Leveraging Bayesian networks and information theory to learn risk factors for breast cancer metastasis
Autor: | Adam Brufsky, Kahmil Shajihan, Darshan Shetty, Xia Jiang, Alan Wells, Richard E. Neapolitan |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
Oncology
medicine.medical_specialty Interaction Information Theory Breast Neoplasms Information theory lcsh:Computer applications to medicine. Medical informatics Biochemistry Metastasis 03 medical and health sciences 0302 clinical medicine Breast cancer Structural Biology Risk Factors Internal medicine medicine Humans Risk factor Neoplasm Metastasis Molecular Biology lcsh:QH301-705.5 030304 developmental biology Markov blanket 0303 health sciences business.industry Applied Mathematics Methodology Article Bayesian network Bayes Theorem medicine.disease Metastatic breast cancer Markov Chains Computer Science Applications lcsh:Biology (General) Risk factors for breast cancer 030220 oncology & carcinogenesis lcsh:R858-859.7 Female business Algorithms |
Zdroj: | BMC Bioinformatics, Vol 21, Iss 1, Pp 1-17 (2020) BMC Bioinformatics |
ISSN: | 1471-2105 |
Popis: | Background Even though we have established a few risk factors for metastatic breast cancer (MBC) through epidemiologic studies, these risk factors have not proven to be effective in predicting an individual’s risk of developing metastasis. Therefore, identifying critical risk factors for MBC continues to be a major research imperative, and one which can lead to advances in breast cancer clinical care. The objective of this research is to leverage Bayesian Networks (BN) and information theory to identify key risk factors for breast cancer metastasis from data. Methods We develop the Markov Blanket and Interactive risk factor Learner (MBIL) algorithm, which learns single and interactive risk factors having a direct influence on a patient’s outcome. We evaluate the effectiveness of MBIL using simulated datasets, and compare MBIL with the BN learning algorithms Fast Greedy Search (FGS), PC algorithm (PC), and CPC algorithm (CPC). We apply MBIL to learn risk factors for 5 year breast cancer metastasis using a clinical dataset we curated. We evaluate the learned risk factors by consulting with breast cancer experts and literature. We further evaluate the effectiveness of MBIL at learning risk factors for breast cancer metastasis by comparing it to the BN learning algorithms Necessary Path Condition (NPC) and Greedy Equivalent Search (GES). Results The averages of the Jaccard index for the simulated datasets containing 2000 records were 0.705, 0.272, 0.228, and 0.147 for MBIL, FGS, PC, and CPC respectively. MBIL, NPC, and GES all learned that grade and lymph_nodes_positive are direct risk factors for 5 year metastasis. Only MBIL and NPC found that surgical_margins is a direct risk factor. Only NPC found that invasive is a direct risk factor. MBIL learned that HER2 and ER interact to directly affect 5 year metastasis. Neither GES nor NPC learned that HER2 and ER are direct risk factors. Discussion The results involving simulated datasets indicated that MBIL can learn direct risk factors substantially better than standard Bayesian network learning algorithms. An application of MBIL to a real breast cancer dataset identified both single and interactive risk factors that directly influence breast cancer metastasis, which can be investigated further. |
Databáze: | OpenAIRE |
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