Montironi R, Mazzucchelli R, Santinelli A, Hamilton P W, Thompson D, Bartels P H
Institute of Pathological Anatomy and Histopathology, University of Ancona, Italy.
Anal Quant Cytol Histol. 1998 Oct;20(5):424-36.
To apply a distance measure and Bayesian belief network-based methodology to the positive identification of case diagnosis in prostatic neoplasia.
Eight morphologic and cellular features were analyzed in 20 cases of normal prostate, 20 of low grade prostatic intraepithelial neoplasia (PIN), 20 of high grade PIN, 20 of prostatic adenocarcinoma with a cribriform pattern and 20 of prostatic adenocarcinoma with an acinar pattern. The diagnostic distance was evaluated to measure the "extent" to which the feature outcomes of the individual cases differed from the expected profile of outcomes in typical cases of normal prostate, low and high grade PIN, and cribriform and large acinar adenocarcinoma. Belief values were evaluated with a Bayesian belief network (BBN).
A bivariate representation of the cumulative absolute diagnostic distances of all the cases from the prototypes of normal prostate and cribriform adenocarcinoma was made. Three separate groups of cases were observed, corresponding to normal prostate, low grade PIN and cribriform adenocarcinoma. An additional group was formed by the cases of high grade PIN and acinar adenocarcinoma--i.e., there was complete overlap between the diagnostic distance values of cases belonging to these two categories. However, these cases showed differences in clue outcomes. To explore the contribution of such observations to case identification, a bivariate representation of the diagnostic distances from high grade PIN and acinar adenocarcinoma was made. The cases then formed five separate groups corresponding to the five diagnostic categories. When the individual cases were considered, their shortest distance was from the prototype of the category into which they were originally diagnosed. The BBN gave these diagnostic categories the highest belief values.
The combined evaluation of diagnostic distance and belief represents an identification procedure. The numeric value of certainty characterizes individual cases according to the level of progression from PIN toward cancer.
应用一种基于距离度量和贝叶斯信念网络的方法对前列腺肿瘤病例诊断进行阳性识别。
对20例正常前列腺、20例低级别前列腺上皮内瘤变(PIN)、20例高级别PIN、20例筛状型前列腺腺癌和20例腺泡型前列腺腺癌的8种形态学和细胞特征进行分析。评估诊断距离以衡量各个病例的特征结果与正常前列腺、低级别和高级别PIN以及筛状型和大腺泡型腺癌典型病例的预期结果“偏离程度”。使用贝叶斯信念网络(BBN)评估信念值。
绘制了所有病例与正常前列腺和筛状腺癌原型的累积绝对诊断距离的双变量表示图。观察到三组不同的病例,分别对应正常前列腺、低级别PIN和筛状腺癌。高级别PIN和腺泡型腺癌病例形成另一组——即,属于这两类的病例的诊断距离值完全重叠。然而,这些病例在线索结果上存在差异。为了探究这些观察结果对病例识别的贡献,绘制了与高级别PIN和腺泡型腺癌诊断距离的双变量表示图。这些病例随后形成了对应五个诊断类别的五个独立组。当考虑各个病例时,它们最短的距离来自其最初被诊断所属类别的原型。BBN赋予这些诊断类别最高的信念值。
诊断距离和信念的综合评估代表一种识别程序。确定性的数值根据从PIN向癌症进展的程度对个体病例进行表征。