P. Kronqvist et al., MANAGEMENT OF UNCERTAINTY IN BREAST-CANCER GRADING WITH BAYESIAN BELIEF NETWORKS, Analytical and quantitative cytology and histology, 17(5), 1995, pp. 300-308
OBJECTIVE: To examine the potential of different constructs of Bayesia
n belief networks (BBN) to manage uncertainty in breast cancer grading
. STUDY DESIGN: We developed four networks, two based on bloom-Richard
son's and two on Helpap's grading systems. The function of the network
s was based either on an expert's experience or frequency counts deriv
ed from subjective grading of a large number of samples. The four BBNs
were tested on 20 specimens, and the resulting final beliefs were com
pared with the subjective gradings. RESULTS: The BBNs showed agreement
with the subjective gradings in 60-85% of cases. Different constructs
of BBNs, however, differed in their performance. The mean beliefs in
frequency-based networks were slightly higher than in experience-based
networks. In addition, as compared with the Bloom-Richardson-based ne
tworks, the Helpap-based BBNs resulted in higher maximum beliefs but p
roduced a larger fraction of discrepancies with the subjectively grade
d cases. Depending on the type of network, 65-90% of the BBN grades we
re associated with high beliefs. CONCLUSION: The results suggest that
for reliable results, grading systems with more than three or four var
iables may be necessary. When based on relevant information, BBNs seem
to have the potential to become a valuable method of assisting the pa
thologist in breast cancer grading.