title: Modelling Predictors of Molecular Response to Frontline Imatinib for Patients with Chronic Myeloid Leukaemia.
abstract: BACKGROUND
  Treatment of patients with chronic myeloid leukaemia (CML) has become increasingly difficult in recent years due to the variety of treatment options available and challenge deciding on the most appropriate treatment strategy for an individual patient. To facilitate the treatment strategy decision, disease assessment should involve molecular response to initial treatment for an individual patient. Patients predicted not to achieve major molecular response (MMR) at 24 months to frontline imatinib may be better treated with alternative frontline therapies, such as nilotinib or dasatinib. The aims of this study were to i) understand the clinical prediction 'rules' for predicting MMR at 24 months for CML patients treated with imatinib using clinical, molecular, and cell count observations (predictive factors collected at diagnosis and categorised based on available knowledge) and ii) develop a predictive model for CML treatment management. This predictive model was developed, based on CML patients undergoing imatinib therapy enrolled in the TIDEL II clinical trial with an experimentally identified achieving MMR group and non-achieving MMR group, by addressing the challenge as a machine learning problem. The recommended model was validated externally using an independent data set from King Faisal Specialist Hospital and Research Centre, Saudi Arabia.
  
  
  RESULTS
  The common prognostic scores yielded similar sensitivity performance in testing and validation datasets and are therefore good predictors of the positive group. The G-mean and F-score values in our models outperformed the common prognostic scores in testing and validation datasets and are therefore good predictors for both the positive and negative groups. Furthermore, a high PPV above 65% indicated that our models are appropriate for making decisions at diagnosis and pre-therapy. Study limitations include that prior knowledge may change based on varying expert opinions; hence, representing the category boundaries of each predictive factor could dramatically change performance of the models.
authors.affiliation: School of Computer Science, University of Adelaide, Adelaide, South Australia, Australia.|The Department of Computer Science, King AbdulAziz University, Jeddah, Saudi Arabia.
authors.forename: Haneen
authors.identifier: http://orcid.org/0000-0003-4475-4878
authors.initials: H
authors.lastname: Banjar
authors.affiliation: School of Computer Science, University of Adelaide, Adelaide, South Australia, Australia.|Auto-ID Lab, School of Computer Science, University of Adelaide, Adelaide, South Australia, Australia.
authors.forename: Damith
authors.initials: D
authors.lastname: Ranasinghe
authors.affiliation: School of Computer Science, University of Adelaide, Adelaide, South Australia, Australia.
authors.forename: Fred
authors.initials: F
authors.lastname: Brown
authors.affiliation: School of Molecular and Biomedical Science, The University of Adelaide, Adelaide, Australia.
authors.forename: David
authors.initials: D
authors.lastname: Adelson
authors.affiliation: School of Computer Science, University of Adelaide, Adelaide, South Australia, Australia.
authors.forename: Trent
authors.initials: T
authors.lastname: Kroger
authors.affiliation: Cancer Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide. South Australia, Australia.|University of Adelaide, Discipline of Medicine, Adelaide, South Australia, Australia.
authors.forename: Tamara
authors.initials: T
authors.lastname: Leclercq
authors.affiliation: Cancer Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide. South Australia, Australia.|University of Adelaide, Discipline of Medicine, Adelaide, South Australia, Australia.|University of Adelaide, Discipline of Paediatrics, Adelaide, South Australia, Australia.|Centre for Cancer Biology, University of South Australia, Adelaide, South Australia, Australia.|Centre for Personalised Cancer Medicine, University of Adelaide, Adelaide, South Australia, Australia.
authors.forename: Deborah
authors.initials: D
authors.lastname: White
authors.affiliation: Cancer Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide. South Australia, Australia.|University of Adelaide, Discipline of Medicine, Adelaide, South Australia, Australia.|Centre for Cancer Biology, University of South Australia, Adelaide, South Australia, Australia.|Centre for Personalised Cancer Medicine, University of Adelaide, Adelaide, South Australia, Australia.|Haematology Department, SA Pathology, Adelaide, South Australia, Australia.
authors.forename: Timothy
authors.initials: T
authors.lastname: Hughes
authors.affiliation: King Faisal Specialist Hospital and Research Centre, Oncology Center, Riyadh, Saudi Arabia.
authors.forename: Naeem
authors.initials: N
authors.lastname: Chaudhri
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000970:Antineoplastic Agents / Q000627:therapeutic use*; D002452:Cell Count; D015331:Cohort Studies; D000069439:Dasatinib / Q000008:administration & dosage; D005260:Female; D006801:Humans; D000068877:Imatinib Mesylate / Q000627:therapeutic use*; D020128:Inhibitory Concentration 50; D053208:Kaplan-Meier Estimate; D015464:Leukemia, Myelogenous, Chronic, BCR-ABL Positive / Q000188:drug therapy*; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D011743:Pyrimidines / Q000008:administration & dosage; D012529:Saudi Arabia; D016896:Treatment Outcome; D055815:Young Adult
chemical_list: D000970:Antineoplastic Agents; D011743:Pyrimidines; D000068877:Imatinib Mesylate; C498826:nilotinib; D000069439:Dasatinib