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Cancer Genetics Risk Assessment and Counseling (PDQ®): Genetics - Health Professional Information [NCI] - Components of the Risk Assessment Process

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In spite of these limitations, disease-specific cumulative risk estimates are most often employed in clinical settings. These estimates usually provide risk for a given time interval and can be anchored to cumulative risks of other health conditions in a given population (e.g., the 5-year risk by the Gail model).[63,66] Cumulative risk models have limitations that may underestimate or overestimate risk. For example, the Gail model excludes paternal family histories of breast cancer.[60] Furthermore, many of these models were constructed from data derived from predominately Caucasian populations and may have limited validity when used to estimate risk in other ethnicities.[67]

Cumulative risk estimates are best used when evidence of other underlying significant risk factors have been ruled out. Careful evaluation of an individual's personal health and family history can identify other confounding risk factors that may outweigh a risk estimate derived from a cumulative risk model. For example, a woman with a prior biopsy showing lobular carcinoma in situ (LCIS) whose mother was diagnosed with breast cancer at age 65 years has a greater lifetime risk from her history of LCIS than her cumulative lifetime risk of breast cancer based on one first-degree relative.[68,69] In this circumstance, recommendations for cancer risk management would be based on the risk associated with her LCIS. Unfortunately, there is no reliable method for combining all of an individual's relevant risk factors for an accurate absolute cancer risk estimate, nor are individual risk factors additive.

In summary, careful ascertainment and review of personal health and cancer family history are essential adjuncts to the use of prior probability models and cancer risk assessment models to assure that critical elements influencing risk calculations are considered.[59] Influencing factors include the following:

  • Differential diagnosis that is consistent with the personal and cancer family history.
  • Consideration of factors that influence how informative the family history may be.
  • Population that is best suited for the use of the model.
  • Tumor-specific features that may be suspicious for an inherited predisposition or modify individual cancer risk predictions.
  • Model-specific limitations that can overestimate or underestimate calculations.[62]

A number of investigators are developing health care provider decision support tools such as the Genetic Risk Assessment on the Internet with Decision Support (GRAIDS),[70] but at this time, clinical judgment remains a key component of any prior probability or absolute cancer risk estimation.[59]

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Last Updated: February 25, 2014
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