Letter from the Editors

Issue 26 Winter/Spring 2016

Cancer prevention and treatment require expertise in a number of topics: etiologic factors, early detection and screening, diagnosis, multidisciplinary treatment and supportive care. Sometimes overlooked, however, is how critically important is the ability to predict and prognosticate outcomes.

The probability with which someone will develop a cancer is critically important information. More specifically, when preventive measures are developed, they need to be targeted to those at high risk. Measures used for prevention have side effects and risks, just as do treatments and procedures used for treatment. And thus, as a rule, we want, as much as possible, to focus their use on those most in need where we can achieve the best benefit to risk ratio. For example, the recently approved screening test for lung cancer using chest CT scans is specifically approved only for those who have smoked a minimum amount in the past, not for everyone. Screening mammography is recommended for women over 45 years of age because the risk is high enough over that age and randomized trials have shown sufficient benefit at that point.

Expert biostatisticians do the intricate sophisticated modeling using large samples of data from prior studies to determine what the specific algorithms are that predict risk. One of the first and best known examples of such an algorithm has come to be known as the Gail Model, named after Dr. Mitchell Gail of the National Cancer Institute, who we highlight in this issue of Cancer Prevention. This model is now widely used in oncologist and breast surgeon offices to predict a woman’s chances of developing breast cancer based on her age, race/ethnicity, pregnancy history and other factors. With this knowledge, if she is at high risk, appropriate choices and decisions can be made with regard to the use of chemo-preventive agents, such as hormonal therapy.

What Dr. Gail initiated with breast cancer has since been emulated for other malignancies. There are predictive models for a variety of cancers. Equally importantly, there are models for prognostication to inform patients who have already been diagnosed with cancer of their risk of mortality and to help in decisions regarding therapy, such as Adjuvant Online. Dr. Gail is indeed worthy of his award from AACR.

The Editors:

Andrew J. Dannenberg, MD
Henry R. Erle, MD-Roberts Family Professor of Medicine
Weill Cornell Medical College
Co-Director, Cancer Prevention Program
NewYork-Presbyterian Cancer Centers

Alfred I. Neugut, MD, PhD
Myron M. Studner Professor of Cancer Research
Professor of Medicine an0Epidemiology
Associate Director for Population Sciences
Herbert Irving Comprehensive Cancer Center
Columbia University College of Physicians and Surgeons
and Mailman School of Public Health
Co-Director, Cancer Prevention Program
NewYork-Presbyterian Cancer Centers