Aim-To develop an expert system model for the diagnosis of fine needle aspiration cytology (FNAC) of the breast.
Methods-Knowledge and uncertainty were represented in the form of a Bayesian belief network which permitted the combination of diagnostic evidence in a cumulative manner and provided a final probability for the possible diagnostic outcomes. The network comprised 10 cytological features (evidence nodes), each independently linked to the diagnosis (decision node) by a conditional probability matrix. The system was designed to be interactive in that the cytopathologist entered evidence into the network in the form of likelihood ratios for the outcomes at each evidence node.
Results-The efficiency of the network was tested on a series of 40 breast FNAC specimens. The highest diagnostic probability provided by the network agreed with the cytopathologists' diagnosis in 100% of cases for the assessment of discrete, benign, and malignant aspirates. A typical probably benign cases were given probabilities in favour of a benign diagnosis. Suspicious cases tended to have similar probabilities for both diagnostic outcomes and so, correctly, could not be assigned as benign or malignant. A closer examination of cumulative belief graphs for the diagnostic sequence of each case provided insight into the diagnostic process, and quantitative data which improved the identification of suspicious cases.
Conclusion-The further development of such a system will have three important roles in breast cytodiagnosis: (1) to aid the cytologist in making a more consistent and objective diagnosis; (2) to provide a teaching tool on breast cytological diagnosis for the non-expert; and (3) it is the first stage in the development of a system capable of automated diagnosis through the use of expert system machine vision.
|Number of pages||8|
|Journal||Journal of Clinical Pathology|
|Publication status||Published - Apr 1994|
- FINE NEEDLE ASPIRATION
Successful Commercial Exploitation of Digital Pathology for Cancer Education, Biomarker Discovery and Clinical Diagnosis
Peter Hamilton (Participant), James Diamond (Participant), Yinhai Wang (Participant), Ching-Wei Wang (Participant), Paul Kelly (Participant), Jacqueline James (Participant) & Dean Fennell (Participant)
Impact: Health Impact, Quality of Life Impact