Liquid Biopsy Cell-free RNA-based Machine Learning Enables Preoperative Risk-Stratification of Uterine Leiomyosarcoma.

BACKGROUND: Accurate preoperative distinction between uterine leiomyoma (UM) and uterine leiomyosarcoma (UMS) remains a major clinical challenge. Misclassification can lead to inadvertent dissemination of occult malignancy during minimally invasive procedures, while cautious management increases the use of more invasive surgery with greater morbidity. Current diagnostic approaches, including imaging and serum biomarkers, lack sufficient accuracy and standardized criteria. Circulating cell-free RNA (cfRNA) in plasma represents a promising alternative for noninvasive tumor classification, but its clinical utility for UMS has not been established. OBJECTIVE: To develop a plasma-based circulating cfRNA machine learning classifier for the preoperative differentiation of UMS from UM, and to evaluate its diagnostic performance and biological generalizability. STUDY DESIGN: This prospective, multicenter study enrolled women undergoing surgery for suspected myometrial tumors at sixteen hospitals in Spain. Peripheral blood was collected immediately before surgery, and postoperative histopathology served as the reference standard. The final cohort included 102 patients (78 UM; 24 UMS). Plasma cfRNA profiles were used to train and evaluate a model consisting of a 100-gene signature. Diagnostic performance was assessed using area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, and negative predictive value. Age effects and cross-tissue generalizability were also evaluated. RESULTS: A 100-gene circulating cfRNA signature reliably differentiates UM from UMS before surgery. This regularized logistic regression classifier achieved an AUROC of 0.868, with a sensitivity of 0.732, and a specificity of 0.813 in Monte Carlo cross-validation. Diagnostic performance remained consistent across age groups, with an AUROC of 0.879 for women below 55 years of age and 0.882 for women above 55 years. Notably, when evaluated in an independent external tissue cohort, the gene signature retained strong discriminatory performance, achieving an AUROC of 0.892, supporting its biological and translational robustness. CONCLUSION: (s): These preliminary findings suggest that plasma cfRNA signals differ between UMS and UM and may, after prospective validation in appropriately selected populations, contribute to preoperative risk stratification. They do not yet establish an accurate diagnostic test, and positive predictive value will depend on the prevalence of the population in which the assay is applied.