Reproducibility of Bayesian belief network assessment of breast fine needle aspirates.
OBJECTIVE
To assess the consistency of diagnosis of fine needle aspiration biopsies of breast lesions by three experienced and five less experienced pathologists using conventional means and applying a Bayesian belief network (BBN) to 10 diagnostic features to support diagnostic decision making.


METHODS
Forty fine needle aspiration biopsies, previously assessed by one of the experienced pathologists both conventionally and using a BBN, were assessed by two further experienced pathologists and five less experienced pathologists.


RESULTS
Using the BBN, the experienced pathologists arrived at diagnoses in agreement with an established consensus at a slightly lower rate than by conventional means. The less experienced pathologists arrived at the correct diagnoses no more frequently with the help of the BBN than conventionally.


CONCLUSIONS
As used in this study, the BBN did not help less experienced pathologists to interpret their observations but did not enable less experienced pathologists to identify how their observations differed and affected their diagnoses. The prototype system used in this study has since been upgraded by providing computer graphic displays of the features to be observed so that a more uniform mental image can be held by the participating pathologists. This will be tested with the same study design.
Whimster
W F
WF
Department of Histopathology, King's College School of Medicine, London, U.K.
Hamilton
P W
PW
Anderson
N A
NA
Humphreys
S
S
Boyle
M
M
Sundaresan
M
M
Rainey
A
A
Giles
A
A
Hopster
D
D
Bartels
P H
PH
D001707:Biopsy, Needle; D001940:Breast / Q000473:pathology*; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D016571:Neural Networks, Computer; D017063:Outcome Assessment, Health Care; D015203:Reproducibility of Results; D012984:Software