Publication: Causal probabilistic modeling for malignancy grading in pathology with explanations of dependency to the related histological features
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Date
2007
Authors
Weidl, G. ; Iglesias-Rozas, J.R. ; Roehrl, N.
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Publisher
Murcia : F. Hernández
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DOI
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info:eu-repo/semantics/article
Description
Abstract
This work demonstrates that histological
grading of brain tumors and astrocytomas can be
accurately predicted and causally explained with the
help of causal probabilistic models, also known as
Bayesian networks (BN). Although created statistically,
this allows individual identification of the grade of
malignancy as an internal cause that has enabled the
development of the histological features to their
observed state. The BN models are built from data
representing 794 cases of astrocytomas with their
malignant grading and corresponding histological
features. The computerized learning process is improved
when pre-specified knowledge (from the pathologist)
about simple dependency relations to the histological
features is taken into account. We use the BN models for
both grading and causal analysis. In addition, the BN
models provide a causal explanation of dependency
between the histological features and the grading. This
can offer the biggest potential for choice of an efficient
treatment, since it concentrates on the malignancy grade
as the cause of pathological observations. The causal
analysis shows that all ten histological features are
important for the grading. The histological features are
causally ordered, implying that features of first order are
of higher priority, e.g. for the choice of treatment in
order not to allow the malignancy to progress to a higher
degree. Due to the explanations of feature relations, the complement to any malignancy classification tool and
allows reproducible comparison of malignancy grading.
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