Agnieszka Onisko1,2, Marek J Druzdzel2,3, R Marshall Austin1. 1. Magee-Womens Hospital, Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, 15213. 2. Faculty of Computer Science, Bialystok University of Technology, Wiejska 45A, Bialystok, 15-351, Poland. 3. School of Computing and Information, University of Pittsburgh, 135 N Bellefield Ave, Pittsburgh, Pennsylvania, 15213.
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: Nicholas L Rider; Gina Cahill; Tina Motazedi; Lei Wei; Ashok Kurian; Lenora M Noroski; Filiz O Seeborg; Ivan K Chinn; Kirk Roberts Journal: PLoS One Date: 2021-02-16 Impact factor: 3.240
Authors: Yuwen Chen; Yiziting Zhu; Kunhua Zhong; Zhiyong Yang; Yujie Li; Xin Shu; Dandan Wang; Peng Deng; Xuehong Bai; Jianteng Gu; Kaizhi Lu; Ju Zhang; Lei Zhao; Tao Zhu; Ke Wei; Bin Yi Journal: Front Med (Lausanne) Date: 2022-09-14