Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks.
BACKGROUND
Clinical data, such as patient history, laboratory analysis, ultrasound parameters--which are the basis of day-to-day clinical decision support--are often underused to guide the clinical management of cancer in the presence of microarray data. We propose a strategy based on Bayesian networks to treat clinical and microarray data on an equal footing. The main advantage of this probabilistic model is that it allows to integrate these data sources in several ways and that it allows to investigate and understand the model structure and parameters. Furthermore using the concept of a Markov Blanket we can identify all the variables that shield off the class variable from the influence of the remaining network. Therefore Bayesian networks automatically perform feature selection by identifying the (in)dependency relationships with the class variable.


RESULTS
We evaluated three methods for integrating clinical and microarray data: decision integration, partial integration and full integration and used them to classify publicly available data on breast cancer patients into a poor and a good prognosis group. The partial integration method is most promising and has an independent test set area under the ROC curve of 0.845. After choosing an operating point the classification performance is better than frequently used indices.
Gevaert
Olivier
O
Department of Electrical Engineering ESAT-SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium. olivier.gevaert@esat.kuleuven.be
De Smet
Frank
F
Timmerman
Dirk
D
Moreau
Yves
Y
De Moor
Bart
B
D001499:Bayes Theorem; D014408:Biomarkers, Tumor / Q000032:analysis / Q000235:genetics / Q000378:metabolism*; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000235:genetics / Q000378:metabolism*; D003198:Computer Simulation; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016015:Logistic Models; D008954:Models, Biological; D009363:Neoplasm Proteins / Q000032:analysis / Q000235:genetics / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D015398:Signal Transduction
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins