Predictive factors for neoplasia and malignancy in a neck mass.

OBJECTIVE To determine clinical factors that are able to predict the likelihood of neoplasia and malignancy of cervical masses. DESIGN Retrospective review of case series. Data were collected for age, sex, a history of alcohol and tobacco use, mass location, number, bilaterality, size, and duration. Logistic regression was performed to determine which clinical variables were significant in a prediction model for neoplasia and malignancy in a cervical mass. SETTING An academic general otolaryngology practice. RESULTS Review of 160 open neck biopsies yielded 95 complete cases for regression analysis. Thirty cases of neoplasia (31.6%) and 12 cases of malignancy (12.6%) were noted. For the prediction of neoplasia, logistic regression analysis identified patient age, duration, and size of the mass to be statistically significant. The overall model for neoplasia had positive and negative predictive values of 63.6% and 78.1%, respectively, and an overall accuracy of 74.7%. For the prediction of malignancy, only age was found to be significant. The model for malignancy failed to show any classification utility beyond that of clinical judgment. CONCLUSIONS On the basis of clinical factors, a logistic regression model can distinguish patients who have a low chance for neoplasia in a neck mass, and thereby help avoid unnecessary biopsy. It is not as useful in selecting patients for early biopsy. The strict prediction of malignancy on the basis of clinical variables alone is difficult.