Risk assessment of uterine masses: a pre-operative biochemical algorithm for risk stratification of sarcoma and guide clinical management.

OBJECTIVE

This study aimed to develop and internally validate a pre-operative biochemical model for stratifying sarcoma risk and supporting clinical decision-making in women with uterine masses.

METHODS

This retrospective diagnostic accuracy study included 2453 women who were surgically treated for uterine masses between 2004 and 2016 at two tertiary gynecologic centers. Histopathological examination confirmed 2404 benign lesions and 49 sarcomas. Pre-operative serum levels of lactate dehydrogenase isoenzymes and total lactate dehydrogenase were used to construct a three-tier risk stratification model. Diagnostic performance was assessed by calculating sensitivity, specificity, and negative predictive value and by performing receiver operating characteristic analysis.

RESULTS

The model correctly classified all 49 malignant cases as high-risk, yielding a sensitivity and a negative predictive value of 100%. Among the 2404 benign cases, 2216 (92.2%) were classified as no risk, 184 (7.6%) as low risk, and 4 (0.2%) as high risk. These classifications yielded a specificity of 92.2% and an overall accuracy of 92.3%. Furthermore, the model reclassified a subset of patients initially considered high-risk into lower-risk categories, potentially reducing the need for unnecessary radical surgeries.

CONCLUSIONS

This biochemical model provides a simple, clinically applicable approach to stratifying uterine masses pre-operatively. By reliably identifying patients with a negligible risk of sarcoma, the model supports more conservative, fertility-preserving management while maintaining oncologic safety. However, because the observed diagnostic performance may reflect cohort-specific characteristics, prospective multicenter validation is required before routine clinical use.