A Novel Clinical Prognostic Model for Breast Cancer Patients with Malignant Pleural Effusion: Avoiding Chemotherapy in Low-risk Groups?

Abstract

Abstract Malignant pleural effusion (MPE) is a severe complication in patients with advanced cancer that is associated with a poor prognosis, and breast cancer is the second leading cause of MPE after lung cancer. Herein, our study aimed to construct a machine learning-based model for predicting the prognosis of patients with MPE combined with breast cancer. We analyzed 196 patients with both MPE and breast cancer (143 in the training group and 53 in the external validation group). Least absolute shrinkage and selection operator and univariate Cox regression analyses were applied to identify eight key clinical variables, and a nomogram model was established. To facilitate the use of the model, an online web server was also created. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses. Based on the ROC curves for 3-, 6-, and 12-month survival, the areas under the curves were 0.824, 0.824, and 0.818 in the training set and 0.777, 0.790, and 0.715 in the validation set, respectively. In the follow-up analysis, both systemic and intrapleural chemotherapy significantly increased survival in the high-risk group compared to the low-risk group. Collectively, we have developed a first-ever survival prediction model for breast cancer patients with newly diagnosed MPE and validated the model using an independent cohort. The model can be used to accurately predict prognosis and guide individualized treatment.