Expert system support using Bayesian belief networks in the diagnosis of fine needle aspiration biopsy specimens of the breast.

Abstract

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. Atypical 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. (3 Clin Pathol 1994;47:329-336) Fine needle aspiration cytology (FNAC) has been established as a rapid, safe, and cost effective method of diagnosis in breast disease. The success of the technique relies strongly on the ability of the cytologist to identify and characterise cytological changes in the prepared aspirate. This presents a number of problems. Diagnosis is largely based on visual criteria which are subjective and can be misleading in certain instances. The number of visual clues which need to be assessed and the number of options available impose difficulties for assimilating all the relevant diagnostic information in a consistent and reproducible manner. This is also true of other areas of histological and cytological diagnosis. 1Expert systems are computer programs that are designed to store, access, and process knowledge about a particular domain.2 They can therefore provide the perfect framework for storing cytological diagnostic knowledge in a logical, consistent, and reproducible manner and have substantial potential in providing cytopathologists with a means of making a more accurate and consistent diagnosis.Decision making in cytopathology (as in other domains) involves the consideration an...

Metanálise do uso de redes bayesianas no diagnóstico de câncer de mama

“…For the development and modeling of Bayesian networks there are dozens of free tools or demo versions; however, though there are also dozens of Bayesian networks applied to support the diagnosis of breast cancer presented by the scientific literature and included in our systematic review 10,23,24,25 , these are made available by contacting the researchers who developed them and are used in the research centers that developed them.…”

“…Research using this theory to support breast cancer diagnosis began in the 1990s with a study in Ireland that aimed to determine the diagnosis and Bayesian network using information from fine needle aspiration 23 . That study 23 , which was included in our systematic review and published in 1994, was a pioneer in using this computational model, and despite having not used an automated technique for the computation of the Bayesian network, that study reported high accuracy and favorable results that support its use in clinical practice.…”

“…The mean participant age was found in only one study 10 . Two studies were performed in the United States 10,24 , one in Ireland 23 , and another in England 25 .…”

“…The Bayesian network documented by Hamilton et al 23 used 10 cytological characteristics as input information obtained by fine needle aspiration cytology (FNAC), and the conditional probability matrices relating each of the diagnostic clues and their outcomes to diagnosis (benign, malignant) were defined by a cytopathologist. The software for generating these Bayesian networks was developed at the University of Arizona (USA).…”

“…Four primary studies, which included breast lesions from 1,223 women, met the inclusion criteria and were analyzed (as shown in Table 1) 10,23,24,25 . The overall concordance between the eligibility and methodological quality of the studies was 84% (κ = 0.67), indicating good agreement 12 .…”