Abstract
PURPOSE: Preoperative diagnosis of uterine leiomyosarcoma (ULMS) can be difficult due to its ability to mimic benign leiomyomas (LM). The current study aimed to investigate the influence of preoperative clinical characteristics and hematologic parameters on preoperative diagnosis and to design a scoring system. PATIENTS AND METHODS: We conducted a retrospective analysis of 288 patients with uterine tumors treated at the First Affiliated Hospital of Wenzhou Medical University between January 2006 and April 2022, including 64 with ULMS and 224 with LM. Preoperative clinical and laboratory variables were compared between groups. Logistic regression analysis was employed to identify predictors of ULMS, with receiver operating characteristic (ROC) curves used to evaluate diagnostic performance. RESULTS: Multivariate analysis identified four independent risk factors for ULMS: older age (>48 years), larger tumor size (>9.7 cm), elevated systemic immune-inflammation index (SII > 500), and higher controlling nutritional status score (CONUT ≥ 3) (all P<0.001). A preoperative scoring system was developed by assigning one point for each risk factor, yielding a total possible score of 0-4 points. A score ≥ 2 points demonstrated significant utility in differentiating ULMS from LM (AUC = 0.823, sensitivity 64.1%, specificity 85.3%). CONCLUSION: This single-center retrospective study demonstrates that the integration of age, tumor size, SII, and CONUT score shows promising utility for preoperative differentiation between ULMS and LM. The constructed scoring system may provide valuable auxiliary support for identifying occult ULMS preoperatively. However, given the study's limitations, including its retrospective design and sample size, external validation through large-scale, multicenter prospective studies is necessary before clinical implementation.