Onisko Agnieszka, Druzdzel Marek J, Austin R Marshall
Magee-Womens Hospital, Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, 15213.
Faculty of Computer Science, Bialystok University of Technology, Wiejska 45A, Bialystok, 15-351, Poland.
Diagn Cytopathol. 2019 Jan;47(1):41-47. doi: 10.1002/dc.23993. Epub 2018 Nov 19.
In the era of extensive data collection, there is a growing need for a large scale data analysis with tools that can handle many variables in one modeling framework. In this article, we present our recent applications of Bayesian network modeling to pathology informatics.
Bayesian networks (BNs) are probabilistic graphical models that represent domain knowledge and allow investigators to process this knowledge following sound rules of probability theory. BNs can be built based on expert opinion as well as learned from accumulating data sets. BN modeling is now recognized as a suitable approach for knowledge representation and reasoning under uncertainty. Over the last two decades BN have been successfully applied to many studies on medical prognosis and diagnosis.
Based on data and expert knowledge, we have constructed several BN models to assess patient risk for subsequent specific histopathologic diagnoses and their related prognosis in gynecological cytopathology and breast pathology. These models include the Pittsburgh Cervical Cancer Screening Model assessing risk for histopathologic diagnoses of cervical precancer and cervical cancer, modeling of the significance of benign-appearing endometrial cells in Pap tests, diagnostic modeling to determine whether adenocarcinoma in tissue specimens is of endometrial or endocervical origin, and models to assess risk for recurrence of invasive breast carcinoma and ductal carcinoma in situ.
Bayesian network models can be used as powerful and flexible risk assessment tools on large clinical datasets and can quantitatively identify variables that are of greatest significance in predicting specific histopathologic diagnoses and their related prognosis. Resulting BN models are able to provide individualized quantitative risk assessments and prognostication for specific abnormal findings commonly reported in gynecological cytopathology and breast pathology.
在广泛数据收集的时代，越来越需要使用能够在一个建模框架中处理多个变量的工具进行大规模数据分析。在本文中，我们展示了贝叶斯网络建模在病理学信息学中的最新应用。
贝叶斯网络（BNs）是概率图形模型，可表示领域知识，并允许研究人员根据概率论的合理规则处理这些知识。BNs可以基于专家意见构建，也可以从积累的数据集中学习。BN建模现在被认为是一种在不确定性下进行知识表示和推理的合适方法。在过去二十年中，BN已成功应用于许多医学预后和诊断研究。
基于数据和专家知识，我们构建了几个BN模型，以评估妇科细胞病理学和乳腺病理学中患者后续特定组织病理学诊断及其相关预后的风险。这些模型包括评估宫颈癌前病变和宫颈癌组织病理学诊断风险的匹兹堡宫颈癌筛查模型、巴氏试验中看似良性的子宫内膜细胞意义的建模、确定组织标本中腺癌是子宫内膜起源还是宫颈内膜起源的诊断建模，以及评估浸润性乳腺癌和原位导管癌复发风险的模型。
贝叶斯网络模型可以用作大型临床数据集上强大且灵活的风险评估工具，并可以定量识别在预测特定组织病理学诊断及其相关预后中具有最大意义的变量。由此产生的BN模型能够为妇科细胞病理学和乳腺病理学中常见的特定异常发现提供个性化的定量风险评估和预后。
