Hamilton P W, Anderson N, Bartels P H, Thompson D
Department of Pathology, Queen's University of Belfast, Northern Ireland.
J Clin Pathol. 1994 Apr;47(4):329-36. doi: 10.1136/jcp.47.4.329.
To develop an expert system model for the diagnosis of fine needle aspiration cytology (FNAC) of the breast.
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.
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.
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.
开发一种用于乳腺细针穿刺细胞学检查（FNAC）诊断的专家系统模型。
知识和不确定性以贝叶斯信念网络的形式表示，该网络允许以累积方式组合诊断证据，并为可能的诊断结果提供最终概率。该网络包含10个细胞学特征（证据节点），每个特征通过条件概率矩阵独立地与诊断（决策节点）相连。该系统设计为交互式的，细胞病理学家以每个证据节点结果的似然比形式将证据输入网络。
该网络的效率在一系列40个乳腺FNAC标本上进行了测试。对于离散、良性和恶性穿刺物的评估，网络提供的最高诊断概率在100%的病例中与细胞病理学家的诊断一致。非典型可能良性病例被赋予有利于良性诊断的概率。可疑病例在两种诊断结果上往往具有相似的概率，因此，正确地说，不能被判定为良性或恶性。对每个病例诊断序列的累积信念图进行更仔细的检查，为诊断过程提供了见解，并提供了有助于识别可疑病例的定量数据。
这种系统的进一步开发在乳腺细胞诊断中将发挥三个重要作用：（1）帮助细胞学家做出更一致和客观的诊断；（2）为非专家提供乳腺细胞诊断的教学工具；（3）它是通过使用专家系统机器视觉开发能够自动诊断的系统的第一阶段。
