Colorectal Cancer

Annals of Surgical Oncology

, Volume 20, Issue 1, pp 161-174

First online:

Clinical Decision Support and Individualized Prediction of Survival in Colon Cancer: Bayesian Belief Network Model

  • Alexander StojadinovicAffiliated withDepartment of Surgery, Uniformed Services University of the Health SciencesClinical Trials Group, United States Military Cancer InstituteDivision of Surgical Oncology, Department of Surgery, Walter Reed National Military Medical Center Email author 
  • , Anton BilchikAffiliated withClinical Trials Group, United States Military Cancer InstituteDepartment of Medicine, University of California Los AngelesCalifornia Oncology Research Institute
  • , David SmithAffiliated withDivision of Biostatistics, City of Hope
  • , John S. EberhardtAffiliated withDecisionQ Corporation
  • , Elizabeth Ben WardAffiliated withDecisionQ Corporation
  • , Aviram NissanAffiliated withClinical Trials Group, United States Military Cancer InstituteDepartment of Surgery, Rabin Medical Center
  • , Eric K. JohnsonAffiliated withDepartment of Surgery, Uniformed Services University of the Health SciencesClinical Trials Group, United States Military Cancer InstituteDepartment of Surgery, Madigan Army Medical Center
  • , Mladjan ProticAffiliated withClinical Trials Group, United States Military Cancer InstituteUniversity of Novi Sad—Medical Faculty, Clinic of Abdominal, Endocrine and Transplantation Surgery, Clinical Centre of Vojvodina
  • , George E. PeoplesAffiliated withDepartment of Surgery, Uniformed Services University of the Health SciencesClinical Trials Group, United States Military Cancer InstituteDepartment of Surgery, Brooke Army Medical Center
    • , Itzhak AvitalAffiliated withDepartment of Surgery, Uniformed Services University of the Health SciencesBon Secours Cancer Institute
    • , Scott R. SteeleAffiliated withDepartment of Surgery, Uniformed Services University of the Health SciencesClinical Trials Group, United States Military Cancer InstituteDepartment of Surgery, Madigan Army Medical Center

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Abstract

Background

We used a large population-based data set to create a clinical decision support system (CDSS) for real-time estimation of overall survival (OS) among colon cancer (CC) patients. Patients with CC diagnosed between 1969 and 2006 were identified from the Surveillance Epidemiology and End Results (SEER) registry. Low- and high-risk cohorts were defined. The tenfold cross-validation assessed predictive utility of the machine-learned Bayesian belief network (ml-BBN) model for clinical decision support (CDS).

Methods

A data set consisting of 146,248 records was analyzed using ml-BBN models to provide CDS in estimating OS based on prognostic factors at 12-, 24-, 36-, and 60-month post-treatment follow-up.

Results

Independent prognostic factors in the ml-BBN model included age, race; primary tumor histology, grade and location; Number of primaries, AJCC T stage, N stage, and M stage. The ml-BBN model accurately estimated OS with area under the receiver-operating-characteristic curve of 0.85, thereby improving significantly upon existing AJCC stage-specific OS estimates. Significant differences in OS were found between low- and high-risk cohorts (odds ratios for mortality: 17.1, 16.3, 13.9, and 8.8 for 12-, 24-, 36-, and 60-month cohorts, respectively).

Conclusions

A CDSS was developed to provide individualized estimates of survival in CC. This ml-BBN model provides insights as to how disease-specific factors influence outcome. Time-dependent, individualized mortality risk assessments may inform treatment decisions and facilitate clinical trial design.