TY - JOUR
T1 - Clinical decision support and individualized prediction of survival in colon cancer
T2 - Bayesian belief network model
AU - Stojadinovic, Alexander
AU - Bilchik, Anton
AU - Smith, David
AU - Eberhardt, John S.
AU - Ward, Elizabeth Ben
AU - Nissan, Aviram
AU - Johnson, Eric K.
AU - Protic, Mladjan
AU - Peoples, George E.
AU - Avital, Itzhak
AU - Steele, Scott R.
N1 - Funding Information:
ACKNOWLEDGEMENT This clinical research effort was supported, in part, by the United States Military Cancer Institute, Washington, D.C. and the Henry M. Jackson Foundation, Rockville, MD and RO1 Grant CA090848.
PY - 2013/1/1
Y1 - 2013/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84871805567&partnerID=8YFLogxK
U2 - 10.1245/s10434-012-2555-4
DO - 10.1245/s10434-012-2555-4
M3 - Article
AN - SCOPUS:84871805567
SN - 1068-9265
VL - 20
SP - 161
EP - 174
JO - Annals of Surgical Oncology
JF - Annals of Surgical Oncology
IS - 1
ER -