Validation of a Nomogram to Predict Recurrence in Patients with Mucinous Neoplasms of the Appendix with Peritoneal Dissemination After Cytoreductive Surgery and HIPEC.

ABSTRACT

BACKGROUND: Survival of patients affected by mucinous appendiceal neoplasms with peritoneal dissemination (PD) is mainly related to histopathological features. However, prognostic stratification is still a concern, as the clinical course of the disease is often unpredictable. The aim of this study is to construct and externally validate a nomogram predicting disease-free survival (DFS) in mucinous appendiceal neoplasms with PD treated by cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS/HIPEC). PATIENTS AND METHODS: Patients treated in two referral centers were included Hospital General Universitario Gregorio Marañón, Madrid, Spain (derivation cohort) and Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy (validation cohort). Cox regression analysis identified factors associated with shorter DFS in the derivation cohort. The nomogram performance was externally evaluated in the validation cohort using concordance index and calibration plots. Histology was classified according to the Peritoneal Surface Oncology Group International (PSOGI). RESULTS: The derivation cohort included 95 patients, and the validation cohort 348. Five-year DFS rates were 51.5 and 62%, respectively. Cox regression analysis (derivation cohort) identified PSOGI histology of the peritoneal components, number of preoperative elevated tumor marker, and peritoneal disease extent, as assessed by peritoneal carcinomatosis index, to be predictors of DFS. The model's predictive capacity was higher than that of PSOGI classification alone, with respective concordance indexes of 0.702 ± 0.023 and 0.610 ± 0.018 (validation cohort). The nomogram approximated the perfect model in the calibration plots at 3- and 5-year DFS. CONCLUSIONS: An easy-to-use model that provides better prognostic stratification than histopathological features has been constructed. This nomogram may help clinicians in individualized survival predictions and informed clinical decision-making.