A Predictive Model for Nodal Metastases in Patients With Appendiceal Cancers. Issue 1 (July 2021)

• Record Type: Journal Article
• Authors: Day, Ryan W.; Chang, Yu-Hui; Stucky, Chee-Chee; Gray, Richard; Pockaj, Barbara; Wasif, Nabil
• Is Part Of: Annals of surgery. Volume 274:Issue 1(2021)
• Publication Date: 2021-07
• Subjects: appendiceal cancer -- appendiceal -- carcinoid -- mucinous -- National Cancer Database -- nonmucinous -- predictive model -- right hemicolectomy -- signet call; Surgery -- Periodicals; 617.005
• DOI: 10.1097/SLA.0000000000003501
• Languages: English
• ISSNs: 0003-4932

Abstract

Abstract : 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. Abstract : Supplemental Digital Content is available in the text