Abstract
BACKGROUND: Systemic anticancer therapy (SACT) near the end of life (EOL) reduces the quality of the patient's remaining life without clinical benefit. Studies investigating machine learning models for predicting cancer mortality to guide treatment decisions have primarily focused on specific types of cancer. This study aimed to evaluate the ability of a pan-cancer model to generalize across 10 cancer types when predicting short-term mortality. PATIENTS AND METHODS: This study included patients with advanced cancer who were referred to the Department of Oncology at Aalborg University Hospital and died between January 2008 and December 2021 (N = 8690). Clinical data were used to train, validate, and test a pan-cancer model and 10 single-cancer models based on the eXtreme Gradient Boosting (XGBoost) algorithm. The average precision (AP) of the pan-cancer and single-cancer models was assessed and compared. Furthermore, explainable AI with Shapley additive explanations (SHAP) was used to evaluate shared prognostic information across the cancer types. RESULTS: The mean AP increased from 0.51 using the single-cancer models to 0.56 using the pan-cancer model to predict short-term mortality (random baseline 0.12). Important features identified by SHAP were shared across cancer types, indicating shared predictors of 30-day mortality. The most important features for predicting 30-day mortality were plasma albumin level, white blood cell count, and lactate dehydrogenase levels. CONCLUSION: A pan-cancer model enhanced the performance of short-term mortality estimates compared with models based on single cancer types. Thus, including multiple cancer types in predictive modeling in oncology could be advantageous when shared predictors are expected across cancer types.