciceri, Jordi Esteve, Sebastian Giebel, Norbert C Gorin, Christoph Schmid, Emmanuelle Polge, Mahmoud Aljurf, Nicolaus Kroger, Charles Craddock, Andrea Bacigalupo, Jan J Cornelissen, Frederic Baron, Ron Unger, Arnon Nagler, and Mohamad Mohty.
• Roni Shouval, Hila Mishan-Shamay, Avichai Shimoni, and Arnon Nagler, The Chaim Sheba Medical Center, Tel-Hashomer; Roni Shouval, Ori Bondi, and Ron Unger, Bar-Ilan University, Ramat-Gan, Israel; Myriam Labopin, Norbert C. Gorin, Emmanuelle Polge, Arnon Nagler, and Mohamad Mohty, European Group for Blood and Marrow Transplantation; Myriam Labopin and Mohamad Mohty, Sorbonne Universités, Centre de Recherche (CDR) Saint-Antoine; Myriam Labopin and Mohamad Mohty, Institut National de la Santé et de la Recherche Médicale, CDR Saint-Antoine; Myriam Labopin and Mohamad Mohty, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Antoine, Paris, France; Fabio Ciceri, San Raffaele Scientific Institute, Milan; Andrea Bacigalupo, Ospedale San Martino, Genoa, Italy; Jordi Esteve, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain; Sebastian Giebel, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice, Poland; Christoph Schmid, Ludwig-Maximilians-University, Munich; Nicolaus Kroger, University Medical Center Hamburg Eppendorf, Hamburg, Germany; Mahmoud Aljurf, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia; Charles Craddock, Queen Elizabeth Hospital, Birmingham, United Kingdom; Jan J. Cornelissen, Erasmus University Medical Center, Rotterdam, the Netherlands; and Frederic Baron, University of Liège, Liège, Belgium. shouval@gmail.com.
• J. Clin. Oncol. 2015 Oct 1; 33 (28): 3144-51.

PurposeAllogeneic hematopoietic stem-cell transplantation (HSCT) is potentially curative for acute leukemia (AL), but carries considerable risk. Machine learning algorithms, which are part of the data mining (DM) approach, may serve for transplantation-related mortality risk prediction.Patients And MethodsThis work is a retrospective DM study on a cohort of 28,236 adult HSCT recipients from the AL registry of the European Group for Blood and Marrow Transplantation. The primary objective was prediction of overall mortality (OM) at 100 days after HSCT. Secondary objectives were estimation of nonrelapse mortality, leukemia-free survival, and overall survival at 2 years. Donor, recipient, and procedural characteristics were analyzed. The alternating decision tree machine learning algorithm was applied for model development on 70% of the data set and validated on the remaining data.ResultsOM prevalence at day 100 was 13.9% (n=3,936). Of the 20 variables considered, 10 were selected by the model for OM prediction, and several interactions were discovered. By using a logistic transformation function, the crude score was transformed into individual probabilities for 100-day OM (range, 3% to 68%). The model's discrimination for the primary objective performed better than the European Group for Blood and Marrow Transplantation score (area under the receiver operating characteristics curve, 0.701 v 0.646; P<.001). Calibration was excellent. Scores assigned were also predictive of secondary objectives.ConclusionThe alternating decision tree model provides a robust tool for risk evaluation of patients with AL before HSCT, and is available online (http://bioinfo.lnx.biu.ac.il/∼bondi/web1.html). It is presented as a continuous probabilistic score for the prediction of day 100 OM, extending prediction to 2 years. The DM method has proved useful for clinical prediction in HSCT.© 2015 by American Society of Clinical Oncology.

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