title: Novel Use of Proteomic Profiles in a Convex-Hull Ensemble Classifier to Predict Gynecological Cancer Patients' Susceptibility to Gastrointestinal Mucositis as Side Effect of Radiation Therapy.
abstract: BACKGROUND
  Whole-pelvis radiation therapy is common practice in the post-surgical treatment of cervical and endometrial cancer. Gastrointestinal mucositis is an adverse side effect of radiation therapy, and is a primary concern in patient management. We investigate whether proteomic information obtained from blood samples drawn from patients scheduled to receive radiation therapy for gynecological cancers could be used to predict which patients are most susceptible to radiation-induced gastrointestinal mucositis, in order to improve the individualization of radiation therapy.
  
  
  METHODS
  We use 132 proteins measured on 17 gynecological cancer patients in a convex-hull-based, selective-voting ensemble classifier to classify each patient into one of two classes: patients who would not (class 1) or would (class 2) develop gastrointestinal mucositis. We employ 20 repetitions of 10-fold cross-validation to measure classification accuracy.
  
  
  RESULTS
  We achieved a 95% confidence interval on average prediction accuracy of (0.711, 0.771) using pre-radiation proteomic profiles to predict which patients would experience gastrointestinal mucositis. Pathway analysis of the 12 most prominent proteins indicated that they could be assembled into a single interaction network with direct associations. The function associated with the highest number of these 12 proteins was cell-to-cell signaling and interaction.
  
  
  CONCLUSIONS
  Pre-radiation proteomic profiles have the potential to classify cervical/endometrial cancer patients with high accuracy as to their susceptibility to gastrointestinal mucositis following radiation therapy. Further study of the network of 12 identified proteins is warranted with a larger patient sample to confirm that these proteins are predictive of gastrointestinal mucositis in this patient population.
authors.affiliation: Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
authors.forename: Ralph L
authors.initials: RL
authors.lastname: Kodell
authors.affiliation: Department of Pharmaceutical Sciences, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA ; Central Arkansas Veterans Healthcare System, Little Rock, AR 72205, USA.
authors.forename: Randy S
authors.initials: RS
authors.lastname: Haun
authors.affiliation: Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
authors.forename: Eric R
authors.initials: ER
authors.lastname: Siegel
authors.affiliation: Department of Applied Mathematics and Computer Science, Philander Smith College, Little Rock, AR 72202, USA.
authors.forename: Chuanlei
authors.initials: C
authors.lastname: Zhang
authors.affiliation: Department of Obstetrics and Gynecology, Division of Gynecology Oncology, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
authors.forename: Angela B
authors.initials: AB
authors.lastname: Trammel
authors.affiliation: Department of Pharmaceutical Sciences, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
authors.forename: Martin
authors.initials: M
authors.lastname: Hauer-Jensen
authors.affiliation: Department of Obstetrics and Gynecology, Division of Gynecology Oncology, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
authors.forename: Alexander F
authors.initials: AF
authors.lastname: Burnett
keywords: Cervical; Endometrial; Gastrointestinal mucositis; Prediction; Proteomics; Radiation