Assessing Breast Cancer Risk Models in Marin County, a Population With High Rates of Delayed Childbirth

Powell, Mark; Jamshidian, Farid; Cheyne, Kate; Nititham, Joanne; Prebil, Lee Ann; Ereman, Rochelle

doi:10.1016/j.clbc.2013.11.003

Cited by 21 publications

(19 citation statements)

References 25 publications

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“…Regarding the risk of cancer, over the years the understanding of the factors involved in the development of cancer has grown, and subsequently an increase in published risk‐prediction models for various types of cancer, such as esophageal, lung, and breast cancer, has been observed . Also, for CRC, several risk‐prediction models have been developed.…”

Section: Aspirin In Primary Prevention: the Need For A Risk Scorementioning

confidence: 99%

Reducing Cardiovascular and Cancer Risk: How to Address Global Primary Prevention in Clinical Practice

Battistoni

Mastromarino

Volpe

2015

Clinical Cardiology

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Emerging evidence suggesting the possibility that interventions able to prevent cardiovascular disease (CVD) may also be effective in the prevention of cancer have recently stimulated great interest in the medical community. In particular, data from both experimental and observational studies have demonstrated that aspirin may play a role in preventing different types of cancer. Although the use of aspirin in the secondary prevention of CVD is well established, aspirin in primary prevention is not systematically recommended because the absolute cardiovascular event reduction is similar to the absolute excess in major bleedings. By adding to its cardiovascular prevention benefits, the potential beneficial effect of aspirin in reducing the incidence of mortality and cancer could tip the balance between risks and benefits of aspirin therapy in primary prevention in favor of the latter and broaden the indication for treatment with aspirin in populations at average risk. Prospective and randomized studies are currently investigating the effect of aspirin in prevention of both cancer and CVD; however, clinical efforts at the individual level to promote the use of aspirin in global (or total) primary prevention already could be made on the basis of a balanced evaluation of the benefit/risk ratio.

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Section: Aspirin In Primary Prevention: the Need For A Risk Scorementioning

confidence: 99%

Reducing Cardiovascular and Cancer Risk: How to Address Global Primary Prevention in Clinical Practice

Battistoni

Mastromarino

Volpe

2015

Clinical Cardiology

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“…The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) model was designed to predict the probability of a BRCA1/BRCA2 mutation and provide breast and ovarian cancer risk estimates, and it uniquely accounts for the possibility of genetic modifiers and the polygenic component of breast cancer risk [27][28][29]. The IBIS model was found to be well calibrated for women of Marin County, California, a population with high rates of breast cancer [30]. The IBIS model was found to be well calibrated for women of Marin County, California, a population with high rates of breast cancer [30].…”

Section: Key Pointsmentioning

confidence: 99%

Genetic risk assessment for breast and gynecological malignancies

Profato

Arun

2015

Current Opinion in Obstetrics &Amp; Gynecology

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Cancer genetic risk assessment is complex, and models developed to estimate risk may not apply to all populations. Identifying genetic factors related to cancer risk is also becoming increasingly complex with the clinical implementation of panel testing. This testing approach should be critically evaluated by healthcare providers. Further research is needed to create evidence-based guidelines for panel testing and management recommendations for moderately penetrant genes.

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“…Each one can be either 0 (zero) or 1 (one), where 0 (zero) means the absence and 1 (one) the presence of the disease on the first and second degree relatives, an early onset of cancer in any relative, bilateral breast cancer and ovarian cancer for the first column and breast biopsies, atypical hyperplasia and Lobular Carcinoma In Situ (LCIS) for the second [16,17].…”

Section: A Case Studymentioning

confidence: 99%

“…However, the development of an all-inclusive risk assessment models should include other characteristics of patient like genetic risk factors (about 5% to 10% of breast cancer cases can to be hereditary) or hormonal factors (women who have had more menstrual cycles have a slightly higher risk of breast cancer, due to a longer lifetime exposure to the hormones estrogen and progesterone) [16,17,18].…”

Section: Introductionmentioning

confidence: 99%

Logic Programming and Artificial Neural Networks in Breast Cancer Detection

Neves

Guimarães

Gomes

et al. 2015

Advances in Computational Intelligence

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Abstract. About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.

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Assessing Breast Cancer Risk Models in Marin County, a Population With High Rates of Delayed Childbirth

Cited by 21 publications

References 25 publications

Reducing Cardiovascular and Cancer Risk: How to Address Global Primary Prevention in Clinical Practice

Reducing Cardiovascular and Cancer Risk: How to Address Global Primary Prevention in Clinical Practice

Genetic risk assessment for breast and gynecological malignancies

Logic Programming and Artificial Neural Networks in Breast Cancer Detection

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