Nomograms are superior to staging and risk grouping systems for identifying high-risk patients: preoperative application in prostate cancer.
OBJECTIVE
We outline a generic approach to using a nomogram to predict a continuous probability of failure in high-risk patients (rather than putting patients into groups), in order to identify patients whose risk exceeds a cutoff point. We discuss the goals of any staging system, what markers should be included, and models of markers.


RESULTS
Selection of high-risk patients for any cancer has traditionally been accomplished by the creation of risk groups, or perhaps clinical stages. Ideally, high-risk patients should be identified as accurately as possible, because of the treatment and psychological implications for the patient. We argue that a continuous multivariable prediction model, such as a nomogram, is the most appropriate and accurate way to select high-risk patients. This type of model predicts outcome more accurately than risk grouping or staging systems. As an example, we use our preoperative prostatic specific antigen recurrence nomogram to identify patients at high risk of biochemical failure, who are in need of an effective neoadjuvant therapy.


CONCLUSIONS
It will follow from our discussion that identification of high-risk patients should follow four simple steps. First, select the endpoint of interest for the trial or the patient. Second, select the method that predicts the endpoint as accurately as possible. Third, determine the cutoff of predicted probability beyond which it makes sense to give the patient experimental therapy. Fourth, offer the novel therapy to the patient whose prediction of the endpoint, using the most accurate prediction method, exceeds the threshold.
Kattan
Michael W
MW
Health Outcomes Research Group, Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA. kattanm@mskcc.org
D006801:Humans; D016015:Logistic Models; D008297:Male; D015233:Models, Statistical; D009367:Neoplasm Staging; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000145:classification* / Q000473:pathology / Q000601:surgery; D012307:Risk Factors