Risk prediction models of breast cancer: a systematic review of model performances. - Wikidata - wikimedia - TriplyDB

Risk prediction models of breast cancer: a systematic review of model performances.

Risk prediction models of breast cancer: a systematic review of model performances.

http://www.wikidata.org/entity/Q37955078

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A multi-centre randomised trial comparing ultrasound vs mammography for screening breast cancer in high-risk Chinese women A focus group study on breast cancer risk presentation: one format does not fit all Breast cancer risk assessment across the risk continuum: genetic and nongenetic risk factors contributing to differential model performance.Breast cancer risk assessment using genetic variants and risk factors in a Singapore Chinese population.Risk factors of breast cancer: a systematic review and meta-analysis.RiGoR: reporting guidelines to address common sources of bias in risk model development.Germline variation in cancer-susceptibility genes in a healthy, ancestrally diverse cohort: implications for individual genome sequencing.Mammographic Breast Density and Common Genetic Variants in Breast Cancer Risk Prediction Personalisation of breast cancer follow-up: a time-dependent prognostic nomogram for the estimation of annual risk of locoregional recurrence in early breast cancer patients.Practical problems with clinical guidelines for breast cancer prevention based on remaining lifetime risk.Relationship of Predicted Risk of Developing Invasive Breast Cancer, as Assessed with Three Models, and Breast Cancer Mortality among Breast Cancer Patients Breast Cancer Prevention: Can Women's Expectations Be Met?Personalized medicine and access to health care: potential for inequitable access?Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach.Methodological issues in current practice may lead to bias in the development of biomarker combinations for predicting acute kidney injury.The Proliferative Activity of Mammary Epithelial Cells in Normal Tissue Predicts Breast Cancer Risk in Premenopausal Women.A clinical risk stratification tool for predicting treatment resistance in major depressive disorder Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports.Prospective validation of the breast cancer risk prediction model BOADICEA and a batch-mode version BOADICEACentre.Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder.Bayesian joint ordinal and survival modeling for breast cancer risk assessment.Epigenetic Biomarkers of Breast Cancer Risk: Across the Breast Cancer Prevention Continuum.Development and validation of risk models and molecular diagnostics to permit personalized management of cancer.The role of dynamic contrast-enhanced screening breast MRI in populations at increased risk for breast cancer.Multigene panels in prostate cancer risk assessment: a systematic review.Breast cancer risk models: a comprehensive overview of existing models, validation, and clinical applications.Using New and Emerging Technologies to Identify and Respond to Suicidality Among Help-Seeking Young People: A Cross-Sectional Study.Variation in Mammographic Breast Density Assessments Among Radiologists in Clinical Practice: A Multicenter Observational Study.Cross-sectional study to assess the association of population density with predicted breast cancer risk.An epidemiological model for prediction of endometrial cancer risk in Europe.A comparison between different prediction models for invasive breast cancer occurrence in the French E3N cohort.Breast cancer risk prediction accuracy in Jewish Israeli high-risk women using the BOADICEA and IBIS risk models.Assessment of performance of the Gail model for predicting breast cancer risk: a systematic review and meta-analysis with trial sequential analysis.Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables.

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P2860

Risk prediction models of breast cancer: a systematic review of model performances.

http://www.wikidata.org/entity/Q37955078

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2011 nî lūn-bûn

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2011年の論文

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2011年学术文章

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2011年学术文章

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2011年学术文章

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2011年学术文章

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2011年学术文章

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2011年學術文章

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2011年學術文章

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2011年學術文章

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name

Risk prediction models of breast cancer: a systematic review of model performances.

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Risk prediction models of breast cancer: a systematic review of model performances.

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type

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Risk prediction models of breast cancer: a systematic review of model performances.

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Risk prediction models of breast cancer: a systematic review of model performances.

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Risk prediction models of breast cancer: a systematic review of model performances.

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Risk prediction models of breast cancer: a systematic review of model performances.

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Breast Cancer Research and Treatment

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Risk prediction models of breast cancer: a systematic review of model performances.

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P2093

Ammarin Thakkinstian

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Chollathip Wiratkapun

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Thunyarat Anothaisintawee

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Vijj Kasamesup

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Yot Teerawattananon

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P2860

Global cancer statistics, 2002

Age at first birth, parity and risk of breast cancer: a meta-analysis of 8 studies from the Nordic countries

Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond

Cumulative risk of breast cancer to age 70 years according to risk factor status: data from the Nurses' Health Study.

Risk prediction models with incomplete data with application to prediction of estrogen receptor-positive breast cancer: prospective data from the Nurses' Health Study.

Validation of the Gail et al. model of breast cancer risk prediction and implications for chemoprevention.

Traditional statistical methods for evaluating prediction models are uninformative as to clinical value: towards a decision analytic framework.

Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure.

What do we mean by validating a prognostic model?

Assessing the value of risk predictions by using risk stratification tables.

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