Optimal risk classification with statistical evidence in endometrial cancer

Abstract Background It is often clinically useful to classify tumor markers into risk groups. This study was aimed to investigate whether beginning with a statistically sound method would find cut points more reasonable than conventional ones. Methods We used data of endometrial cancer including 442 patients. The optimal number of cutoffs was based on the Akaike criterion and statistical algorithms were adapted to find the best locations. Codes were provided as a package. Results Myometrium invasion was an independent risk factor for lymph nodal metastasis when stratified into three groups by 0.41 and 0.89. Tumor size was an independent risk factor for overall survival when stratified into two groups by 4.11 cm. Both had better prediction than conventional choices and clinical relevance. Conclusion A statistically sound algorithm should be used to stratify patients into risk groups.