Clinical decision support and individualized prediction of survival in colon cancer: bayesian belief network model.
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.
Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, MD, USA. alexander.stojadinovic@med.navy.mil
Alexander
A
Stojadinovic
Anton
A
Bilchik
David
D
Smith
John S
JS
Eberhardt
Elizabeth Ben
EB
Ward
Aviram
A
Nissan
Eric K
EK
Johnson
Mladjan
M
Protic
George E
GE
Peoples
Itzhak
I
Avital
Scott R
SR
Steele
D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D001499:Bayes Theorem; D003110:Colonic Neoplasms / Q000208:ethnology / Q000401:mortality* / Q000473:pathology*; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D016017:Odds Ratio; D057285:Precision Medicine; D012372:ROC Curve; D016019:Survival Analysis; D013997:Time Factors; D014481:United States / Q000453:epidemiology