Construction of a Bayesian network for mammographic diagnosis of breast cancer

Kahn, Charles Η.; Roberts, Larry E.; Shaffer, Katherine A.; Haddawy, Peter

doi:10.1016/s0010-4825(96)00039-x

Cited by 154 publications

(93 citation statements)

References 42 publications

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“…For the development and modeling of Bayesian networks there are dozens of free tools or demo versions; however, though there are also dozens of Bayesian networks applied to support the diagnosis of breast cancer presented by the scientific literature and included in our systematic review 10,23,24,25 , these are made available by contacting the researchers who developed them and are used in the research centers that developed them.…”

Section: Discussionmentioning

confidence: 99%

“…Four primary studies, which included breast lesions from 1,223 women, met the inclusion criteria and were analyzed (as shown in Table 1) 10,23,24,25 . The overall concordance between the eligibility and methodological quality of the studies was 84% (κ = 0.67), indicating good agreement 12 .…”

Section: Identification Of Studies and Eligibilitymentioning

confidence: 99%

“…The Bayesian network in Kahn et al 24 used information from the patients' histories, such as the physical exam and mammography results, and the conditional probabilities were obtained by reviewing the indexed medical literature, census data and statistical health reports, i.e., conditional probabilities for architectural distortion, previous biopsy at the same site were estimated as well; values for demographic variables were derived from published epidemiological data; and statistical studies published in peerreviewed radiology journals provided most of the data for knowledge base, such as values of conditional probabilities findings and mammographic findings for breast cancer. The software used for learning conditional probabilities was BNG 26 , and IDEAL was used for inference 27 .…”

Section: Identification Of Studies and Eligibilitymentioning

confidence: 99%

“…Three studies performed well and received a positive rating on at least 8 of the 11 items 10,24,25 . The inter-observer agreement in the analysis of methodological quality with QUADAS was 94% (κ = 0.86), indicating a good agreement 14 .…”

Section: Identification Of Studies and Eligibilitymentioning

confidence: 99%

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Metanálise do uso de redes bayesianas no diagnóstico de câncer de mama

et al. 2015

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Section: Discussionmentioning

confidence: 99%

Section: Identification Of Studies and Eligibilitymentioning

confidence: 99%

Section: Identification Of Studies and Eligibilitymentioning

confidence: 99%

Section: Identification Of Studies and Eligibilitymentioning

confidence: 99%

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Metanálise do uso de redes bayesianas no diagnóstico de câncer de mama

et al. 2015

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“…3). BBN is a popular statistical learning method that has been investigated and applied in a number of CAD schemes for detecting breast cancer [30][31][32]. One unique advantage of the BBN approach is that the topology of the BBN represents the joint probability distribution of a problem domain by exploiting the dependencies between variables and capturing the knowledge of a given problem in a natural and efficient way [33].…”

Section: A Bayesian Belief Networkmentioning

confidence: 99%

Computer-Aided Diagnosis of Breast DCE-MRI Images Using Bilateral Asymmetry of Contrast Enhancement Between Two Breasts

Yang

Zhang

et al. 2013

J Digit Imaging

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Dynamic contrast material-enhanced magnetic resonance imaging (DCE-MRI) of breasts is an important imaging modality in breast cancer diagnosis with higher sensitivity but relatively lower specificity. The objective of this study is to investigate a new approach to help improve diagnostic performance of DCE-MRI examinations based on the automated detection and analysis of bilateral asymmetry of characteristic kinetic features between the left and right breast. An image dataset involving 130 DCE-MRI examinations was assembled and used in which 80 were biopsyproved malignant and 50 were benign. A computer-aided diagnosis (CAD) scheme was developed to segment breast areas depicted on each MR image, register images acquired from the sequential MR image scan series, compute average contrast enhancement of all pixels in one breast, and a set of kinetic features related to the difference of contrast enhancement between the left and right breast, and then use a multifeature based Bayesian belief network to classify between malignant and benign cases. A leave-one-case-out validation method was applied to test CAD performance. The computed area under a receiver operating characteristic (ROC) curve is 0.78±0.04. The positive and negative predictive values are 0.77 and 0.64, respectively. The study indicates that bilateral asymmetry of kinetic features between the left and right breasts is a potentially useful image biomarker to enhance the detection of angiogenesis associated with malignancy. It also demonstrates the feasibility of applying a simple CAD approach to classify between malignant and benign DCE-MRI examinations based on this new image biomarker.

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Bayes' theorem in paleopathological diagnosis

Byers

Roberts

2003

American J Phys Anthropol

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The utility of Bayes' theorem in paleopathological diagnoses is explored. Since this theorem has been used heavily by modern clinical medicine, its usefulness in that field is described first. Next, the mechanics of the theorem are discussed, along with methods for deriving the prior probabilities needed for its application. Following this, the sources of these prior probabilities and their accompanying problems in paleopathology are considered. Finally, an application using prehistoric rib lesions is presented to demonstrate the utility of this method to paleopathology.

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Construction of a Bayesian network for mammographic diagnosis of breast cancer

Cited by 154 publications

References 42 publications

Metanálise do uso de redes bayesianas no diagnóstico de câncer de mama

Metanálise do uso de redes bayesianas no diagnóstico de câncer de mama

Computer-Aided Diagnosis of Breast DCE-MRI Images Using Bilateral Asymmetry of Contrast Enhancement Between Two Breasts

Bayes' theorem in paleopathological diagnosis

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