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01-01-2013 | Colorectal Cancer

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

Authors: Alexander Stojadinovic, MD, Anton Bilchik, MD, PhD, David Smith, PhD, John S. Eberhardt, BA, Elizabeth Ben Ward, MS, Aviram Nissan, MD, Eric K. Johnson, MD, Mladjan Protic, MD, George E. Peoples, MD, Itzhak Avital, MD, Scott R. Steele, MD

Published in: Annals of Surgical Oncology | Issue 1/2013

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.

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Title: Clinical Decision Support and Individualized Prediction of Survival in Colon Cancer: Bayesian Belief Network Model
Authors: Alexander Stojadinovic, MD
Anton Bilchik, MD, PhD
David Smith, PhD
John S. Eberhardt, BA
Elizabeth Ben Ward, MS
Aviram Nissan, MD
Eric K. Johnson, MD
Mladjan Protic, MD
George E. Peoples, MD
Itzhak Avital, MD
Scott R. Steele, MD
Publication date: 01-01-2013
Publisher: Springer-Verlag
Published in: Annals of Surgical Oncology / Issue 1/2013
Print ISSN: 1068-9265
Electronic ISSN: 1534-4681
DOI: https://doi.org/10.1245/s10434-012-2555-4

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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