| scholarly article | Q13442814 |
| P50 | author | David A Hinds | Q37391803 |
| P2093 | author name string | Brian K Rhees | |
| Ross L Prentice | |||
| Mary Pettinger | |||
| Matthew E Mealiffe | |||
| Renee P Stokowski | |||
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| P433 | issue | 21 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | breast cancer | Q128581 |
| P304 | page(s) | 1618-1627 | |
| P577 | publication date | 2010-10-18 | |
| P1433 | published in | Journal of the National Cancer Institute | Q400279 |
| P1476 | title | Assessment of clinical validity of a breast cancer risk model combining genetic and clinical information | |
| P478 | volume | 102 |
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| Q36250581 | A steroid metabolizing gene variant in a polyfactorial model improves risk prediction in a high incidence breast cancer population |
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| Q34472668 | Additive interactions between susceptibility single-nucleotide polymorphisms identified in genome-wide association studies and breast cancer risk factors in the Breast and Prostate Cancer Cohort Consortium |
| Q34517195 | Adipocytokines, hepatic and inflammatory biomarkers and incidence of type 2 diabetes. the CoLaus study |
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| Q35195254 | Associations between breast density and a panel of single nucleotide polymorphisms linked to breast cancer risk: a cohort study with digital mammography |
| Q36619329 | Breast Cancer Risk Prediction Using Clinical Models and 77 Independent Risk-Associated SNPs for Women Aged Under 50 Years: Australian Breast Cancer Family Registry |
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| Q33892384 | Breast cancer risk assessment using genetic variants and risk factors in a Singapore Chinese population |
| Q36393193 | Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement |
| Q36481548 | Breast cancer risk prediction and mammography biopsy decisions: a model-based study |
| Q64074174 | Breast cancer risk prediction in women aged 35-50 years: impact of including sex hormone concentrations in the Gail model |
| Q40605693 | Breast cancer risk prediction using a clinical risk model and polygenic risk score |
| Q37968316 | Breast cancer worry in further examination of mammography screening--a systematic review |
| Q35834376 | Can the breast screening appointment be used to provide risk assessment and prevention advice? |
| Q53292592 | Cancer risk assessment using genetic panel testing: considerations for clinical application. |
| Q89746441 | Clinical applications of polygenic breast cancer risk: a critical review and perspectives of an emerging field |
| Q36489222 | Clinically relevant measures of fit? A note of caution |
| Q92528444 | Comparative Validation of Breast Cancer Risk Prediction Models and Projections for Future Risk Stratification |
| Q33863654 | Development of a melanoma risk prediction model incorporating MC1R genotype and indoor tanning exposure: impact of mole phenotype on model performance |
| Q38236180 | Development of a risk assessment tool for projecting individualized probabilities of developing breast cancer for Chinese women |
| Q41651668 | Development of risk scoring system for stratifying population for hepatocellular carcinoma screening. |
| Q50460101 | Economic evaluation of using a genetic test to direct breast cancer chemoprevention in white women with a previous breast biopsy. |
| Q36877641 | Estimating improvement in prediction with matched case-control designs |
| Q35916647 | Evaluating Prognostic Markers Using Relative Utility Curves and Test Tradeoffs |
| Q35924410 | Evaluating subject-level incremental values of new markers for risk classification rule |
| Q51627211 | Familial breast cancer. |
| Q64251583 | Genetic Epidemiology of Breast Cancer in Latin America |
| Q64080586 | Genetic Testing to Guide Risk-Stratified Screens for Breast Cancer |
| Q22064616 | Genome-wide association studies and the clinic: a focus on breast cancer |
| Q28943514 | Genome-wide association study of survival in non-small cell lung cancer patients receiving platinum-based chemotherapy |
| Q48717652 | Impact of a Panel of 88 Single Nucleotide Polymorphisms on the Risk of Breast Cancer in High-Risk Women: Results From Two Randomized Tamoxifen Prevention Trials. |
| Q26770167 | Improved prediction of complex diseases by common genetic markers: state of the art and further perspectives |
| Q34225990 | Inclusion of endogenous hormone levels in risk prediction models of postmenopausal breast cancer |
| Q36017113 | Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases |
| Q38778524 | Incorporating Biomarkers in Studies of Chemoprevention |
| Q37614763 | Incorporating genomics into breast and prostate cancer screening: assessing the implications |
| Q37724736 | Incremental impact of breast cancer SNP panel on risk classification in a screening population of white and African American women |
| Q92145386 | Informing a Risk Prediction Model for Binary Outcomes with External Coefficient Information |
| Q64935691 | Is risk-stratified breast cancer screening economically efficient in Germany? |
| Q28389384 | JNCI and cancer prevention |
| Q54938332 | MC1R variants as melanoma risk factors independent of at-risk phenotypic characteristics: a pooled analysis from the M-SKIP project. |
| Q35144259 | Model for individualized prediction of breast cancer risk after a benign breast biopsy |
| Q46091841 | Net reclassification improvement: a link between statistics and clinical practice. |
| Q37696697 | Net risk reclassification p values: valid or misleading? |
| Q36540821 | Oxidative stress and its relationship with adenosine deaminase activity in various stages of breast cancer |
| Q36090164 | Performance of reclassification statistics in comparing risk prediction models |
| Q46621012 | Personalized Medicine Through SNP Testing for Breast Cancer Risk: Clinical Implementation |
| Q92277944 | Predicting breast cancer risk using personal health data and machine learning models |
| Q36583004 | Prediction of breast cancer risk based on profiling with common genetic variants |
| Q37223335 | Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status |
| Q57266562 | Prevention of breast cancer in the context of a national breast screening programme |
| Q39076835 | Projecting Individualized Absolute Invasive Breast Cancer Risk in US Hispanic Women |
| Q35112414 | Quantification of population benefit in evaluation of biomarkers: practical implications for disease detection and prevention |
| Q90744442 | Risk Models for Breast Cancer and Their Validation |
| Q39326953 | Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci |
| Q26991921 | Risk determination and prevention of breast cancer |
| Q30540812 | Risk prediction for complex diseases: application to Parkinson disease. |
| Q35883531 | Risk prediction measures for case-cohort and nested case-control designs: an application to cardiovascular disease |
| Q54950358 | Risk, Prediction and Prevention of Hereditary Breast Cancer - Large-Scale Genomic Studies in Times of Big and Smart Data. |
| Q36322904 | SNPs and breast cancer risk prediction for African American and Hispanic women |
| Q50100527 | Simple Decision-Analytic Functions of the AUC for Ruling Out a Risk Prediction Model and an Added Predictor. |
| Q98612742 | Simple Methods for Evaluating 4 Types of Biomarkers: Surrogate Endpoint, Prognostic, Predictive, and Cancer Screening |
| Q37579917 | Smoking quit success genotype score predicts quit success and distinct patterns of developmental involvement with common addictive substances |
| Q37237634 | The associations between a polygenic score, reproductive and menstrual risk factors and breast cancer risk |
| Q36137764 | The contributions of breast density and common genetic variation to breast cancer risk |
| Q53818766 | The future of genomic testing in primary care: the changing face of personalized medicine. |
| Q27013910 | The genetics of cancer risk |
| Q35807814 | The impact of direct-to-consumer personal genomic testing on perceived risk of breast, prostate, colorectal, and lung cancer: findings from the PGen study |
| Q33871568 | The role of the fat mass and obesity associated gene (FTO) in breast cancer risk |
| Q35030287 | The use of the Gail model, body mass index and SNPs to predict breast cancer among women with abnormal (BI-RADS 4) mammograms |
| Q48152313 | This special issue contains several papers on clinical trials, exemplifying Ross Prentice's influence. Preface |
| Q37119173 | Translating biomarkers to clinical practice |
| Q33763777 | Using SNP genotypes to improve the discrimination of a simple breast cancer risk prediction model |
| Q55074868 | Utility of Genetic Testing in Addition to Mammography for Determining Risk of Breast Cancer Depends on Patient Age. |
| Q57040851 | Validation of a genetic risk score for Arkansas women of color |
| Q37370221 | Variation in predictive ability of common genetic variants by established strata: the example of breast cancer and age. |
| Q37638197 | Variation of adenoma prevalence by age, sex, race, and colon location in a large population: implications for screening and quality programs |
| Q92755500 | Why the Gold Standard Approach by Mammography Demands Extension by Multiomics? Application of Liquid Biopsy miRNA Profiles to Breast Cancer Disease Management |
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