A tree augmented naive Bayesian network experiment for breast cancer
  prediction

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Abstract

In order to investigate the breast cancer prediction problem on the aging population with the grades of DCIS, we conduct a tree augmented naive Bayesian network experiment trained and tested on a large clinical dataset including consecutive diagnostic mammography examinations, consequent biopsy outcomes and related cancer registry records in the population of women across all ages. The aggregated results of our ten-fold cross validation method recommend a biopsy threshold higher than 2% for the aging population.

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Effects of mammography screening under different screening schedules: model estimates of potential benefits and harms.

Jeanne S Mandelblatt, Kathleen A Cronin, Stephanie Bailey … (2009)

Despite trials of mammography and widespread use, optimal screening policy is controversial. To evaluate U.S. breast cancer screening strategies. 6 models using common data elements. National data on age-specific incidence, competing mortality, mammography characteristics, and treatment effects. A contemporary population cohort. Lifetime. Societal. 20 screening strategies with varying initiation and cessation ages applied annually or biennially. Number of mammograms, reduction in deaths from breast cancer or life-years gained (vs. no screening), false-positive results, unnecessary biopsies, and overdiagnosis. The 6 models produced consistent rankings of screening strategies. Screening biennially maintained an average of 81% (range across strategies and models, 67% to 99%) of the benefit of annual screening with almost half the number of false-positive results. Screening biennially from ages 50 to 69 years achieved a median 16.5% (range, 15% to 23%) reduction in breast cancer deaths versus no screening. Initiating biennial screening at age 40 years (vs. 50 years) reduced mortality by an additional 3% (range, 1% to 6%), consumed more resources, and yielded more false-positive results. Biennial screening after age 69 years yielded some additional mortality reduction in all models, but overdiagnosis increased most substantially at older ages. Varying test sensitivity or treatment patterns did not change conclusions. Results do not include morbidity from false-positive results, patient knowledge of earlier diagnosis, or unnecessary treatment. Biennial screening achieves most of the benefit of annual screening with less harm. Decisions about the best strategy depend on program and individual objectives and the weight placed on benefits, harms, and resource considerations. National Cancer Institute.

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Area under the Precision-Recall Curve: Point Estimates and Confidence Intervals

Kendrick Boyd, Kevin H. Eng, C David Page (2013)

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Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience.

Adelaine Leung, Daniel L Rubin, P E Fine … (2006)

To retrospectively determine whether a Bayesian network (BN) computer model can accurately predict the probability of breast cancer on the basis of risk factors and mammographic appearance of microcalcifications, to improve the positive predictive value (PPV) of biopsy, with pathologic examination and follow-up as reference standards. The institutional review board approved this HIPAA-compliant study; informed consent was not required. Results of 111 consecutive image-guided breast biopsies performed for microcalcifications deemed suspicious by radiologists were analyzed. Mammograms obtained before biopsy were analyzed in a blinded manner by a breast imager who recorded Breast Imaging Reporting and Data System (BI-RADS) descriptors and provided a probability of malignancy. The BN uses probabilistic relationships between breast disease and mammography findings to estimate the risk of malignancy. Probability estimates from the radiologist and the BN were used to create receiver operating characteristic (ROC) curves, and area under the ROC curve (A(z)) values were compared. PPV of biopsy was also evaluated on the basis of these probability estimates. The BN and the radiologist achieved A(z) values of 0.919 and 0.916, respectively, which were not significantly different. If the 34 patients estimated by the BN to have less than a 10% probability of malignancy had not undergone biopsy, the PPV of biopsy would have increased from 21.6% to 31.2% without missing a breast cancer (P < .001). At this level, the radiologist's probability estimation improved the PPV to 30.0% (P < .001). A probabilistic model that includes BI-RADS descriptors for microcalcifications can distinguish between benign and malignant abnormalities at mammography as well as a breast imaging specialist can and may be able to improve the PPV of image-guided breast biopsy. (c) RSNA, 2006.