A breast cancer prediction model incorporating familial and personal risk factors
By Jack Cuzick. Hereditary Cancer in Clinical Practice, 2004
1,156 citations recorded by the source corpus.
An Erratum has been published for this article in Statistics in Medicine 2005; 24(1):156.
Subfields: Cancer Research, Genetics, Oncology
View Paper: https://www.semanticscholar.org/paper/f2a5fa996c6a6fbf890512b0c38e2f64a6053e5e
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In Cancer Research
- Sanjiv Sam Gambhir
- Lisa M. Coussens
- Robert James Gillies
- Robert A. Gatenby
- Rakesh K. Jain
- Tarik F. Massoud
In Genetics
In Oncology
- Timothy R. Rebbeck
- Stephen A. Feig
- Lisa M. Coussens
- A. Mantovani
- Rakesh K. Jain
- J. Pollard
- Pengfei Lu
Citations in the corpus
- Early detection of cancer (2022, positive): “Artificial intelligence (AI) and machine learning (ML) approaches, such as support-vector machine and neural networkmodels, can discover cancer biomarkers, detect cancer-specific signatures in highdimensional datasets, and build prospective statistical classifiers for evaluating diagnostic performan”
- Breast cancer screening with imaging: recommendations from the Society of Breast Imaging and the ACR on the use of mammography, breast MRI, breast ultrasound, and other technologies for the detection of clinically occult breast cancer. (2010, methodology): “The Tyrer-Cuzick model takes into account family and reproductive history as well as Ashkenazi Jewish ancestry, reproductive history, and other personal factors such as rf height and weight [58].”
- Treatment of Symptoms of the Menopause: An Endocrine Society Clinical Practice Guideline. (2015, background): “The International Breast Intervention Study calculator predicts 10-year and lifetime risk (155, 156).”