Yong Zhao

Association of aspirin and ibuprofen use with endometrial cancer risk in the PLCO dataset

Abstract Given the increasing incidence of endometrial cancer (EC) and the lack of improvement in survival rates, it is imperative to explore possible prevention methods. Studies on aspirin's effect on EC risk have been controversial; research on ibuprofen remains limited. We therefore aimed to investigate the relationship between aspirin and ibuprofen use and risk of EC through a cohort study. This analysis was based on the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, which recruited participants aged 55–74 years from 1993 to 2001, with follow‐up for cancer incidence continuing until December 31, 2009. A total of 42,394 women were enrolled in this analysis, and 678 cases of EC were diagnosed during a median follow‐up period of 12.0 years. Compared with an intake of <4 pills per month, the use of ibuprofen ≥30 pills per month significantly reduced EC risk (fully‐adjusted hazard ratio [HR], 0.75; 95% confidence interval [CI]: 0.58–0.98), particularly in participants with a history of cardiovascular disease (fully‐adjusted HR, 0.57; 95% CI: 0.37–0.87). No evidence was found for an association between aspirin and EC occurrence in the general population (fully‐adjusted HR, 0.98; 95% CI: 0.81–1.19), nor in specific subgroups. In conclusion, frequent ibuprofen use, unlike aspirin, was linked to reduced EC risk. This protective effect of ibuprofen was enhanced in women with a history of cardiovascular disease. Additional well‐designed, prospective research is needed to validate these findings and explore the underlying mechanisms.

Development and validation of a prediction model for lymph node metastasis based on molecular typing in clinically early-stage endometrial carcinoma

To develop and externally validate a machine learning-based preoperative model integrating molecular typing and clinical features to predict lymph node metastasis (LNM) in patients with early-stage endometrial carcinoma (EC). This retrospective study included 465 patients with clinically early-stage EC treated at Qilu Hospital of Shandong University. Tumors were classified into molecular subtypes using The Cancer Genome Atlas-based methods. Least Absolute Shrinkage and Selection Operator regression identified five preoperative predictors: molecular typing (CN-H vs. non-CN-H), histological subtype, depth of myometrial invasion, neutrophil-to-lymphocyte ratio, and CA125 levels. Multiple machine learning algorithms were evaluated, and logistic regression (LR) was selected based on optimal discrimination and clinical applicability. Model performance was assessed using area under the curve (AUC), calibration plots, and decision curve analysis (DCA). A web-based nomogram was developed for clinical use. The LR model demonstrated excellent discrimination, with AUCs of 0.843 in the training cohort and 0.809 in the testing cohort. The CN-H subtype was significantly associated with increased LNM risk. The model enabled effective risk stratification and calibration curves and DCA confirmed the model's accuracy and clinical utility. By integrating molecular and preoperative clinical features, this model offers accurate LNM risk stratification for early-stage EC. It supports clinical decision-making and has been implemented as a user-friendly online tool. Further prospective multicenter validation is warranted.