Predictive factors for neoplasia and malignancy in a neck mass

Objective: To determine clinical factors that are able to predict the likel ihood of neoplasia and malignancy of cervical masses. Design: Retrospective review of case series. Data were collected for age, s ex, a history of alcohol and tobacco use, mass location, number, bilaterali ty, size, and duration. Logistic regression was performed to determine whic h clinical variables were significant in a prediction model for neoplasia a nd malignancy in a cervical mass. Setting: An academic general otolaryngology practice. Results: Review of 160 open neck biopsies yielded 95 complete cases for reg ression analysis. Thirty cases of neoplasia (31.6%) and 12 cases of maligna ncy (12.6%) were noted. For the prediction of neoplasia, logistic regressio n analysis identified patient age, duration, and size of the mass to be sta tistically significant. The overall model for neoplasia had positive and ne gative predictive values of 63.6% and 78.1%, respectively, and an overall a ccuracy of 74.7%. For the prediction of malignancy, only age was found to b e significant. The model for malignancy failed to show any classification u tility 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 selecti ng patients for early biopsy. The strict prediction of malignancy on the ba sis of clinical variables alone is difficult.