Introduction Preoperative differential diagnosis of clinical stage I uterine sarcoma (US) is essential for surgical intervention. Many studies have been done using CT or MRI imaging for machine learning prediction models but not with blood biomarkers. We aimed to develop a new model for diagnosis and prognosis prediction in the US using preoperative blood biomarkers and patient age.

Methods Overall, 143 US patients and 210 benign uterine myoma (UM) patients were randomly assigned to the ‘training and test’ cohort. 78(55%) cases were on clinical stage I. 30 preoperative peripheral blood parameters and patient’s age was surveyed. The Random Forest (RF) classifier was used to construct an algorithm. The accuracy, the area under the receiver operating characteristic curve (AUC), and the variable importance were calculated in the test cohort. The Ethics Committee approved this study.

Results The accuracy and AUC values for segregating stage I US from UM were 87% and 0.89, respectively. Variable important parameters for this classifier included age, CRP, and Hematocrit. Additionally, they were 85% and 0.95 in leiomyosarcoma, and 92% and 0.81 in ESS, respectively. Furthermore, unsupervised clustering analysis based on RF showed significant differences in two clusters in clinical stage I US with a median progression-free survival of 47 (3–115) vs. 13 (1–93) months (P < 0.001).

Conclusion/Implications The RF approach using common blood biomarkers and patient age can differentiate its malignancy and prognosis of US patients before primary intervention. This predictive model may provide a clinically useful approach to preoperative diagnosis distinct from conventional imaging techniques.