Probabilistic Computer Model Developed from Clinical Data in National Mammography Database Format to Classify Mammographic Findings

Abstract

Purpose:To determine whether a Bayesian network trained on a large database of patient demographic risk factors and radiologist-observed findings from consecutive clinical mammography examinations can exceed radiologist performance in the classification of mammographic findings as benign or malignant. Materials and Methods:The institutional review board exempted this HIPAA-compliant retrospective study from requiring informed consent. Structured reports from 48 744 consecutive pooled screening and diagnostic mammography examinations in 18 269 patients from April 5, 1999 to February 9, 2004 were collected. Mammographic findings were matched with a state cancer registry, which served as the reference standard. By using 10-fold cross validation, the Bayesian network was tested and trained to estimate breast cancer risk by using demographic risk factors (age, family and personal history of breast cancer, and use of hormone replacement therapy) and mammographic findings recorded in the Breast Imaging Reporting and Data System lexicon. The performance of radiologists compared with the Bayesian network was evaluated by using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Results:The Bayesian network significantly exceeded the performance of interpreting radiologists in terms of AUC (0.960 vs 0.939, P ϭ .002), sensitivity (90.0% vs 85.3%, P Ͻ .001), and specificity (93.0% vs 88.1%, P Ͻ .001). Conclusion:On the basis of prospectively collected variables, the evaluated Bayesian network can predict the probability of breast cancer and exceed interpreting radiologist performance. Bayesian networks may help radiologists improve mammographic interpretation.

A Bayesian Model to Describe Factors Influencing Trough Levels of Vancomycin in Hemodialysis Patients

“…There is a growing interest in Bayesian networks that have been used to develop diagnostic and prognostic tools aimed at supporting medical decisions, that is, pancreatic cancer prediction [16] , diagnosis of appendicitis [20] , classification of mammographic findings in terms of benign or malignant [15] , prediction of falls in ageing patients [21] and diagnosis of primary aldosteronism [22] . Bayesian networks have several advantages.…”

“…They can combine different sources of knowledge and provide a fast response once the model is compiled. Their ability to outperform logistic regression for diagnosis prediction has already been reported [16,20] . Our results confirm the interest of Bayesian networks in the medical fields.…”

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

“…The use of this system, together with pre-interpreted diagnostic information, could also provide an effective computer-based training system for breast cancer diagnostic 45 . This comparison does not imply that the Bayesian network could replace the specialist but may indicate that technology can calculate diagnostic across many variables, incorporate complex dependencies among variables, and aid, for example, the radiologists' interpretations 45 .…”

Developing a utility decision framework to evaluate predictive models in breast cancer risk estimation

“…On the other hand, there is a long history of risk estimation for breast cancer by using imaging findings. [10][11][12][13] Now, it is widely agreed that imaging findings, in concert with genetic variants will likely be necessary for accurate assessment of a patient's breast cancer risk. A promising new paradigm, "radiogenomics," delves into the analysis of the interaction of imaging findings and genetic variants for estimating cancer risk.…”