A Predictive Model for Nodal Metastases in Patients With Appendiceal Cancers

BACKGROUND: Histologic subtypes of appendiceal cancer vary in their propensity for metastases to regional lymph nodes (LN). A predictive model would help direct subsequent surgical therapy. METHODS: The National Cancer Database was queried for patients with appendiceal cancer undergoing surgery between 1998 and 2012. Multivariable logistic regression was used to develop a predictive model of LN metastases which was internally validated using Brier score and Area under the Curve (AUC). RESULTS: A total of 21,647 patients were identified, of whom 9079 (41.9%) had node negative disease, 4575 (21.1%) node positive disease, and 7993 (36.9%) unknown LN status. The strongest predictors of LN positivity were histology (carcinoid tumors OR 12.78, 95% CI 9.01-18.12), increasing T Stage (T3 OR 3.36, 95% CI 2.52-4.50, T4 OR 6.30, 95% CI 4.71-8.42), and tumor grade (G3 OR 5.55, 95% CI 4.78-6.45, G4 OR 5.98, 95% CI 4.30-8.31). The coefficients from the regression analysis were used to construct a calculator that generated predicted probabilities of LN metastases given certain inputs. Internal validation of the overall model showed an AUC of 0.75 (95% CI 0.74-0.76) and Brier score of 0.188. Histology-specific predictive models were also constructed with an AUC that varied from 0.669 for signet cell to 0.75 for goblet cell tumors. CONCLUSIONS: The risk for nodal metastases in patients with appendiceal cancers can be quantified with reasonable accuracy using a predictive model incorporating patient age, sex, tumor histology, T-stage, and grade. This can help inform clinical decision making regarding the need for a right hemicolectomy following appendectomy.