Mortality risk prediction for primary appendiceal cancer

Background: Accurately predicting survival in patients with cancer is crucial for both clinical decisionmaking and patient counseling. The primary aim of this study was to generate the first machinelearning algorithm to predict the risk of mortality following the diagnosis of an appendiceal neoplasm. Methods: Patients with primary appendiceal cancer in the Surveillance, Epidemiology, and End Results database from 2000 to 2019 were included. Patient demographics, tumor characteristics, and survival data were extracted from the Surveillance, Epidemiology, and End Results database. Extreme gradient boost, random forest, neural network, and logistic regression machine learning models were employed to predict 1-, 5-, and 10-year mortality. After algorithm validation, the best-performance model was used to develop a patient-specific web-based risk prediction model. Results: A total of 16,579 patients were included in the study, with 13,262 in the training group (80%) and 3,317 in the validation group (20%). Extreme gradient boost exhibited the highest prediction accuracy for 1-, 5-, and 10-year mortality, with the 10-year model exhibiting the maximum area under the curve (0.909 [+/- 0.006]) after 10-fold cross-validation. Variables that significantly influenced the predictive ability of the model were disease grade, malignant carcinoid histology, incidence of positive regional lymph nodes, number of nodes harvested, and presence of distant disease. Conclusion: Here, we report the development and validation of a novel prognostic prediction model for patients with appendiceal neoplasms of numerous histologic subtypes that incorporate a vast array of patient, surgical, and pathologic variables. By using machine learning, we achieved an excellent predictive accuracy that was superior to that of previous nomograms. (c) 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).