Lung cancer risk prediction: a tool for early detection.
Although 45% of men and 39% of women will be diagnosed with cancer in their lifetime, it is difficult to predict which individuals will be affected. For some cancers, substantial progress in individual risk estimation has already been made. However, relatively few models have been developed to predict lung cancer risk beyond effects of age and smoking. This paper reviews published models for lung cancer risk prediction, discusses their potential contribution to clinical and research settings and suggests improvements to the risk modeling strategy for lung cancer. The sensitivity and specificity of existing cancer risk models is less than optimal. Improvement in individual risk prediction is important for selection of individuals for prevention or early detection interventions. In addition to smoking, factors related to occupational exposure, personal medical history and family history of cancer can add to the predictive power. A good risk prediction model is one that can identify a small fraction of the population in which a large proportion of the disease cases will occur. In the future, genetic and other biological markers are likely to be useful, although they will require rigorous evaluation. Validation is essential to establish the predictive effect and for ongoing monitoring of the model's continued relevance.
Cassidy
Adrian
A
Roy Castle Lung Cancer Research Programme, University of Liverpool Cancer Research Centre, Liverpool, United Kingdom.
Duffy
Stephen W
SW
Myles
Jonathan P
JP
Liloglou
Triantafillos
T
Field
John K
JK
D042241:Early Diagnosis; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis / Q000453:epidemiology*; D018570:Risk Assessment; D012307:Risk Factors